code/SciRepEval_SciBERT_classifier.ipynb

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    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## ENVIRONMENT SETUP"
      ],
      "metadata": {
        "id": "VOaziWdAkmrs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CjRmnXC0gpqb",
        "outputId": "32bc4ef1-50ff-4a3f-b978-eb10266007c0"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun Apr  9 13:22:56 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   58C    P8    10W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/allenai/scirepeval.git\n",
        "%cd scirepeval\n",
        "!pip install --upgrade pip\n",
        "!pip install -q -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Uw3Vo1BjE9L",
        "outputId": "4e772fa8-a78b-44e9-c258-6e79a4704d59"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'scirepeval'...\n",
            "remote: Enumerating objects: 1500, done.\u001b[K\n",
            "remote: Counting objects: 100% (434/434), done.\u001b[K\n",
            "remote: Compressing objects: 100% (230/230), done.\u001b[K\n",
            "remote: Total 1500 (delta 239), reused 263 (delta 204), pack-reused 1066\u001b[K\n",
            "Receiving objects: 100% (1500/1500), 14.17 MiB | 27.80 MiB/s, done.\n",
            "Resolving deltas: 100% (1001/1001), done.\n",
            "/content/scirepeval\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pip in /usr/local/lib/python3.9/dist-packages (22.0.4)\n",
            "Collecting pip\n",
            "  Downloading pip-23.0.1-py3-none-any.whl (2.1 MB)\n",
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            "\u001b[?25hInstalling collected packages: pip\n",
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            "    Found existing installation: pip 22.0.4\n",
            "    Uninstalling pip-22.0.4:\n",
            "      Successfully uninstalled pip-22.0.4\n",
            "Successfully installed pip-23.0.1\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.2/61.2 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m131.4/131.4 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m307.0/307.0 kB\u001b[0m \u001b[31m21.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m48.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.3/93.3 kB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.2/114.2 kB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.3/132.3 kB\u001b[0m \u001b[31m16.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.1/17.1 MB\u001b[0m \u001b[31m74.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m151.5/151.5 kB\u001b[0m \u001b[31m18.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.2/12.2 MB\u001b[0m \u001b[31m107.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m75.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m98.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m65.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m98.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m705.6/705.6 kB\u001b[0m \u001b[31m64.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m500.8/500.8 kB\u001b[0m \u001b[31m48.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m765.9/765.9 kB\u001b[0m \u001b[31m46.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.8/62.8 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.6/79.6 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.6/116.6 kB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m880.6/880.6 kB\u001b[0m \u001b[31m57.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m30.8/30.8 MB\u001b[0m \u001b[31m40.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.4/89.4 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.9/43.9 MB\u001b[0m \u001b[31m16.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m73.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m91.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.9/5.9 MB\u001b[0m \u001b[31m115.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.9/4.9 MB\u001b[0m \u001b[31m113.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m125.4/125.4 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.6/6.6 MB\u001b[0m \u001b[31m115.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m776.4/776.4 MB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.7/3.7 MB\u001b[0m \u001b[31m98.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m419.6/419.6 kB\u001b[0m \u001b[31m35.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.1/19.1 MB\u001b[0m \u001b[31m81.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.4/78.4 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.7/4.7 MB\u001b[0m \u001b[31m111.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.4/140.4 kB\u001b[0m \u001b[31m17.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m232.7/232.7 kB\u001b[0m \u001b[31m27.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.2/211.2 kB\u001b[0m \u001b[31m24.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m264.6/264.6 kB\u001b[0m \u001b[31m19.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.3/55.3 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m53.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.0/154.0 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.0/143.0 kB\u001b[0m \u001b[31m14.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.5/139.5 kB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.8/138.8 kB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.8/140.8 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for fairseq (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pytrec-eval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for sklearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for openai (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "ipython 7.34.0 requires jedi>=0.16, which is not installed.\n",
            "yfinance 0.2.14 requires pytz>=2022.5, but you have pytz 2022.4 which is incompatible.\n",
            "torchtext 0.15.1 requires torch==2.0.0, but you have torch 1.12.1 which is incompatible.\n",
            "torchdata 0.6.0 requires torch==2.0.0, but you have torch 1.12.1 which is incompatible.\n",
            "tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.19.4 which is incompatible.\n",
            "tensorflow 2.12.0 requires tensorboard<2.13,>=2.12, but you have tensorboard 2.10.0 which is incompatible.\n",
            "tensorflow-hub 0.13.0 requires protobuf>=3.19.6, but you have protobuf 3.19.4 which is incompatible.\n",
            "imbalanced-learn 0.10.1 requires joblib>=1.1.1, but you have joblib 1.1.0 which is incompatible.\n",
            "grpcio-status 1.48.2 requires grpcio>=1.48.2, but you have grpcio 1.47.0 which is incompatible.\n",
            "googleapis-common-protos 1.59.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.4 which is incompatible.\n",
            "google-cloud-translate 3.8.4 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.4 which is incompatible.\n",
            "google-cloud-language 2.6.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.4 which is incompatible.\n",
            "google-cloud-firestore 2.7.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.4 which is incompatible.\n",
            "google-cloud-datastore 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.4 which is incompatible.\n",
            "google-cloud-bigquery 3.4.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.4 which is incompatible.\n",
            "google-cloud-bigquery-storage 2.19.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.4 which is incompatible.\n",
            "google-api-core 2.11.0 requires google-auth<3.0dev,>=2.14.1, but you have google-auth 2.11.0 which is incompatible.\n",
            "google-api-core 2.11.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.4 which is incompatible.\n",
            "cvxpy 1.3.1 requires setuptools>65.5.1, but you have setuptools 63.4.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Needed to fix some version conflict bug with protobuf\n",
        "!pip install --upgrade protobuf\n",
        "!cp /usr/local/lib/python3.9/dist-packages/google/protobuf/internal/builder.py /content/scirepeval\n",
        "!pip install protobuf==3.19.5\n",
        "!mv /content/scirepeval/builder.py /usr/local/lib/python3.9/dist-packages/google/protobuf/internal"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BaSekkOGdT4i",
        "outputId": "59d6e957-da83-47f8-a182-b92c6bc8d525"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.9/dist-packages (3.19.4)\n",
            "Collecting protobuf\n",
            "  Downloading protobuf-4.22.1-cp37-abi3-manylinux2014_x86_64.whl (302 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.4/302.4 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: protobuf\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.19.4\n",
            "    Uninstalling protobuf-3.19.4:\n",
            "      Successfully uninstalled protobuf-3.19.4\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.12.0 requires tensorboard<2.13,>=2.12, but you have tensorboard 2.10.0 which is incompatible.\n",
            "tensorflow-metadata 1.13.0 requires protobuf<4,>=3.13, but you have protobuf 4.22.1 which is incompatible.\n",
            "tensorboardx 2.5.1 requires protobuf<=3.20.1,>=3.8.0, but you have protobuf 4.22.1 which is incompatible.\n",
            "tensorboard 2.10.0 requires protobuf<3.20,>=3.9.2, but you have protobuf 4.22.1 which is incompatible.\n",
            "grpcio-status 1.48.2 requires grpcio>=1.48.2, but you have grpcio 1.47.0 which is incompatible.\n",
            "google-api-core 2.11.0 requires google-auth<3.0dev,>=2.14.1, but you have google-auth 2.11.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed protobuf-4.22.1\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting protobuf==3.19.5\n",
            "  Downloading protobuf-3.19.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m14.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: protobuf\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 4.22.1\n",
            "    Uninstalling protobuf-4.22.1:\n",
            "      Successfully uninstalled protobuf-4.22.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.19.5 which is incompatible.\n",
            "tensorflow 2.12.0 requires tensorboard<2.13,>=2.12, but you have tensorboard 2.10.0 which is incompatible.\n",
            "tensorflow-hub 0.13.0 requires protobuf>=3.19.6, but you have protobuf 3.19.5 which is incompatible.\n",
            "grpcio-status 1.48.2 requires grpcio>=1.48.2, but you have grpcio 1.47.0 which is incompatible.\n",
            "google-api-core 2.11.0 requires google-auth<3.0dev,>=2.14.1, but you have google-auth 2.11.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed protobuf-3.19.5\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TRAINING\n",
        "\n",
        "- **SKIP THIS PART** if you only want to try out inferencing !!"
      ],
      "metadata": {
        "id": "1mKA4MiaYQFp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset Creation and Preprocessing\n",
        "\n"
      ],
      "metadata": {
        "id": "lc5nkuxEnLu9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import datasets\n",
        "\n",
        "fos = datasets.load_dataset('allenai/scirepeval', 'fos')"
      ],
      "metadata": {
        "id": "lhK06BY7YFaY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377,
          "referenced_widgets": [
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        "outputId": "041259a2-33b3-45e2-ed82-4ac3129cca6d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/8.74k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            "Downloading and preparing dataset scirepeval/fos (download: 651.77 MiB, generated: 607.45 MiB, post-processed: Unknown size, total: 1.23 GiB) to /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83...\n",
            "{'train': 'https://ai2-s2-research-public.s3.us-west-2.amazonaws.com/scirepeval/train/fos/train.jsonl', 'val': 'https://ai2-s2-research-public.s3.us-west-2.amazonaws.com/scirepeval/train/fos/val.jsonl', 'test': 'https://ai2-s2-research-public.s3.us-west-2.amazonaws.com/scirepeval/test/fos/meta.jsonl'}\n"
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          "text": [
            "Dataset scirepeval downloaded and prepared to /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83. Subsequent calls will reuse this data.\n"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J9uX7Z4oKa-K",
        "outputId": "cf32e1df-76db-46a5-ae33-5f66d164eff4"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    evaluation: Dataset({\n",
              "        features: ['doc_id', 'corpus_id', 'title', 'abstract', 'labels', 'labels_text'],\n",
              "        num_rows: 68147\n",
              "    })\n",
              "    train: Dataset({\n",
              "        features: ['doc_id', 'corpus_id', 'title', 'abstract', 'labels', 'labels_text'],\n",
              "        num_rows: 541218\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['doc_id', 'corpus_id', 'title', 'abstract', 'labels', 'labels_text'],\n",
              "        num_rows: 67631\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding no. of unique labels\n",
        "import itertools\n",
        "\n",
        "labels = list(itertools.chain.from_iterable(fos['train']['labels']))\n",
        "label_texts = list(itertools.chain.from_iterable(fos['train']['labels_text']))\n",
        "\n",
        "labels_dict = dict()\n",
        "temp = set()\n",
        "\n",
        "for label_text, label in zip(label_texts, labels):\n",
        "    temp.add((label_text, label))\n",
        "\n",
        "labels_dict = {label_text:label for label_text, label in list(temp)}\n",
        "num_labels = len(labels_dict)\n",
        "\n",
        "print(labels_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8zuzA9hmFxc",
        "outputId": "727476c4-576b-4f1b-94af-823eabfb18ed"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Linguistics': 14, 'History': 12, 'Chemistry': 4, 'Philosophy': 18, 'Physics': 19, 'Geography': 10, 'Business': 3, 'Sociology': 22, 'Economics': 6, 'Geology': 11, 'Education': 7, 'Law': 13, 'Computer science': 5, 'Environmental science': 9, 'Agricultural and Food sciences': 0, 'Materials science': 15, 'Political science': 20, 'Medicine': 17, 'Biology': 2, 'Psychology': 21, 'Art': 1, 'Mathematics': 16, 'Engineering': 8}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import concatenate_datasets\n",
        "\n",
        "def pick_balanced_dataset(dataset, num_papers_to_pick, num_labels):\n",
        "  '''\n",
        "    Function to pick a sample fraction of a dataset, with papers from each category \n",
        "    selected in the same proportion as the original.\n",
        "  '''\n",
        "  num_total_papers = len(dataset)\n",
        "  bal_dict = dict()  \n",
        "\n",
        "  for label in range(num_labels):\n",
        "    fos_subj = dataset.filter(lambda example: label in example['labels'])\n",
        "    subj_count = len(fos_subj)\n",
        "    small_fos_subj = fos_subj.shuffle(seed=42).select(range(round(subj_count/num_total_papers*num_papers_to_pick)))\n",
        "    temp_dict = dict()\n",
        "\n",
        "    temp_dict['data'] = small_fos_subj\n",
        "    temp_dict['count'] = len(small_fos_subj)\n",
        "    bal_dict[label] = temp_dict  \n",
        " \n",
        "  print(\"Total papers picked : {}\\n\".format(num_papers_to_pick))\n",
        "\n",
        "  bal_dataset = list()\n",
        "  for label_text in labels_dict.keys():\n",
        "    label = labels_dict[label_text]\n",
        "    num_papers = bal_dict[label]['count']\n",
        "    print(\"{} : {} ({:.2f}%)\".format(label_text, num_papers, num_papers/num_papers_to_pick*100))\n",
        "    bal_dataset.append(bal_dict[label]['data'])\n",
        "\n",
        "  # Finally, concatenate datasets for each subject and then shuffle the contents\n",
        "  bal_dataset = concatenate_datasets(bal_dataset).shuffle(seed=40)  \n",
        "  return bal_dataset"
      ],
      "metadata": {
        "id": "8y9PH7ChD-57"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fos_train = fos['train']\n",
        "fos_eval = fos['evaluation']\n",
        "num_train_papers_to_pick = 20000\n",
        "num_eval_papers_to_pick = 5000\n",
        "\n",
        "# Pick sample, balanced train and val datasets\n",
        "fos_small_train = pick_balanced_dataset(fos_train, num_train_papers_to_pick, num_labels)\n",
        "fos_small_eval = pick_balanced_dataset(fos_eval, num_eval_papers_to_pick, num_labels)"
      ],
      "metadata": {
        "id": "6jSTBWHqRkoK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67e5e978-47b1-4e91-fdb9-070649826ba0"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-6fd6ac05c535f4ed.arrow\n",
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            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-1e88fd433830fea5.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-135aaafde0e361b3.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-b5a8b4f92836eb3a.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-6dd117ec112aca5f.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-dfb9c1522127d5c2.arrow\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total papers picked : 20000\n",
            "\n",
            "Linguistics : 68 (0.34%)\n",
            "History : 1590 (7.95%)\n",
            "Chemistry : 1529 (7.65%)\n",
            "Philosophy : 266 (1.33%)\n",
            "Physics : 2628 (13.14%)\n",
            "Geography : 367 (1.84%)\n",
            "Business : 309 (1.54%)\n",
            "Sociology : 238 (1.19%)\n",
            "Economics : 842 (4.21%)\n",
            "Geology : 439 (2.20%)\n",
            "Education : 1112 (5.56%)\n",
            "Law : 209 (1.04%)\n",
            "Computer science : 1329 (6.64%)\n",
            "Environmental science : 1979 (9.89%)\n",
            "Agricultural and Food sciences : 732 (3.66%)\n",
            "Materials science : 878 (4.39%)\n",
            "Political science : 928 (4.64%)\n",
            "Medicine : 2569 (12.85%)\n",
            "Biology : 2031 (10.15%)\n",
            "Psychology : 1093 (5.46%)\n",
            "Art : 120 (0.60%)\n",
            "Mathematics : 1300 (6.50%)\n",
            "Engineering : 1332 (6.66%)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total papers picked : 5000\n",
            "\n",
            "Linguistics : 18 (0.36%)\n",
            "History : 397 (7.94%)\n",
            "Chemistry : 382 (7.64%)\n",
            "Philosophy : 67 (1.34%)\n",
            "Physics : 654 (13.08%)\n",
            "Geography : 90 (1.80%)\n",
            "Business : 78 (1.56%)\n",
            "Sociology : 62 (1.24%)\n",
            "Economics : 210 (4.20%)\n",
            "Geology : 111 (2.22%)\n",
            "Education : 278 (5.56%)\n",
            "Law : 53 (1.06%)\n",
            "Computer science : 332 (6.64%)\n",
            "Environmental science : 493 (9.86%)\n",
            "Agricultural and Food sciences : 184 (3.68%)\n",
            "Materials science : 220 (4.40%)\n",
            "Political science : 232 (4.64%)\n",
            "Medicine : 641 (12.82%)\n",
            "Biology : 508 (10.16%)\n",
            "Psychology : 273 (5.46%)\n",
            "Art : 32 (0.64%)\n",
            "Mathematics : 324 (6.48%)\n",
            "Engineering : 332 (6.64%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fos_small_train[3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PkECf8uGxwKN",
        "outputId": "3b03dca6-951d-4aa1-ae9d-d15672e953a7"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'doc_id': '94682184',\n",
              " 'corpus_id': 94682184,\n",
              " 'title': 'PREPARATION OF CHITOSAN STABILIZED OFLOXACIN- GOLD NANO CONJUGATE FOR THE IMPROVED ANTI BACTERIAL ACTIVITY AGAINST HUMAN PATHOGENIC BACTERIA',\n",
              " 'abstract': 'Objective .In the present study, biocom[patible polymer chitosan stabilized ofloxacin- gold nanoconjugate was synthesized for the improved anti bacterial activity against clinical isolate of E.coli and Staphylococcus aureus Methods. Chitosan stabilized oflaxacin-gold nanoparticles conjugate was prepared with aqueous solution of ofloxacin ,gold nanoparticles suspension and chitosan under optimum condition. Synthesized nanoconjugate was .characterized by scanning electron microscopy, FTIR and the synthesized nanoconjugate was tested against E.coli and Staph.aureus adopting well diffusion assay. Result. Synthesis of chitosan stabilized ofloxacin- gold nanoconjugate was primarly confirmed by colour change of the reaction mixture, characteristic change in the FTIR pattern and size and shape by SEM. Anti bacterial activity revealed increased spectrum of antibacterial activity was recorded in chitosan stabilized ofloxacin - gold nanoconjugate against both the tested strains. The highest increase in inhibitory zone for E.coli and Staph.aureus was observed. Conclusion. The present study suggests possible utilization of antibiotics –gold nanoconjugate as an effective anti microbial agent against the pathogenic bacteria',\n",
              " 'labels': [17],\n",
              " 'labels_text': ['Medicine']}"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "from datasets import Dataset\n",
        "\n",
        "max_length = 400\n",
        "\n",
        "def preprocess(dataset, tokenizer):\n",
        "    '''\n",
        "      Function to preprocess dataset in the correct format.\n",
        "    '''\n",
        "    \n",
        "    dataset_ = dict()\n",
        "    labels, texts = list(), list()\n",
        "\n",
        "    for paper_dict in dataset:\n",
        "        text = paper_dict['title'] + ' ' + paper_dict['abstract']\n",
        "        for label in paper_dict['labels']:      # For each label of a paper, add a separate record to the dataset\n",
        "            labels.append(label)\n",
        "            texts.append(text)\n",
        "\n",
        "    dataset_['label'] = labels\n",
        "    dataset_['text'] = texts\n",
        "    preprocessed_dataset = Dataset.from_dict(dataset_)\n",
        "\n",
        "    def tokenize_function(examples):\n",
        "        return tokenizer(examples[\"text\"], padding=True, truncation=True, max_length=max_length)\n",
        "\n",
        "    tokenized_dataset = preprocessed_dataset.map(tokenize_function, batched=True) \n",
        "    return tokenized_dataset"
      ],
      "metadata": {
        "id": "yxYvxFBQ5-fd"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"allenai/scibert_scivocab_uncased\")\n",
        "\n",
        "train = preprocess(fos_small_train, tokenizer)\n",
        "eval = preprocess(fos_small_eval, tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "916ed81068874447b3c435fd32646f67",
            "69edc0e2486f41a79f967dc616267dc4",
            "94598ee9cd314569998ba293fc1647d8",
            "177ed1b993f04a7ab19db792ded1455d",
            "29d5c67bddd647c289ae04ed367941d7",
            "34e9c9c49baf4712afa8d037d0a02ee3",
            "f7330bd074844480be08411f35588391",
            "3bc82cc8a0da4d60b8e514836e8dec87",
            "4dcd2ee6e47343ffab66434fd2919111",
            "e071929be7e5486a9ab46b5bc2a7086b",
            "164cb76fe9c445bfaf5f6feda032baeb",
            "2f80c8dfa81848e3933f7aa318ac89a6",
            "79c5bc9e5c784fc799733c4d3ed6a9ba",
            "b5331fb1092441b3bfdddfa20a5020ec",
            "0952ca87dec84937b6fa29ff23034925",
            "62a09dacf0ca40d29762294876a58b90",
            "1a528449f7fd4f73bfc9aada38e3ef81",
            "3a2a01b3997147f9a5d4d124ea11a343",
            "d161b4b75ebc40b298570f58090e79bf",
            "bf5625b19c814a748b692fa505b66156",
            "16a8b565f2c94afcb858b8034a5dc13d",
            "672dc0ce532d48d08f0d8d863eb32ede"
          ]
        },
        "id": "gTW6objl4oZw",
        "outputId": "bfd69d9c-2f9b-4b8a-f097-a6370a63cbc4"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
            "loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.17.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 31090\n",
            "}\n",
            "\n",
            "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/vocab.txt from cache at /root/.cache/huggingface/transformers/33593020f507d72099bd84ea6cd2296feb424fecd62d4a8edcc2a02899af6e29.38339d84e6e392addd730fd85fae32652c4cc7c5423633d6fa73e5f7937bbc38\n",
            "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/tokenizer.json from cache at None\n",
            "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/added_tokens.json from cache at None\n",
            "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/special_tokens_map.json from cache at None\n",
            "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/tokenizer_config.json from cache at None\n",
            "loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.17.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 31090\n",
            "}\n",
            "\n",
            "loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.17.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 31090\n",
            "}\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "916ed81068874447b3c435fd32646f67"
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          },
          "metadata": {}
        },
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          "output_type": "display_data",
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              "version_major": 2,
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              "model_id": "2f80c8dfa81848e3933f7aa318ac89a6"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importing the Model"
      ],
      "metadata": {
        "id": "oiN-zhbjnZbu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"allenai/scibert_scivocab_uncased\", num_labels=num_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wkN6F2X78USb",
        "outputId": "3a042cca-968d-4c73-b2a4-0b05ea18787b"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\",\n",
            "    \"3\": \"LABEL_3\",\n",
            "    \"4\": \"LABEL_4\",\n",
            "    \"5\": \"LABEL_5\",\n",
            "    \"6\": \"LABEL_6\",\n",
            "    \"7\": \"LABEL_7\",\n",
            "    \"8\": \"LABEL_8\",\n",
            "    \"9\": \"LABEL_9\",\n",
            "    \"10\": \"LABEL_10\",\n",
            "    \"11\": \"LABEL_11\",\n",
            "    \"12\": \"LABEL_12\",\n",
            "    \"13\": \"LABEL_13\",\n",
            "    \"14\": \"LABEL_14\",\n",
            "    \"15\": \"LABEL_15\",\n",
            "    \"16\": \"LABEL_16\",\n",
            "    \"17\": \"LABEL_17\",\n",
            "    \"18\": \"LABEL_18\",\n",
            "    \"19\": \"LABEL_19\",\n",
            "    \"20\": \"LABEL_20\",\n",
            "    \"21\": \"LABEL_21\",\n",
            "    \"22\": \"LABEL_22\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_10\": 10,\n",
            "    \"LABEL_11\": 11,\n",
            "    \"LABEL_12\": 12,\n",
            "    \"LABEL_13\": 13,\n",
            "    \"LABEL_14\": 14,\n",
            "    \"LABEL_15\": 15,\n",
            "    \"LABEL_16\": 16,\n",
            "    \"LABEL_17\": 17,\n",
            "    \"LABEL_18\": 18,\n",
            "    \"LABEL_19\": 19,\n",
            "    \"LABEL_2\": 2,\n",
            "    \"LABEL_20\": 20,\n",
            "    \"LABEL_21\": 21,\n",
            "    \"LABEL_22\": 22,\n",
            "    \"LABEL_3\": 3,\n",
            "    \"LABEL_4\": 4,\n",
            "    \"LABEL_5\": 5,\n",
            "    \"LABEL_6\": 6,\n",
            "    \"LABEL_7\": 7,\n",
            "    \"LABEL_8\": 8,\n",
            "    \"LABEL_9\": 9\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.17.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 31090\n",
            "}\n",
            "\n",
            "loading weights file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/de14937a851e8180a2bc5660c0041d385f8a0c62b1b2ccafa46df31043a2390c.74830bb01a0ffcdeaed8be9916312726d0c4cd364ac6fc15b375f789eaff4cbb\n",
            "Some weights of the model checkpoint at allenai/scibert_scivocab_uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.decoder.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at allenai/scibert_scivocab_uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # JUST A CODE CELL FOR INFINITE RUNNING :)\n",
        "# !python -m pip install huggingface_hub\n",
        "# !huggingface-cli login"
      ],
      "metadata": {
        "id": "zqFypd_CqV1p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training the model"
      ],
      "metadata": {
        "id": "2jbFqgdhnoPT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "training_args = TrainingArguments(output_dir=\"test_trainer\", evaluation_strategy=\"epoch\")"
      ],
      "metadata": {
        "id": "YSOGQVw-8moU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "354f1f98-b612-4af7-d8ae-fce754a101a9"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def compute_metrics(pred_eval):\n",
        "    '''\n",
        "      Function to compute evaluation metrics like\n",
        "      cs_precision and cs_recall.\n",
        "    '''\n",
        "    logits, labels = pred_eval\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "\n",
        "    cs_label = labels_dict['Computer science']\n",
        "\n",
        "    num_total_papers = len(labels)\n",
        "    num_cs_papers_actual = sum(np.array(labels == cs_label))\n",
        "    num_cs_papers_pred = sum(np.array(predictions == cs_label))\n",
        "    num_cs_papers_correct = sum(np.array((predictions == labels) & (labels == cs_label)))\n",
        "\n",
        "    print(\"Total papers : {}\".format(num_total_papers))\n",
        "    print(\"Total CS papers : {}\".format(num_cs_papers_actual))\n",
        "    print(\"Total CS papers predicted : {}\".format(num_cs_papers_pred))\n",
        "    print(\"Total papers correctly predicted : {}\".format(num_cs_papers_correct))\n",
        "\n",
        "    cs_precision = num_cs_papers_correct / num_cs_papers_pred\n",
        "    cs_recall = num_cs_papers_correct / num_cs_papers_actual\n",
        "\n",
        "    return {\"cs_precision\": cs_precision, \"cs_recall\": cs_recall}"
      ],
      "metadata": {
        "id": "lKoOHZEJIGi1"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **FOR DEBUGGING ONLY !!**"
      ],
      "metadata": {
        "id": "jmFo3O3McZqa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Tiny sample for DEBUGGING ONLY\n",
        "fos_tiny_train = pick_balanced_dataset(fos_train, 500, num_labels)\n",
        "fos_tiny_eval = pick_balanced_dataset(fos_eval, 500, num_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9VuCLe4wiDuu",
        "outputId": "73fda2a1-c694-42f8-e249-baab6715cb3d"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-32a68ffb3c884e98.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-8314ef5bbf29120c.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-7484f4c7a63cc9e7.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-8d1158cdaf1185da.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-39d91b23e938b0a7.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-caf42c23c823e599.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-cf8a68569a6f18ab.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-bd9c8c70435ec873.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-3684a76212880a55.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-51dccf8e5a82b85d.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-ccef3a49cf9de1bb.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-dcbf67fc5c1579bb.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-8656736669529859.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-85e00b37ae8780f8.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-372aa1cf755cc97b.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-e9aa91614aa21adf.arrow\n",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total papers picked : 500\n",
            "\n",
            "Linguistics : 2 (0.40%)\n",
            "History : 40 (8.00%)\n",
            "Chemistry : 38 (7.60%)\n",
            "Philosophy : 7 (1.40%)\n",
            "Physics : 66 (13.20%)\n",
            "Geography : 9 (1.80%)\n",
            "Business : 8 (1.60%)\n",
            "Sociology : 6 (1.20%)\n",
            "Economics : 21 (4.20%)\n",
            "Geology : 11 (2.20%)\n",
            "Education : 28 (5.60%)\n",
            "Law : 5 (1.00%)\n",
            "Computer science : 33 (6.60%)\n",
            "Environmental science : 49 (9.80%)\n",
            "Agricultural and Food sciences : 18 (3.60%)\n",
            "Materials science : 22 (4.40%)\n",
            "Political science : 23 (4.60%)\n",
            "Medicine : 64 (12.80%)\n",
            "Biology : 51 (10.20%)\n",
            "Psychology : 27 (5.40%)\n",
            "Art : 3 (0.60%)\n",
            "Mathematics : 32 (6.40%)\n",
            "Engineering : 33 (6.60%)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-a586a666f8a2d7c8.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-8e9a9898d1a0be69.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-26bce02762a7fb90.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-3a5aad3af58568cf.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-877b87636fa93189.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-2cbd3c30c638b742.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/fos/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-a809be8c1758fd6d.arrow\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total papers picked : 500\n",
            "\n",
            "Linguistics : 2 (0.40%)\n",
            "History : 40 (8.00%)\n",
            "Chemistry : 38 (7.60%)\n",
            "Philosophy : 7 (1.40%)\n",
            "Physics : 65 (13.00%)\n",
            "Geography : 9 (1.80%)\n",
            "Business : 8 (1.60%)\n",
            "Sociology : 6 (1.20%)\n",
            "Economics : 21 (4.20%)\n",
            "Geology : 11 (2.20%)\n",
            "Education : 28 (5.60%)\n",
            "Law : 5 (1.00%)\n",
            "Computer science : 33 (6.60%)\n",
            "Environmental science : 49 (9.80%)\n",
            "Agricultural and Food sciences : 18 (3.60%)\n",
            "Materials science : 22 (4.40%)\n",
            "Political science : 23 (4.60%)\n",
            "Medicine : 64 (12.80%)\n",
            "Biology : 51 (10.20%)\n",
            "Psychology : 27 (5.40%)\n",
            "Art : 3 (0.60%)\n",
            "Mathematics : 32 (6.40%)\n",
            "Engineering : 33 (6.60%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fos_tiny_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xAWMLiXdYPAV",
        "outputId": "8f7a9d49-712c-4006-bc65-29642cee44cf"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['doc_id', 'corpus_id', 'title', 'abstract', 'labels', 'labels_text'],\n",
              "    num_rows: 596\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"allenai/scibert_scivocab_uncased\")\n",
        "\n",
        "tiny_train = preprocess(fos_tiny_train, tokenizer)\n",
        "tiny_eval = preprocess(fos_tiny_eval, tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "c62928bcc7f34052a993d9a16ffeb1c2",
            "aa6f75986f714fd8be0e73ee29460cab",
            "e0945f7a72b04591b4971fa7b462ee68",
            "37668c27b2d04a46a7f29669f04baa3c",
            "2e721055d7134104bcb042a26dc5971a",
            "df5c674941954325a7c1777668406a04",
            "44a374688b7740968bf45a8e531da115",
            "eb1df94b04294cdcaf9223f59c937a73",
            "69bdfe7756334981aaf2d4288de28f73",
            "108ed9d40eaf4de481253d8242841deb",
            "26b9de2eaa224e6aa41ab26fe4d943ef",
            "1144f85cb82a4d3191ddd5fcc49aa8bb",
            "085198f121eb475b915eba579233ff99",
            "338826ef881f4c878ca06502627372b4",
            "f80fa421e95046ad92842c73bbd9eb8b",
            "77e2134144c8478aba9a5372dfb4e31f",
            "5e549a72ea5747878af429ce03167a85",
            "9ee865d85e42474881234911199ed899",
            "a6fe53a5e61941c2abb61cb70353a9a4",
            "5950750ddba44f819f3936589d0a3819",
            "02d5499fb6854b5aafe723d1789b212e",
            "8b15864ab4504735bd6937fdcf6d3a0c"
          ]
        },
        "id": "EP-q6-1kU4v2",
        "outputId": "69699f58-e89e-4172-ede4-1a285794a7d9"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
            "loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.17.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 31090\n",
            "}\n",
            "\n",
            "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/vocab.txt from cache at /root/.cache/huggingface/transformers/33593020f507d72099bd84ea6cd2296feb424fecd62d4a8edcc2a02899af6e29.38339d84e6e392addd730fd85fae32652c4cc7c5423633d6fa73e5f7937bbc38\n",
            "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/tokenizer.json from cache at None\n",
            "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/added_tokens.json from cache at None\n",
            "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/special_tokens_map.json from cache at None\n",
            "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/tokenizer_config.json from cache at None\n",
            "loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.17.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 31090\n",
            "}\n",
            "\n",
            "loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.17.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 31090\n",
            "}\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c62928bcc7f34052a993d9a16ffeb1c2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1144f85cb82a4d3191ddd5fcc49aa8bb"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tiny_trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tiny_train,\n",
        "    eval_dataset=tiny_eval,\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n",
        "\n",
        "tiny_trainer.train()"
      ],
      "metadata": {
        "id": "Q07KIpwMiU1t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 918
        },
        "outputId": "2930ba1f-cea7-4f64-f6a0-8fac69e067a8"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the training set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "***** Running training *****\n",
            "  Num examples = 810\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 306\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='306' max='306' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [306/306 04:41, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Cs Precision</th>\n",
              "      <th>Cs Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.519404</td>\n",
              "      <td>0.620000</td>\n",
              "      <td>0.688889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.406410</td>\n",
              "      <td>0.561404</td>\n",
              "      <td>0.711111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.322418</td>\n",
              "      <td>0.615385</td>\n",
              "      <td>0.711111</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 828\n",
            "  Batch size = 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total papers : 828\n",
            "Total CS papers : 45\n",
            "Total CS papers predicted : 50\n",
            "Total papers correctly predicted : 31\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 828\n",
            "  Batch size = 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total papers : 828\n",
            "Total CS papers : 45\n",
            "Total CS papers predicted : 57\n",
            "Total papers correctly predicted : 32\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 828\n",
            "  Batch size = 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total papers : 828\n",
            "Total CS papers : 45\n",
            "Total CS papers predicted : 52\n",
            "Total papers correctly predicted : 32\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=306, training_loss=0.8982162974239175, metrics={'train_runtime': 282.2882, 'train_samples_per_second': 8.608, 'train_steps_per_second': 1.084, 'total_flos': 499594075128000.0, 'train_loss': 0.8982162974239175, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tiny_evaluation = tiny_trainer.evaluate()\n",
        "print(tiny_evaluation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "ldHYcjmkZEmR",
        "outputId": "5f6e8fe6-26ee-49d5-a32a-8bb84812c87e"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 828\n",
            "  Batch size = 8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='208' max='104' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [104/104 00:57]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total papers : 828\n",
            "Total CS papers : 45\n",
            "Total CS papers predicted : 52\n",
            "Total papers correctly predicted : 32\n",
            "{'eval_loss': 1.322418212890625, 'eval_cs_precision': 0.6153846153846154, 'eval_cs_recall': 0.7111111111111111, 'eval_runtime': 24.9544, 'eval_samples_per_second': 33.18, 'eval_steps_per_second': 4.168, 'epoch': 3.0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training on actual dataset"
      ],
      "metadata": {
        "id": "kOTfAbaxcQ3c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Whole dataset will take 52 hours for training on GPU, 20k random samples around 2 hr\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train,\n",
        "    eval_dataset=eval,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ],
      "metadata": {
        "id": "XfoM95_VIeZS"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "id": "n9puMjwMIeix",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ce794b4a-275f-4eeb-a2d1-852aa99cc980"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the training set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "***** Running training *****\n",
            "  Num examples = 32152\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 12057\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='12057' max='12057' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [12057/12057 2:30:54, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Cs Precision</th>\n",
              "      <th>Cs Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.965800</td>\n",
              "      <td>0.948198</td>\n",
              "      <td>0.745614</td>\n",
              "      <td>0.395349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.732400</td>\n",
              "      <td>0.900670</td>\n",
              "      <td>0.713311</td>\n",
              "      <td>0.486047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.522600</td>\n",
              "      <td>0.881514</td>\n",
              "      <td>0.630480</td>\n",
              "      <td>0.702326</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to test_trainer/checkpoint-500\n",
            "Configuration saved in test_trainer/checkpoint-500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-1000\n",
            "Configuration saved in test_trainer/checkpoint-1000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-1000/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-1500\n",
            "Configuration saved in test_trainer/checkpoint-1500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-1500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-2000\n",
            "Configuration saved in test_trainer/checkpoint-2000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-2000/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-2500\n",
            "Configuration saved in test_trainer/checkpoint-2500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-2500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-3000\n",
            "Configuration saved in test_trainer/checkpoint-3000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-3000/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-3500\n",
            "Configuration saved in test_trainer/checkpoint-3500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-3500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-4000\n",
            "Configuration saved in test_trainer/checkpoint-4000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-4000/pytorch_model.bin\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 8077\n",
            "  Batch size = 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total papers : 8077\n",
            "Total CS papers : 430\n",
            "Total CS papers predicted : 228\n",
            "Total papers correctly predicted : 170\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to test_trainer/checkpoint-4500\n",
            "Configuration saved in test_trainer/checkpoint-4500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-4500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-5000\n",
            "Configuration saved in test_trainer/checkpoint-5000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-5000/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-5500\n",
            "Configuration saved in test_trainer/checkpoint-5500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-5500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-6000\n",
            "Configuration saved in test_trainer/checkpoint-6000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-6000/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-6500\n",
            "Configuration saved in test_trainer/checkpoint-6500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-6500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-7000\n",
            "Configuration saved in test_trainer/checkpoint-7000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-7000/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-7500\n",
            "Configuration saved in test_trainer/checkpoint-7500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-7500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-8000\n",
            "Configuration saved in test_trainer/checkpoint-8000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-8000/pytorch_model.bin\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 8077\n",
            "  Batch size = 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total papers : 8077\n",
            "Total CS papers : 430\n",
            "Total CS papers predicted : 293\n",
            "Total papers correctly predicted : 209\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to test_trainer/checkpoint-8500\n",
            "Configuration saved in test_trainer/checkpoint-8500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-8500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-9000\n",
            "Configuration saved in test_trainer/checkpoint-9000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-9000/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-9500\n",
            "Configuration saved in test_trainer/checkpoint-9500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-9500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-10000\n",
            "Configuration saved in test_trainer/checkpoint-10000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-10000/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-10500\n",
            "Configuration saved in test_trainer/checkpoint-10500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-10500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-11000\n",
            "Configuration saved in test_trainer/checkpoint-11000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-11000/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-11500\n",
            "Configuration saved in test_trainer/checkpoint-11500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-11500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-12000\n",
            "Configuration saved in test_trainer/checkpoint-12000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-12000/pytorch_model.bin\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 8077\n",
            "  Batch size = 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total papers : 8077\n",
            "Total CS papers : 430\n",
            "Total CS papers predicted : 479\n",
            "Total papers correctly predicted : 302\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=12057, training_loss=0.8265603963955229, metrics={'train_runtime': 9054.9024, 'train_samples_per_second': 10.652, 'train_steps_per_second': 1.332, 'total_flos': 1.98308008685376e+16, 'train_loss': 0.8265603963955229, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluation = trainer.evaluate()\n",
        "print(evaluation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "ZnKufBh5aiI2",
        "outputId": "46ba3309-a019-45ea-a098-0a0f131d73f5"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 8077\n",
            "  Batch size = 8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1010' max='1010' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1010/1010 04:04]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total papers : 8077\n",
            "Total CS papers : 430\n",
            "Total CS papers predicted : 479\n",
            "Total papers correctly predicted : 302\n",
            "{'eval_loss': 0.8815141916275024, 'eval_cs_precision': 0.6304801670146137, 'eval_cs_recall': 0.7023255813953488, 'eval_runtime': 245.1729, 'eval_samples_per_second': 32.944, 'eval_steps_per_second': 4.12, 'epoch': 3.0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Saving the Model"
      ],
      "metadata": {
        "id": "xArAkfKjiGEy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the files that can be directly loaded for model inference\n",
        "trainer.save_model(\"/content/scirepeval/final_checkpoint/\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mczHLv0gcZNM",
        "outputId": "984a3b08-009d-4f72-9a02-b81d9481ae5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to /content/scirepeval/final_checkpoint/\n",
            "Configuration saved in /content/scirepeval/final_checkpoint/config.json\n",
            "Model weights saved in /content/scirepeval/final_checkpoint/pytorch_model.bin\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Zip the final checkpoint\n",
        "!zip -r /content/scirepeval/final_checkpoint.zip /content/scirepeval/final_checkpoint/ "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C03jdITIPAzq",
        "outputId": "60e1615a-9ab0-4e40-8ace-9cfc8f2b287e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: content/scirepeval/final_checkpoint/ (stored 0%)\n",
            "  adding: content/scirepeval/final_checkpoint/pytorch_model.bin (deflated 7%)\n",
            "  adding: content/scirepeval/final_checkpoint/config.json (deflated 64%)\n",
            "  adding: content/scirepeval/final_checkpoint/training_args.bin (deflated 48%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Download the checkpoint\n",
        "from google.colab import files\n",
        "files.download('/content/scirepeval/final_checkpoint.zip')"
      ],
      "metadata": {
        "id": "eaarGXVxYPkr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Or, add it to Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VuAS6geHPANg",
        "outputId": "646a2153-166f-4383-99c1-b48c5db709ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp /content/scirepeval/final_checkpoint_small.zip '/content/gdrive/MyDrive/G25_IR_TermProject'"
      ],
      "metadata": {
        "id": "Kof9JTy2Rkuk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## INFERENCE"
      ],
      "metadata": {
        "id": "41UQhnSficE-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading pretrained model \n"
      ],
      "metadata": {
        "id": "dtVRT0FBoE9Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Install `gdown` using the following command, it will be needed to install files from Google Drive."
      ],
      "metadata": {
        "id": "vj2UpozLvgov"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gdown # Needed to install files from Google Drive"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wDuWn7bhQKDu",
        "outputId": "0819e24d-58a2-472f-e31a-5a5ee38e04f6"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.9/dist-packages (4.6.6)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.9/dist-packages (from gdown) (4.11.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from gdown) (1.16.0)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.9/dist-packages (from gdown) (2.28.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from gdown) (3.8.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from gdown) (4.64.0)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.9/dist-packages (from beautifulsoup4->gdown) (2.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests[socks]->gdown) (2022.6.15)\n",
            "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.9/dist-packages (from requests[socks]->gdown) (2.1.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests[socks]->gdown) (3.3)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests[socks]->gdown) (1.26.12)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.9/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Now use `gdown` to download the classifier model checkpoints and extract the checkpoint to the correct location."
      ],
      "metadata": {
        "id": "Gfu5ioO_oUz7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown https://drive.google.com/uc?id=1yGO03wbwVPyGe1HeSaQlYklXBgm0L0WG  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "61Fc76zIPHz4",
        "outputId": "30358eae-d97b-4808-ff9f-b87593a13fe7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1yGO03wbwVPyGe1HeSaQlYklXBgm0L0WG\n",
            "To: /content/scirepeval/classifier_checkpoint.zip\n",
            "100% 408M/408M [00:03<00:00, 116MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip classifier_checkpoint.zip -d /content/scirepeval/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_xtRkloaX3Om",
        "outputId": "86cf4494-8333-4d7b-9215-dce87c278366"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  classifier_checkpoint.zip\n",
            "   creating: /content/scirepeval/classifier_checkpoint/\n",
            "  inflating: /content/scirepeval/classifier_checkpoint/pytorch_model.bin  \n",
            "  inflating: /content/scirepeval/classifier_checkpoint/config.json  \n",
            "  inflating: /content/scirepeval/classifier_checkpoint/training_args.bin  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> You can now load the pretrained model and model config files from the checkpoint.\n",
        "\n",
        "**NOTE** : You might need to run the following cell twice "
      ],
      "metadata": {
        "id": "yr-MUr5slPbB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification, Trainer, TrainingArguments\n",
        "\n",
        "# Load the configuration from the config.json file\n",
        "pretrained_config = AutoConfig.from_pretrained(\"classifier_checkpoint/config.json\")\n",
        "\n",
        "# Load the model from the pytorch_model.bin file\n",
        "pretrained_model = AutoModelForSequenceClassification.from_pretrained(\"classifier_checkpoint/pytorch_model.bin\", config=pretrained_config)"
      ],
      "metadata": {
        "id": "xe59_pmGLYYV"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def compute_accuracy(pred_eval):\n",
        "    '''\n",
        "      Function to compute evaluation metrics like\n",
        "      overall accuracy, cs-specific and non-cs accuracy.\n",
        "    '''\n",
        "    logits, labels = pred_eval\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "\n",
        "    # Compute the accuracy\n",
        "    accuracy = np.mean(predictions == labels)\n",
        "    cs_label = labels_dict['Computer science']\n",
        "\n",
        "    num_total_papers = len(labels)\n",
        "    num_cs_papers = sum(np.array(labels == cs_label))\n",
        "    num_non_cs_papers = num_total_papers - num_cs_papers\n",
        "\n",
        "    cs_accuracy = np.mean((labels == cs_label) & (predictions == labels)) * (num_total_papers / num_cs_papers)\n",
        "    non_cs_accuracy = np.mean((labels != cs_label) & (predictions == labels)) * (num_total_papers / num_non_cs_papers)\n",
        "\n",
        "    return {\"accuracy\": accuracy, \"cs_accuracy\": cs_accuracy, \"non_cs_accuracy\": non_cs_accuracy}"
      ],
      "metadata": {
        "id": "C_76FSqRNjSp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model = pretrained_model,\n",
        "    eval_dataset=eval_dataset,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_accuracy,\n",
        ")"
      ],
      "metadata": {
        "id": "UHnot1DHM-19"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Trainer instance with the loaded model and training arguments\n",
        "trainer = Trainer(\n",
        "    model=pretrained_model\n",
        ")"
      ],
      "metadata": {
        "id": "BDPl6JgLLbT-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Single-input inference"
      ],
      "metadata": {
        "id": "-U7W24wgpwO2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Trying out inference on a single prompt\n",
        "import torch\n",
        "from transformers import AutoModel, AutoTokenizer, PretrainedConfig, Trainer\n",
        "\n",
        "# Original category : Biology (Label = 2)\n",
        "text = \"A method for the ultrastructural examination of cell monolayers cultured in plastic microtitre plates. A method is presented by which cells growing as monolayers cultured in microtitre plates can be embedded for electron microscopy. The technique has the following advantages: numerous specimens may be prepared with relatively small numbers of cells, cell-cell interactions remain undisturbed and may be enumerated, and morphology can be studied under circumstances identical with those used in cell-mediated cytotoxicity assays. \"\n",
        "\n",
        "# Load the pretrained SciBERT tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"allenai/scibert_scivocab_uncased\")\n",
        "\n",
        "encoding = tokenizer(text, return_tensors=\"pt\")\n",
        "encoding = {k: v.to(trainer.model.device) for k,v in encoding.items()}\n",
        "outputs = trainer.model(**encoding)\n",
        "\n",
        "logits = outputs.logits\n",
        "pred = torch.argmax(logits, axis = 1)[0].int().item() # Label = 2 stands for Biology"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "b0c7b28ea3434d768f91c750b9e84dfa",
            "ed3ae32771aa43f1ba97a1d57b214487",
            "a1c93466e5d94a8988dc416ed0fcca6a",
            "bdf5143ef7914d61acb469f29b698dc7",
            "f88b52f96afc48b7a34380355708966f",
            "b1e51b9c3dc0417ab50fc1df17fa8f20",
            "d5b04fb120c242e193a4fb570443e779",
            "a5199b71eb694de0a5acc855f1217972",
            "181275530f61478ebd86a0dbd230e8f1",
            "1153446d013d45c7a8e705a7ac25abb3",
            "ac995bc00c374f04a0555d15db0a5f17",
            "e33340e92fd8459d8e427b2a09d9c7cd",
            "9b921b8271a142ab8ccafb8e3f2a1a38",
            "f0f867a39a894356a5ad4a13ce3631e1",
            "dc8e78090d7c435bb92a7519eb182342",
            "0cce95fa4553420a854302dbb7c545e3",
            "fe00a731059d4208b8e8de92ed6ef7a2",
            "3ef042acaff14c04b6cb503808b7be95",
            "17e2a15407764774be510ae67856bf9a",
            "a355ca37b6714d19bf55cfb43912a2b5",
            "03a4d8fa5cb1404cb6ab40d710804034",
            "cb9f21c02183468bb7b30437f7134850"
          ]
        },
        "id": "YKfiVLHJZZb7",
        "outputId": "41b95eaf-a095-4ce0-f4cf-0e73a60c1e80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
            "https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmp44m4zkd1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/385 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b0c7b28ea3434d768f91c750b9e84dfa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "storing https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json in cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
            "creating metadata file for /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
            "loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.17.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 31090\n",
            "}\n",
            "\n",
            "https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/vocab.txt not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmp0ol2kcyk\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/223k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "storing https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/vocab.txt in cache at /root/.cache/huggingface/transformers/33593020f507d72099bd84ea6cd2296feb424fecd62d4a8edcc2a02899af6e29.38339d84e6e392addd730fd85fae32652c4cc7c5423633d6fa73e5f7937bbc38\n",
            "creating metadata file for /root/.cache/huggingface/transformers/33593020f507d72099bd84ea6cd2296feb424fecd62d4a8edcc2a02899af6e29.38339d84e6e392addd730fd85fae32652c4cc7c5423633d6fa73e5f7937bbc38\n",
            "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/vocab.txt from cache at /root/.cache/huggingface/transformers/33593020f507d72099bd84ea6cd2296feb424fecd62d4a8edcc2a02899af6e29.38339d84e6e392addd730fd85fae32652c4cc7c5423633d6fa73e5f7937bbc38\n",
            "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/tokenizer.json from cache at None\n",
            "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/added_tokens.json from cache at None\n",
            "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/special_tokens_map.json from cache at None\n",
            "loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/tokenizer_config.json from cache at None\n",
            "loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.17.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 31090\n",
            "}\n",
            "\n",
            "loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.17.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 31090\n",
            "}\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred # Actual label = 2 (Biology)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DSRGsjNkKuxD",
        "outputId": "923ec0d3-0ef4-4ab8-d12e-276f6541b25b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading the `high_influence_cite` dataset "
      ],
      "metadata": {
        "id": "yGMmf3oPp3TT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import datasets\n",
        "\n",
        "high_inf_cite = datasets.load_dataset('allenai/scirepeval', 'high_influence_cite')"
      ],
      "metadata": {
        "id": "NB1At7gVhvfj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377,
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        "outputId": "bede0740-652c-45ea-ab9d-f21ea984fb72"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/8.74k [00:00<?, ?B/s]"
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          "name": "stdout",
          "text": [
            "Downloading and preparing dataset scirepeval/high_influence_cite (download: 2.93 GiB, generated: 2.82 GiB, post-processed: Unknown size, total: 5.75 GiB) to /root/.cache/huggingface/datasets/allenai___scirepeval/high_influence_cite/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83...\n",
            "{'train': 'https://ai2-s2-research-public.s3.us-west-2.amazonaws.com/scirepeval/train/high_influence_cite/train.jsonl', 'val': 'https://ai2-s2-research-public.s3.us-west-2.amazonaws.com/scirepeval/train/high_influence_cite/val.jsonl', 'test': 'https://ai2-s2-research-public.s3.us-west-2.amazonaws.com/scirepeval/test/high_influence_cite/meta.jsonl'}\n"
          ]
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e82515fc52854f468c9aa2ce60406dbd"
            }
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          "metadata": {}
        },
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            "application/vnd.jupyter.widget-view+json": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "58ab4210455b4228a68af22452090769"
            }
          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Generating evaluation split:   0%|          | 0/1199 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3bbc2721f09848e5a7ce82a52aa18c4d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Generating train split:   0%|          | 0/58626 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ab85668880cc4af2b8a61a903215ed66"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Generating validation split:   0%|          | 0/7356 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2527023a307c4b15b4fec63892cf63ad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset scirepeval downloaded and prepared to /root/.cache/huggingface/datasets/allenai___scirepeval/high_influence_cite/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83. Subsequent calls will reuse this data.\n"
          ]
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "390296399a1440d095c20dd64763bad5"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "high_inf_cite"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8rF5DOTfs7NS",
        "outputId": "7dc55671-c167-4e40-836c-1212d6dec76b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    evaluation: Dataset({\n",
              "        features: ['query', 'candidates'],\n",
              "        num_rows: 1199\n",
              "    })\n",
              "    train: Dataset({\n",
              "        features: ['query', 'candidates'],\n",
              "        num_rows: 58626\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['query', 'candidates'],\n",
              "        num_rows: 7356\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hic_train = high_inf_cite[\"train\"]\n",
        "hic_train[3]['query']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fBZmZD6gvlWc",
        "outputId": "9d83f437-3006-446a-b10e-0ad653069607"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'doc_id': '41825099',\n",
              " 'title': 'Exact SER Analysis of DFrFT-Based QPSK OFDM System over Frequency Selective Rayleigh Fading Channel with CFO',\n",
              " 'abstract': 'We present the exact symbol error rate (SER) expression for quadrature phase shift keying (QPSK) modulation in frequency selective Rayleigh fading channel for discrete fractional Fourier transform- (DFrFT-) based orthogonal frequency division multiplexing (OFDM) system in the presence of carrier frequency offset (CFO). The theoretical result is confirmed by means of Monte Carlo simulations. It is shown that the performance of the proposed system, at different values of the DFrFT angle parameter źź,ź is better than that of OFDM based on discrete Fourier transform.',\n",
              " 'corpus_id': 41825099}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(hic_train[3]['candidates'])[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XEtTuye53Pu9",
        "outputId": "282e9762-609e-4149-c1bb-ffcd4e210a37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'doc_id': '14203403',\n",
              " 'title': 'Fractional Fourier transform as a signal processing tool: An overview of recent developments',\n",
              " 'abstract': 'Fractional Fourier transform (FRFT) is a generalization of the Fourier transform, rediscovered many times over the past 100 years. In this paper, we provide an overview of recent contributions pertaining to the FRFT. Specifically, the paper is geared toward signal processing practitioners by emphasizing the practical digital realizations and applications of the FRFT. It discusses three major topics. First, the manuscripts relates the FRFT to other mathematical transforms. Second, it discusses various approaches for practical realizations of the FRFT. Third, we overview the practical applications of the FRFT. From these discussions, we can clearly state that the FRFT is closely related to other mathematical transforms, such as time-frequency and linear canonical transforms. Nevertheless, we still feel that major contributions are expected in the field of the digital realizations and its applications, especially, since many digital realizations of the FRFT still lack properties of the continuous FRFT. Overall, the FRFT is a valuable signal processing tool. Its practical applications are expected to grow significantly in years to come, given that the FRFT offers many advantages over the traditional Fourier analysis.',\n",
              " 'corpus_id': 14203403,\n",
              " 'score': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Now, we classify the papers in the `high_influence_cite` subpart of the SciRepEval dataset using the pretrained SciBERT multi-label classifier."
      ],
      "metadata": {
        "id": "PLehOBZvZrf9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_length = 400\n",
        "\n",
        "def classify(example):\n",
        "    '''\n",
        "        Function to classify paper text + abstract into different domains \n",
        "        using pretrained classifier.\n",
        "    '''\n",
        "    # queries = examples['query']\n",
        "    \n",
        "    # for query in queries:\n",
        "    #     print(query)\n",
        "    #     break\n",
        "\n",
        "    # titles = [query['title'] for query in queries]\n",
        "    # abstracts = [query['abstract'] for query in queries]\n",
        "    # texts = [title + ' ' + abstract for title, abstract in zip(titles, abstracts)]\n",
        "\n",
        "    # encodings = tokenizer(text, max_length = max_length, truncation = True, return_tensors=\"pt\")\n",
        "    # encodings = {k: v.to(trainer.model.device) for k,v in encodings.items()}\n",
        "    # outputs = trainer.model(**encodings)\n",
        "\n",
        "    title = example['query']['title']\n",
        "    abstract = example['query']['abstract']\n",
        "    text = title + ' ' + abstract\n",
        "\n",
        "    encoding = tokenizer(text, max_length = max_length, truncation = True, return_tensors=\"pt\")\n",
        "    encoding = {k: v.to(trainer.model.device) for k,v in encoding.items()}\n",
        "    output = trainer.model(**encoding)\n",
        "\n",
        "    logits = output.logits\n",
        "    example['label'] = torch.argmax(logits, axis = 1)[0].int().item()\n",
        "\n",
        "    return example"
      ],
      "metadata": {
        "id": "ifcAst3K3Z3S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Ignore the next few cells, for DEBUGGING PURPOSES ONLY !!**"
      ],
      "metadata": {
        "id": "zhdcYmNsZSXx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hic_train_tiny = hic_train.shuffle(seed=42).select(range(100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MW_PuCnkSpWy",
        "outputId": "3a689272-3611-416f-cdfd-759939a2123d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/allenai___scirepeval/high_influence_cite/1.1.0/05b6b341c1750891a5539df5b3eb892babd18e8f299bfaba4735b87e20f6cf83/cache-2081574173c3ad90.arrow\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hic_train_tiny_clf = hic_train_tiny.map(classify)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "6bd191b8b2b74440bcaf4ab0e28f5944",
            "6bbe3ed8ddcc467993391ac12c459114",
            "24e367d31ea84f4a9d0236424efe8986",
            "4845249d93c5492bb9ecbe9c9738cd6d",
            "bf1d6a61b3ed42049883b119099be1fb",
            "4cefbe6851744c059591aab1ba57b3af",
            "19d00216e5e64026b7d226ed1b4c0cd4",
            "2f3fe6b6dd41408e9b6636e6d798171e",
            "d1a42c449c4947b3810771368ce5d10b",
            "bafde7bb890b491d8b88a741126b6794",
            "07c65a26770047c0b7791df40f850c4f"
          ]
        },
        "id": "wzrnuyLfRh1n",
        "outputId": "09898f61-f3ec-4634-cd44-1843e181be64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6bd191b8b2b74440bcaf4ab0e28f5944"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hic_train_tiny_clf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kJZJ1gMERvNF",
        "outputId": "d54d7a36-bf05-430f-c031-55c34257d7d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['query', 'candidates', 'label'],\n",
              "    num_rows: 100\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cs_label = 5"
      ],
      "metadata": {
        "id": "kgEzS77lV78s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hic_train_tiny_filter = hic_train_tiny_clf.filter(lambda example: example['label'] == cs_label)\n",
        "hic_train_tiny_filter"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "1a8a4e879faa4159b973aa4f316b21a6",
            "3ae7f417f8d640b9af17189503260362",
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            "5298a808ccd14a80935269712201da6b",
            "cd60a5f2a70c43fb889698e0d0c493a4",
            "a0dea7e5364443f3ac41412db79aa88d",
            "9911f1aefe974a3dad89ff181c1db65e",
            "3e9207bf7a0a4621a15495edf8c2ebb4",
            "2e322983b5ff42b3b386f61005bd54d8",
            "1e6e432f9764431bbfcf5a57669db76b",
            "801e8224fb7241bf89c72cf8bf684d30"
          ]
        },
        "id": "wcH072KhS2HD",
        "outputId": "56dac6d6-bbd4-40bf-b6ec-a44c3e5d1759"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1a8a4e879faa4159b973aa4f316b21a6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['query', 'candidates', 'label'],\n",
              "    num_rows: 12\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hic_train_tiny_filter['query']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PjcxofwwWBkG",
        "outputId": "c1863266-a24e-41d1-f0b0-8d8bef6965b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'doc_id': '5969778',\n",
              "  'title': 'Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement',\n",
              "  'abstract': 'The scalability of modern data centers has become a practical concern and has attracted significant attention in recent years. In contrast to existing solutions that require changes in the network architecture and the routing protocols, this paper proposes using traffic-aware virtual machine (VM) placement to improve the network scalability. By optimizing the placement of VMs on host machines, traffic patterns among VMs can be better aligned with the communication distance between them, e.g. VMs with large mutual bandwidth usage are assigned to host machines in close proximity. We formulate the VM placement as an optimization problem and prove its hardness. We design a two-tier approximate algorithm that efficiently solves the VM placement problem for very large problem sizes. Given the significant difference in the traffic patterns seen in current data centers and the structural differences of the recently proposed data center architectures, we further conduct a comparative analysis on the impact of the traffic patterns and the network architectures on the potential performance gain of traffic-aware VM placement. We use traffic traces collected from production data centers to evaluate our proposed VM placement algorithm, and we show a significant performance improvement compared to existing general methods that do not take advantage of traffic patterns and data center network characteristics.',\n",
              "  'corpus_id': 5969778},\n",
              " {'doc_id': '11934420',\n",
              "  'title': 'FRAMEWORK FOR DEVELOPING REAL-TIME MOBILE ROBOTIC APPLICATIONS BASED ON BEHAVIOURAL MODELS',\n",
              "  'abstract': 'Abstract In complex real-time control systems such as autonomous mobile robotic systems, the use of development tools for the design, analysis and validation of robotic applications is highly desirable, specially to improve the robot performances and to avoid early software and hardware design faults. This paper presents a framework for developing real-time mobile robotic applications. The main feature of the environment is that it permits the integrated analysis and validation of the functional behaviour of the robot (i.e. robot carrying out its plans) and the guarantee of the temporal constraints of the system processes (i.e. reactive and deliberative process execution). The verification of the correctness of the functional requirements is supported by means of a robot simulation tool that reflects the progress of the system at the application level. Likewise, a temporal analysis tool, based on scheduling analytical techniques, guarantees the schedulability of the system load at the execution level. The usefulness of the development tool is shown through design examples applied to mobile robotic applications.',\n",
              "  'corpus_id': 11934420},\n",
              " {'doc_id': '13892369',\n",
              "  'title': 'Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review.',\n",
              "  'abstract': 'Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned.',\n",
              "  'corpus_id': 13892369},\n",
              " {'doc_id': '5678393',\n",
              "  'title': 'HDR-ARtiSt: High dynamic range advanced real-time imaging system',\n",
              "  'abstract': 'This paper describes the HDR-ARtiSt hardware platform, a FPGA-based architecture that can produce a real-time high dynamic range video from successive image acquisition. The hardware platform is built around a standard low dynamic range (LDR) CMOS sensor and a Virtex 5 FPGA board. The CMOS sensor is a EV76C560 provided by e2v. This 1.3 Megapixel device offers novel pixel integration/readout modes and embedded image pre-processing capabilities including multiframe acquisition with various exposure times. Our approach consists of a hardware architecture with different algorithms: double exposure control during image capture, building of an HDR image by combining the multiple frames, and final tone mapping for viewing on a LCD display. Our video camera system is able to achieve a real-time video rate of 30 frames per second for a full sensor resolution of 1,280 × 1,024 pixels.',\n",
              "  'corpus_id': 5678393},\n",
              " {'doc_id': '2901353',\n",
              "  'title': 'Network Systems and String Stability',\n",
              "  'abstract': 'Network systems and their control are highly important and appear in a variety of applications, including vehicle platooning and formation con- trol. Especially vehicle platoons are highly investigated and an interesting problem that arises in this area is string stability, which broadly spoken means that a input signal amplifies unbounded as it travels through the vehicle string. However, various definitions are commonly used. In this paper, we aim to formalise the notion of string stability and illustrate the importance of those distinctions on simulation examples. A second goal is to generalise the found definitions for general network systems.',\n",
              "  'corpus_id': 2901353},\n",
              " {'doc_id': '150374073',\n",
              "  'title': 'Inferring Catchment in Internet Routing',\n",
              "  'abstract': 'BGP is the de-facto Internet routing protocol for exchanging prefix reachability information between Autonomous Systems (AS). It is a dynamic, distributed, path-vector protocol that enables rich expressions of network policies (typically treated as secrets). In this regime, where complexity is interwoven with information hiding, answering questions such as \"what is the expected catchment of the anycast sites of a content provider on the AS-level, if new sites are deployed?\", or \"how will load-balancing behave if an ISP changes its routing policy for a prefix?\", is a hard challenge. In this work, we present a formal model and methodology that takes into account policy-based routing and topological properties of the Internet graph, to predict the routing behavior of networks. We design algorithms that provide new capabilities for informative route inference (e.g., isolating the effect of randomness that is present in prior simulation-based approaches). We analyze the properties of these inference algorithms, and evaluate them using publicly available routing datasets and real-world experiments. The proposed framework can be useful in a number of applications: measurements, traffic engineering, network planning, Internet routing models, etc. As a use case, we study the problem of selecting a set of measurement vantage points to maximize route inference. Our methodology is general and can capture standard valley-free routing, as well as more complex topological and routing setups appearing in practice.',\n",
              "  'corpus_id': 150374073},\n",
              " {'doc_id': '11238636',\n",
              "  'title': 'Self-Adaptation-Based Dynamic Coalition Formation in a Distributed Agent Network: A Mechanism and a Brief Survey',\n",
              "  'abstract': 'In some real systems, e.g., distributed sensor networks, individual agents often need to form coalitions to accomplish complex tasks. Due to communication and computation constraints, it is infeasible for agents to directly interact with all other agents to form coalitions. Most previous coalition formation studies, however, overlooked this aspect. Those studies did not provide an explicitly modeled agent network or assumed that agents were in a fully connected network, where an agent can directly communicate with all other agents. Thus, to alleviate this problem, it is necessary to provide a neighborhood network structure, within which agents can directly interact only with their neighbors. Toward this end, in this paper, a self-adaptation-based dynamic coalition formation mechanism is proposed. The proposed mechanism operates in a neighborhood agent network. Based on self-adaptation principles, this mechanism enables agents to dynamically adjust their degrees of involvement in multiple coalitions and to join new coalitions at any time. The self-adaptation process, i.e., agents adjusting their degrees of involvement in multiple coalitions, is realized by exploiting a negotiation protocol. The proposed mechanism is evaluated through a comparison with a centralized mechanism (CM) and three other coalition formation mechanisms. Experimental results demonstrate the good performance of the proposed mechanism in terms of the entire network profit and time consumption. Additionally, a brief survey of current coalition formation research is also provided. From this survey, readers can have a general understanding of the focuses and progress of current research. This survey provides a classification of the primary emphasis of each related work in coalition formation, so readers can conveniently find the most related studies.',\n",
              "  'corpus_id': 11238636},\n",
              " {'doc_id': '70138630',\n",
              "  'title': 'A Transformation-Based Framework for KNN Set Similarity Search',\n",
              "  'abstract': 'Set similarity search is a fundamental operation in a variety of applications. While many previous studies focus on threshold based set similarity search and join, few efforts have been paid for KNN set similarity search. In this paper, we propose a transformation based framework to solve the problem of KNN set similarity search, which given a collection of set records and a query set, returns <inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq1-2886189.gif\"/></alternatives></inline-formula> results with the largest similarity to the query. We devise an effective transformation mechanism to transform sets with various lengths to fixed length vectors which can map similar sets closer to each other. Then, we index such vectors with a tiny tree structure. Next, we propose efficient search algorithms and pruning strategies to perform exact KNN set similarity search. We also design an estimation technique by leveraging the data distribution to support approximate KNN search, which can speed up the search while retaining high recall. Experimental results on real world datasets show that our framework significantly outperforms state-of-the-art methods in both memory and disk based settings.',\n",
              "  'corpus_id': 70138630},\n",
              " {'doc_id': '211532389',\n",
              "  'title': 'Exploiting Variable Impedance for Energy Efficient Sequential Movements',\n",
              "  'abstract': 'Compliant robotics have seen successful applications in energy efficient locomotion and cyclic manipulation. However, fully exploitation of variable physical impedance for energy efficient sequential movements has not been extensively addressed. This work employs a hierarchical approach to encapsulate low-level optimal control for sub-movement generation into an outer loop of iterative policy improvement, thereby benefits of both optimal control and reinforcement learning are leveraged. The framework enables optimizing efficiency trade-off for minimal energy expenses in a model-free manner, by taking account of cost function weighting, variable impedance exploitation, and transition timing, which are associated with the skill of compliance. The effectiveness of the proposed method is evaluated using two consecutive reaching tasks on a variable impedance actuator. The results demonstrate significant energy saving by improving the skill of compliance, with a 30% electrical consumption reduction measured on hardware.',\n",
              "  'corpus_id': 211532389},\n",
              " {'doc_id': '211055840',\n",
              "  'title': 'Scheduling Scientific Workflow Using Multi-Objective Algorithm With Fuzzy Resource Utilization in Multi-Cloud Environment',\n",
              "  'abstract': 'The provision of resources and services for scientific workflow applications using a multi-cloud architecture and a pay-per-use rule has recently gained popularity within the cloud computing research domain. This is because workflow applications are computation intensive. Most of the existing studies on workflow scheduling in the cloud mainly focus on finding an ideal makespan or cost. Nevertheless, there are other important quality of service metrics that are of critical concern in workflow scheduling such as reliability and resource utilization. In this respect, this paper proposes a new multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS) for scheduling scientific workflow based on particle swarm optimization (PSO) method. The algorithm minimizes cost and makespan while considering reliability constraint. The coding scheme jointly considers task execution location and data transportation order. Simulation experiments reveal that FR-MOS outperforms the basic MOS over the PSO algorithm.',\n",
              "  'corpus_id': 211055840},\n",
              " {'doc_id': '211069522',\n",
              "  'title': 'Interpretable Companions for Black-Box Models',\n",
              "  'abstract': 'We present an interpretable companion model for any pre-trained black-box classifiers. The idea is that for any input, a user can decide to either receive a prediction from the black-box model, with high accuracy but no explanations, or employ a companion rule to obtain an interpretable prediction with slightly lower accuracy. The companion model is trained from data and the predictions of the black-box model, with the objective combining area under the transparency--accuracy curve and model complexity. Our model provides flexible choices for practitioners who face the dilemma of choosing between always using interpretable models and always using black-box models for a predictive task, so users can, for any given input, take a step back to resort to an interpretable prediction if they find the predictive performance satisfying, or stick to the black-box model if the rules are unsatisfying. To show the value of companion models, we design a human evaluation on more than a hundred people to investigate the tolerable accuracy loss to gain interpretability for humans.',\n",
              "  'corpus_id': 211069522},\n",
              " {'doc_id': '13302710',\n",
              "  'title': 'Anytime solution optimization for sampling-based motion planning',\n",
              "  'abstract': 'Recent work in sampling-based motion planning has yielded several different approaches for computing good quality paths in high degree of freedom systems: path shortcutting methods that attempt to shorten a single solution path by connecting non-consecutive configurations, a path hybridization technique that combines portions of two or more solutions to form a shorter path, and asymptotically optimal algorithms that converge to the shortest path over time. This paper presents an extensible meta-algorithm that incorporates a traditional sampling-based planning algorithm with offline path shortening techniques to form an anytime algorithm which exhibits competitive solution lengths to the best known methods and optimizers. A series of experiments involving rigid motion and complex manipulation are performed as well as a comparison with asymptotically optimal methods which show the efficacy of the proposed scheme, particularly in high-dimensional spaces.',\n",
              "  'corpus_id': 13302710}]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hic_train_tiny_cs = hic_train_tiny_filter.remove_columns('label')"
      ],
      "metadata": {
        "id": "1Z2xUjhlWSIf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hic_train_tiny_cs"
      ],
      "metadata": {
        "colab": {
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        "id": "apKy9JfcYvLx",
        "outputId": "dba31b3d-e1e4-41c9-c8d6-72603c3bc697"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['query', 'candidates'],\n",
              "    num_rows: 12\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hic_train_tiny_cs[3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "uPD_skOjYxeO",
        "outputId": "62545aef-3294-4406-cf1b-bfb6a5f8d099"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'query': {'doc_id': '5678393',\n",
              "  'title': 'HDR-ARtiSt: High dynamic range advanced real-time imaging system',\n",
              "  'abstract': 'This paper describes the HDR-ARtiSt hardware platform, a FPGA-based architecture that can produce a real-time high dynamic range video from successive image acquisition. The hardware platform is built around a standard low dynamic range (LDR) CMOS sensor and a Virtex 5 FPGA board. The CMOS sensor is a EV76C560 provided by e2v. This 1.3 Megapixel device offers novel pixel integration/readout modes and embedded image pre-processing capabilities including multiframe acquisition with various exposure times. Our approach consists of a hardware architecture with different algorithms: double exposure control during image capture, building of an HDR image by combining the multiple frames, and final tone mapping for viewing on a LCD display. Our video camera system is able to achieve a real-time video rate of 30 frames per second for a full sensor resolution of 1,280 × 1,024 pixels.',\n",
              "  'corpus_id': 5678393},\n",
              " 'candidates': [{'doc_id': '62742488',\n",
              "   'title': \"A 1/3'' VGA linear wide dynamic range CMOS image sensor implementing a predictive multiple sampling algorithm with overlapping integration intervals\",\n",
              "   'abstract': 'Many tasks performed by machine vision systems involve processing of natural scenes with large intra-frame illumination ratios. Thus, wide dynamic range visible spectrum image sensors are required to achieve adequate processing performance and reliability. An image sensor implementing an algorithm that linearly increases the illumination dynamic range of solid-state pixels is presented. Optimal exposure is achieved with a predictive pixel saturation decision that allows for multiple integration intervals of different duration to run concurrently for different pixels while keeping the sensor frame rate constant. A proof-of-concept chip was fabricated in a 0.18-/spl mu/m CMOS process. Added functionality to standard imagers is mainly concentrated off-pixel so fill factor is not sacrificed. Measured data corroborates the algorithm functionality.',\n",
              "   'corpus_id': 62742488,\n",
              "   'score': 0},\n",
              "  {'doc_id': '42847118',\n",
              "   'title': 'A high-sensitivity CMOS image sensor with gain-adaptive column amplifiers',\n",
              "   'abstract': 'A high-sensitivity CMOS image sensor using gain-adaptive column amplifiers is presented and tested. The use of high gain for the column amplifier reduces input-referred random noise, and when coupled with a column-based digital noise cancellation technique, also reduces fixed pattern noise. An experimental application of the circuit using 0.25-/spl mu/m CMOS technology with pinned photodiodes gave an rms random noise of 263 /spl mu/V and an rms fixed pattern noise of 50 /spl mu/V.',\n",
              "   'corpus_id': 42847118,\n",
              "   'score': 0},\n",
              "  {'doc_id': '73530571',\n",
              "   'title': 'A 1000 FPS at 128 128 Vision Processor With 8-Bit Digitized I / O',\n",
              "   'abstract': 'This paper presents a mixed-signal programmable chip for high-speed vision applications. It consists of an array of processing elements, arranged to operate in accordance with the principles of single instruction multiple data (SIMD) computing architectures. This chip, implemented in a 0.35m fully digital CMOS technology, contains 3.75 M transistors and exhibits peak performance figures of 330 GOPS (8-bit equivalent giga-operations per second), 3.6 GOPS mm and 82.5 GOPS/W. It includes structures for image acquisition and for image processing, meaning that it does not require a separate imager for operation. At the sensory side, integration and log-compression sensing circuits are embedded, thus allowing the chip to handle a large variety of illumination conditions. At the processing plane, analog and digital circuits are employed whose parameters can be programmed and their architecture reconfigured for the realization of software-coded processing algorithms. The chip provides, and accepts, 8-bit digitized data through a 32-bit bidirectional data bus which operates at 120 MB/s. Experimental results show that frame rates of 1000 frames per second (FPS) can be achieved under room illumination conditions — applications using exposures of about 50 s have been recently reached by using special illumination setups. The chip can capture an image, run approximately 150 two-dimensional linear convolutions, and download the result in 8-bit digital format, in less than 1 ms. This feature, together with the possibility of executing sequences of user-definable instructions (stored on a full-custom 32-kb on-chip memory), and storing intermediate results (up to 8 grayscale images) makes the chip a true general-purpose sensory/processing device.',\n",
              "   'corpus_id': 73530571,\n",
              "   'score': 0},\n",
              "  {'doc_id': '6391848',\n",
              "   'title': 'A 10 000 fps CMOS Sensor With Massively Parallel Image Processing',\n",
              "   'abstract': 'A high-speed analog VLSI image acquisition and pre-processing system has been designed and fabricated in a 0.35 ¿m standard CMOS process. The chip features a massively parallel architecture enabling the computation of programmable low-level image processing in each pixel. Extraction of spatial gradients and convolutions such as Sobel or Laplacian filters are implemented on the circuit. For this purpose, each 35 ¿m × 35 ¿m pixel includes a photodiode, an amplifier, two storage capacitors, and an analog arithmetic unit based on a four-quadrant multiplier architecture. The retina provides address-event coded output on three asynchronous buses: one output dedicated to the gradient and the other two to the pixel values. A 64 × 64 pixel proof-of-concept chip was fabricated. A dedicated embedded platform including FPGA and ADCs has also been designed to evaluate the vision chip. Measured results show that the proposed sensor successfully captures raw images up to 10 000 frames per second and runs low-level image processing at a frame rate of 2000 to 5000 frames per second.',\n",
              "   'corpus_id': 6391848,\n",
              "   'score': 0},\n",
              "  {'doc_id': '52852816',\n",
              "   'title': 'Recovering high dynamic range radiance maps from photographs',\n",
              "   'abstract': 'We present a method of recovering high dynamic range radiance maps from photographs taken with conventional imaging equipment. In our method, multiple photographs of the scene are taken with different amounts of exposure. Our algorithm uses these differently exposed photographs to recover the response function of the imaging process, up to factor of scale, using the assumption of reciprocity. With the known response function, the algorithm can fuse the multiple photographs into a single, high dynamic range radiance map whose pixel values are proportional to the true radiance values in the scene. We demonstrate our method on images acquired with both photochemical and digital imaging processes. We discuss how this work is applicable in many areas of computer graphics involving digitized photographs, including image-based modeling, image compositing, and image processing. Lastly, we demonstrate a few applications of having high dynamic range radiance maps, such as synthesizing realistic motion blur and simulating the response of the human visual system.',\n",
              "   'corpus_id': 52852816,\n",
              "   'score': 1},\n",
              "  {'doc_id': '13946222',\n",
              "   'title': 'High dynamic range video',\n",
              "   'abstract': 'Typical video footage captured using an off-the-shelf camcorder suffers from limited dynamic range. This paper describes our approach to generate high dynamic range (HDR) video from an image sequence of a dynamic scene captured while rapidly varying the exposure of each frame. Our approach consists of three parts: automatic exposure control during capture, HDR stitching across neighboring frames, and tonemapping for viewing. HDR stitching requires accurately registering neighboring frames and choosing appropriate pixels for computing the radiance map. We show examples for a variety of dynamic scenes. We also show how we can compensate for scene and camera movement when creating an HDR still from a series of bracketed still photographs.',\n",
              "   'corpus_id': 13946222,\n",
              "   'score': 0},\n",
              "  {'doc_id': '9567855',\n",
              "   'title': 'Tone Reproduction and Physically Based Spectral Rendering',\n",
              "   'abstract': 'The ultimate aim of realistic graphics is the creation of images that provoke the same responses that a viewer would have to a real scene. This STAR addresses two related key problem areas in this effort which are located at opposite ends of the rendering pipeline, namely the data structures used to describe light during the actual rendering process, and the issue of displaying such radiant intensities in a meaningful way. The interest in the first of these subproblems stems from the fact that it is common industry practice to use RGB colour values to describe light intensity and surface reflectancy. While viable in the context of methods that do not strive to achieve true realism, this approach has to be replaced by more physically accurate techniques if a prediction of nature is intended. The second subproblem is that while research into ways of rendering images provides us with better and faster methods, we do not necessarily see their full effect due to limitations of the display hardware. The low dynamic range of a standard computer monitor requires some form of mapping to produce images that are perceptually accurate. Tone reproduction operators attempt to replicate the effect of real-world luminance intensities. This STAR report will review the work to date on spectral rendering and tone reproduction techniques. It will include an investigation into the need for spectral imagery synthesis methods and accurate tone reproduction, and a discussion of major approaches to physically correct rendering and key tone mapping algorithms. The future of both spectral rendering and tone reproduction techniques will be considered, together with the implications of advances in display hardware.',\n",
              "   'corpus_id': 9567855,\n",
              "   'score': 0},\n",
              "  {'doc_id': '4829240',\n",
              "   'title': 'Evaluation of HDR tone mapping methods using essential perceptual attributes',\n",
              "   'abstract': 'The problem of reproducing high dynamic range images on media with restricted dynamic range has gained a lot of interest in the computer graphics community. There exist various approaches to this issue, which span several research areas including computer graphics, image processing, color vision, physiological aspects, etc. These approaches assume a thorough knowledge of both the objective and subjective attributes of an image. However, no comprehensive overview and analysis of such attributes has been published so far. In this contribution, we present an overview about the effects of basic image attributes in high dynamic range tone mapping. Furthermore, we propose a scheme of relationships between these attributes, leading to the definition of an overall image quality measure. We present results of subjective psychophysical experiments that we have performed to prove the proposed relationship scheme. Moreover, we also present an evaluation of existing tone mapping methods (operators) with regard to these attributes. Finally, the execution of with reference and without a real reference perceptual experiments gave us the opportunity to relate the obtained subjective results. Our effort is not just useful to get into the tone mapping field or when implementing a tone mapping method, but it also sets the stage for well-founded quality comparisons between tone mapping methods. By providing good definitions of the different attributes, user-driven or fully automatic comparisons are made possible.',\n",
              "   'corpus_id': 4829240,\n",
              "   'score': 0},\n",
              "  {'doc_id': '21392061',\n",
              "   'title': 'A real-time FPGA-based architecture for a Reinhard-like tone mapping operator',\n",
              "   'abstract': 'This paper presents a field-programmable gate array-based hardware architecture for a Reinhard-like tone mapping operator. Modifications to the original Reinhard operator were done to ensure that the operator is amenable to implementation in hardware. The architecture is described in VHDL and has been synthesized using Altera Quartus tools. It achieves an operating frequency consistent with a video rate of 60 frames per second for a frame of 1024x768 pixels. The quality of the implementation is measured using peak signal-to-noise ratios on testbench images.',\n",
              "   'corpus_id': 21392061,\n",
              "   'score': 0},\n",
              "  {'doc_id': '2234474',\n",
              "   'title': 'Photographic tone reproduction for digital images',\n",
              "   'abstract': 'A classic photographic task is the mapping of the potentially high dynamic range of real world luminances to the low dynamic range of the photographic print. This tone reproduction problem is also faced by computer graphics practitioners who map digital images to a low dynamic range print or screen. The work presented in this paper leverages the time-tested techniques of photographic practice to develop a new tone reproduction operator. In particular, we use and extend the techniques developed by Ansel Adams to deal with digital images. The resulting algorithm is simple and produces good results for a wide variety of images.',\n",
              "   'corpus_id': 2234474,\n",
              "   'score': 0},\n",
              "  {'doc_id': '17469529',\n",
              "   'title': 'A real-time implementation of gradient domain high dynamic range compression using a local Poisson solver',\n",
              "   'abstract': 'This paper presents a real-time hardware implementation of a gradient domain dynamic range compression algorithm for high dynamic range (HDR) images. This technique works by calculating the gradients of the HDR image, manipulating those gradients, and reconstructing an output low dynamic range image that corresponds to the manipulated gradients. Reconstruction involves solving the Poisson equation. We propose a Poisson solver that utilizes only local information around each pixel along with special boundary conditions, and requires a small and fixed amount of hardware for any image size, with no need to buffer the entire image. The hardware implementation is described in VHDL and synthesized for a field programmable gate array (FPGA) device. The maximum operating frequency achieved is fast enough to process high dynamic range videos with one megapixel per frame at a rate of about 100 frames per second. The hardware is tested on standard HDR images from the Debevec library. The output images produced have good visual quality.',\n",
              "   'corpus_id': 17469529,\n",
              "   'score': 0},\n",
              "  {'doc_id': '6110371',\n",
              "   'title': 'Gradient Domain High Dynamic Range Compression',\n",
              "   'abstract': 'We present a new method for rendering high dynamic range images on conventional displays. Our method is conceptually simple, computationally efficient, robust, and easy to use. We manipulate the gradient field of the luminance image by attenuating the magnitudes of large gradients. A new, low dynamic range image is then obtained by solving a Poisson equation on the modified gradient field. Our results demonstrate that the method is capable of drastic dynamic range compression, while preserving fine details and avoiding common artifacts, such as halos, gradient reversals, or loss of local contrast. The method is also able to significantly enhance ordinary images by bringing out detail in dark regions. CR Categories: I.3.3 [Computer Graphics]: Picture/image generation—display algorithms, viewing algorithms; I.4.3 [Image Processing and Computer Vision]: Enhancement—filtering, grayscale manipulation, sharpening and deblurring',\n",
              "   'corpus_id': 6110371,\n",
              "   'score': 0},\n",
              "  {'doc_id': '6229988',\n",
              "   'title': 'High Dynamic Range Video through Fusion of Exposured-Controlled Frames',\n",
              "   'abstract': 'In this paper, we present a method for generating high dynamic range (HDR) video through fusion of exposure-controlled frames in a stationary video camera system. Frames captured by commercial video cameras have a limit dynamic range, even if we use auto-exposure control (AEC), and these frames often show saturation areas which may degrade the image quality. To overcome these, in the video sequence alternating short and long exposures, we combine neighboring frames using an image fusion approach, since images under different exposures have different visibility in a scene. When we fuse exposure frames, there are two considerations; controlling exposures and handling moving objects. For the first, we control short and long exposures with the AEC approach. For the other, we adjust fusion masks to reduce ghosting artifacts by moving objects. Our method has the advantage of automatically selecting proper exposure times to make outputs more visible than fixed exposure ones. It also reduces motion blur distortion caused by moving objects in output frames, by using the maskadjustment method which is simpler and faster than frame registration methods.To verify the proposed method, we show experimental results using a real scene.',\n",
              "   'corpus_id': 6229988,\n",
              "   'score': 0},\n",
              "  {'doc_id': '28427063',\n",
              "   'title': 'A versatile HDR video production system',\n",
              "   'abstract': 'Although High Dynamic Range (HDR) imaging has been the subject of significant research over the past fifteen years, the goal of acquiring cinema-quality HDR images of fast-moving scenes using available components has not yet been achieved. In this work, we present an optical architecture for HDR imaging that allows simultaneous capture of high, medium, and low-exposure images on three sensors at high fidelity with efficient use of the available light. We also present an HDR merging algorithm to complement this architecture, which avoids undesired artifacts when there is a large exposure difference between the images. We implemented a prototype high-definition HDR-video system and we present still frames from the acquired HDR video, tonemapped with various techniques.',\n",
              "   'corpus_id': 28427063,\n",
              "   'score': 0},\n",
              "  {'doc_id': '13429653',\n",
              "   'title': 'Radiometric self calibration',\n",
              "   'abstract': 'A simple algorithm is described that computes the radiometric response function of an imaging system, from images of an arbitrary scene taken using different exposures. The exposure is varied by changing either the aperture setting or the shutter speed. The algorithm does not require precise estimates of the exposures used. Rough estimates of the ratios of the exposures (e.g. F-number settings on an inexpensive lens) are sufficient for accurate recovery of the response function as well as the actual exposure ratios. The computed response function is used to fuse the multiple images into a single high dynamic range radiance image. Robustness is tested using a variety of scenes and cameras as well as noisy synthetic images generated using 100 randomly selected response curves. Automatic rejection of image areas that have large vignetting effects or temporal scene variations make the algorithm applicable to not just photographic but also video cameras.',\n",
              "   'corpus_id': 13429653,\n",
              "   'score': 0},\n",
              "  {'doc_id': '14744949',\n",
              "   'title': 'Estimation-theoretic approach to dynamic range enhancement using multiple exposures',\n",
              "   'abstract': \"We present a new approach for improving the effective dynamic range of cameras by using multiple photographs of the same scene taken with different exposure times. Using this method enables the photographer to accurately capture scenes that contain high dynamic range by using a device with low dynamic range, which allows the capture of scenes that have both very bright and very dark regions. We approach the problem from a probabilistic standpoint, distinguishing it from the other methods reported in the literature on photographic dynamic range improvement. A new method is proposed for determining the camera's response function, which is an iterative procedure that need be done only once for a particular camera. With the response function known, high dynamic range images can be easily constructed by a weighted average of the input images. The particular form of weighting is controlled by the probabilistic formulation of the problem, and results in higher weight being assigned to pixels taken at longer exposure times. The advantages of this new weighting scheme are explained by com- parison with other methods in the literature. Experimental results are presented to demonstrate the utility of the algorithm. © 2003 SPIE\",\n",
              "   'corpus_id': 14744949,\n",
              "   'score': 0},\n",
              "  {'doc_id': '8554207',\n",
              "   'title': 'Optimal HDR reconstruction with linear digital cameras',\n",
              "   'abstract': 'Given a multi-exposure sequence of a scene, our aim is to recover the absolute irradiance falling onto a linear camera sensor. The established approach is to perform a weighted average of the scaled input exposures. However, there is no clear consensus on the appropriate weighting to use. We propose a weighting function that produces statistically optimal estimates under the assumption of compound-Gaussian noise. Our weighting is based on a calibrated camera model that accounts for all noise sources. This model also allows us to simultaneously estimate the irradiance and its uncertainty. We evaluate our method on simulated and real world photographs, and show that we consistently improve the signal-to-noise ratio over previous approaches. Finally, we show the effectiveness of our model for optimal exposure sequence selection and HDR image denoising.',\n",
              "   'corpus_id': 8554207,\n",
              "   'score': 0},\n",
              "  {'doc_id': '8667973',\n",
              "   'title': 'Tone-mapping high dynamic range images by novel histogram adjustment',\n",
              "   'abstract': 'In this paper, we present novel histogram adjustment methods for displaying high dynamic range image. We first present a global histogram adjustment based tone mapping operator, which well reproduces global contrast for high dynamic range images. We then segment images and carry out adaptive contrast adjustment using our global tone mapping operator in the local regions to reproduce local contrast and ensure better quality. We demonstrate that our methods are fast, easy to use and a fixed set of parameter values produce good results for a wide variety of images.',\n",
              "   'corpus_id': 8667973,\n",
              "   'score': 0}]}"
            ]
          },
          "metadata": {},
          "execution_count": 38
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "f88430f283ee492b8afb37e694089ef2"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "shutil.make_archive(\"hic_cs_papers\", \"zip\", \"hic_cs_papers\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "DEwxzFpysR7P",
        "outputId": "43328510-5343-434f-a144-ffd3b9a17852"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/scirepeval/hic_cs_papers.zip'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Download the zipped dataset\n",
        "from google.colab import files\n",
        "files.download(\"hic_cs_papers.zip\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "Ie3OJX8Gsfxu",
        "outputId": "6721192b-dbec-44e0-edf5-4f7798ab99d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_935974ca-add2-4608-80ef-d615624485d4\", \"hic_cs_papers.zip\", 168913)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf hic_cs_papers"
      ],
      "metadata": {
        "id": "6B5XMqketX9Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Classifying papers in the `high_influence_cite` dataset"
      ],
      "metadata": {
        "id": "YFBKsdCaau5m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cs_label = 5  # Actual label for Computer science domain papers in 'Field of Study' dataset"
      ],
      "metadata": {
        "id": "WbAcuoQabvMP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hic_train_clf = hic_train.map(classify)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "17d19c98668f42c9b9ee3178afd69c36",
            "44eefd286a1646939703e9ce8c345890",
            "983c2b56bc774d96b18a1d4b2f27d769",
            "c4146294597442c798cf8d3605652e0e",
            "b24f90bfc1b24d38b8201b55078d5d08",
            "e857e93ac4ea4b72b98de2ca0ae913a3",
            "02a89a4177104b03b25c8b60004838f2",
            "11d58fbb50ff4fbda095b820c6222e19",
            "7b4fb728d96f4df8ad9cb8decd6a76ea",
            "4408b4f040794f97baafc24b3e456989",
            "dd34307dd79949f8b9d636f41366d662"
          ]
        },
        "id": "nKEcvi7pbdpy",
        "outputId": "1b4f4a95-d8bd-4113-8d96-997f75a9ae2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/58626 [00:00<?, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "17d19c98668f42c9b9ee3178afd69c36"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hic_train_filter = hic_train_clf.filter(lambda example: example['label'] == cs_label)\n",
        "hic_train_cs = hic_train_filter.remove_columns('label')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "45caf7e2c2d24bd29592487af725ae31",
            "bb4007d0525d4f0d8486187fe80c3a3c",
            "70220c9f19f84a55a71141bd7303baca",
            "514b42c074f441ef9ff855b76989e089",
            "6a6fec393a4b424692c695d0f870045f",
            "d9cdceb2d5124330b645fa5a1b9162dd",
            "18b96770bf3d46099d8399c17114166c",
            "20b82df7e50b41e287053285138ffdc4",
            "12ca7ba71f5c4c9db4409e2ab30f44cc",
            "beb52ef0942f43e3b8ef66d384a7c06d",
            "1cfc592b122342b2b50e8a91eef235a7"
          ]
        },
        "id": "5g-ro3qRpHA8",
        "outputId": "cb0f9c81-b9b7-4783-a45a-33eb3bd7d5c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/59 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "45caf7e2c2d24bd29592487af725ae31"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hic_train_cs.save_to_disk(\"hic_cs_papers\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "06a24ad4d1384ff08b473f7b44c6cddd",
            "88e91d75f2de4f6a9b0862ae01f2cad7",
            "815a1b2e00e44e43ac332de56498a317",
            "9f6f18aa55c54fdf91555e31eda7910e",
            "1054be0927954c0d905d87a4b3d2797d",
            "254c42c353404d37a37250357aabb8ab",
            "c5dcd1061a7d488ab07ced439850d574",
            "4d0e86546edd47a18699e037c3263225",
            "894e7bc193a649108910b642eb9bdbe8",
            "2b320d1dbdb14175907bc08765f37924",
            "2385f1815a3c44b9b76dd2498a338d74"
          ]
        },
        "id": "v0CMHng2sipz",
        "outputId": "109f260a-606d-4712-ad0a-8ad38d003132"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Flattening the indices:   0%|          | 0/7 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "06a24ad4d1384ff08b473f7b44c6cddd"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Zip the dataset directory\n",
        "import shutil\n",
        "shutil.make_archive(\"hic_cs_papers\", \"zip\", \"hic_cs_papers\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "jyuXR6odtuAD",
        "outputId": "d31171c6-8aab-46ac-e29a-c7d0cdd1eba3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/scirepeval/hic_cs_papers.zip'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add it to Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jD9IjGZauoh2",
        "outputId": "fbf17726-00c5-4c30-fc9f-6e58b95bbfc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp /content/scirepeval/hic_cs_papers.zip '/content/gdrive/MyDrive/G25_IR_TermProject'"
      ],
      "metadata": {
        "id": "zAx4GeX9vPdo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Or, download the zipped dataset\n",
        "from google.colab import files\n",
        "files.download(\"hic_cs_papers.zip\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "QT4ASbXvtzZo",
        "outputId": "6edc32d3-0212-4e70-8319-97295e04bcaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_93b81593-9cab-4d26-85e4-a98c9bdc7e73\", \"hic_cs_papers.zip\", 56002530)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load dataset from Google Drive"
      ],
      "metadata": {
        "id": "ppPC8su74O-j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Install `gdown` using the following command, it will be needed to install files from Google Drive."
      ],
      "metadata": {
        "id": "ZpR3KldZ5Kdc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gdown # Needed to install files from Google Drive"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "daf61649-f851-43c8-b629-d5c5c92c90c9",
        "id": "6-6P_o9a5Kdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.9/dist-packages (4.6.6)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.9/dist-packages (from gdown) (2.28.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from gdown) (1.16.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.9/dist-packages (from gdown) (4.11.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from gdown) (3.8.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from gdown) (4.64.0)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.9/dist-packages (from beautifulsoup4->gdown) (2.4)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests[socks]->gdown) (3.3)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests[socks]->gdown) (1.26.12)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests[socks]->gdown) (2022.6.15)\n",
            "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.9/dist-packages (from requests[socks]->gdown) (2.1.1)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.9/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Now use `gdown` to download the CS-classified papers and extract the file to the correct location."
      ],
      "metadata": {
        "id": "EQYVc8zF5Kde"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown https://drive.google.com/uc?id=1b0g06yMQCjOoO0VaRAs3fNk5Kvqni8Ka  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbb5c618-78d5-46db-e322-377aae90686e",
        "id": "Wg5RwGmd5Kde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1b0g06yMQCjOoO0VaRAs3fNk5Kvqni8Ka\n",
            "To: /content/scirepeval/hic_cs_papers.zip\n",
            "100% 56.0M/56.0M [00:00<00:00, 264MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip hic_cs_papers.zip -d /content/scirepeval/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b1eb3ab-dcb1-47bf-ccfe-ea960fca0a05",
        "id": "MOXcJ9Hf5Kde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  hic_cs_papers.zip\n",
            "  inflating: /content/scirepeval/dataset.arrow  \n",
            "  inflating: /content/scirepeval/dataset_info.json  \n",
            "  inflating: /content/scirepeval/state.json  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# To load the dataset back from disk\n",
        "from datasets import load_from_disk\n",
        "\n",
        "dataset = load_from_disk('hic_cs_papers')"
      ],
      "metadata": {
        "id": "E63w2Lx9t-xQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2QCgjrkA6Ckr",
        "outputId": "5d4853d4-7e51-4c96-f9b8-b39c4bce20c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['query', 'candidates'],\n",
              "    num_rows: 6165\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gr2aCpcq6VXq",
        "outputId": "e97477b4-a831-4da8-c52d-6d91a9cfbaef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'query': {'doc_id': '8202993',\n",
              "  'title': 'Coherent Filtering: Detecting Coherent Motions from Crowd Clutters',\n",
              "  'abstract': 'Coherent motions, which describe the collective movements of individuals in crowd, widely exist in physical and biological systems. Understanding their underlying priors and detecting various coherent motion patterns from background clutters have both scientific values and a wide range of practical applications, especially for crowd motion analysis. In this paper, we propose and study a prior of coherent motion called Coherent Neighbor Invariance, which characterizes the local spatiotemporal relationships of individuals in coherent motion. Based on the coherent neighbor invariance, a general technique of detecting coherent motion patterns from noisy time-series data called Coherent Filtering is proposed. It can be effectively applied to data with different distributions at different scales in various real-world problems, where the environments could be sparse or extremely crowded with heavy noise. Experimental evaluation and comparison on synthetic and real data show the existence of Coherence Neighbor Invariance and the effectiveness of our Coherent Filtering.',\n",
              "  'corpus_id': 8202993},\n",
              " 'candidates': [{'doc_id': '15175317',\n",
              "   'title': 'Collective cognition in animal groups',\n",
              "   'abstract': 'The remarkable collective action of organisms such as swarming ants, schooling fish and flocking birds has long captivated the attention of artists, naturalists, philosophers and scientists. Despite a long history of scientific investigation, only now are we beginning to decipher the relationship between individuals and group-level properties. This interdisciplinary effort is beginning to reveal the underlying principles of collective decision-making in animal groups, demonstrating how social interactions, individual state, environmental modification and processes of informational amplification and decay can all play a part in tuning adaptive response. It is proposed that important commonalities exist with the understanding of neuronal processes and that much could be learned by considering collective animal behavior in the framework of cognitive science.',\n",
              "   'corpus_id': 15175317,\n",
              "   'score': 0},\n",
              "  {'doc_id': '205981453',\n",
              "   'title': 'Collective Information Processing and Pattern Formation in Swarms, Flocks, and Crowds',\n",
              "   'abstract': \"The spontaneous organization of collective activities in animal groups and societies has attracted a considerable amount of attention over the last decade. This kind of coordination often permits group-living species to achieve collective tasks that are far beyond single individuals' capabilities. In particular, a key benefit lies in the integration of partial knowledge of the environment at the collective level. In this contribution, we discuss various self-organization phenomena in animal swarms and human crowds from the point of view of information exchange among individuals. In particular, we provide a general description of collective dynamics across species and introduce a classification of these dynamics not only with respect to the way information is transferred among individuals but also with regard to the knowledge processing at the collective level. Finally, we highlight the fact that the individual's ability to learn from past experiences can have a feedback effect on the collective dynamics, as experienced with the development of behavioral conventions in pedestrian crowds.\",\n",
              "   'corpus_id': 205981453,\n",
              "   'score': 0},\n",
              "  {'doc_id': '34354788',\n",
              "   'title': 'Self-Organization in Biological Systems',\n",
              "   'abstract': 'From the Publisher: \\n\"Broad in scope, thorough yet accessible, this book is a self-contained introduction to self-organization and complexity in biology - a field of study at the forefront of life sciences research.\"--BOOK JACKET.',\n",
              "   'corpus_id': 34354788,\n",
              "   'score': 0},\n",
              "  {'doc_id': '10627743',\n",
              "   'title': 'Counting Crowded Moving Objects',\n",
              "   'abstract': 'In its full generality, motion analysis of crowded objects necessitates recognition and segmentation of each moving entity. The difficulty of these tasks increases considerably with occlusions and therefore with crowding. When the objects are constrained to be of the same kind, however, partitioning of densely crowded semi-rigid objects can be accomplished by means of clustering tracked feature points. We base our approach on a highly parallelized version of the KLT tracker in order to process the video into a set of feature trajectories. While such a set of trajectories provides a substrate for motion analysis, their unequal lengths and fragmented nature present difficulties for subsequent processing. To address this, we propose a simple means of spatially and temporally conditioning the trajectories. Given this representation, we integrate it with a learned object descriptor to achieve a segmentation of the constituent motions. We present experimental results for the problem of estimating the number of moving objects in a dense crowd as a function of time.',\n",
              "   'corpus_id': 10627743,\n",
              "   'score': 1},\n",
              "  {'doc_id': '11636674',\n",
              "   'title': 'Unsupervised Bayesian Detection of Independent Motion in Crowds',\n",
              "   'abstract': 'While crowds of various subjects may offer applicationspecific cues to detect individuals, we demonstrate that for the general case, motion itself contains more information than previously exploited. This paper describes an unsupervised data driven Bayesian clustering algorithm which has detection of individual entities as its primary goal. We track simple image features and probabilistically group them into clusters representing independently moving entities. The numbers of clusters and the grouping of constituent features are determined without supervised learning or any subject-specific model. The new approach is instead, that space-time proximity and trajectory coherence through image space are used as the only probabilistic criteria for clustering. An important contribution of this work is how these criteria are used to perform a one-shot data association without iterating through combinatorial hypotheses of cluster assignments. Our proposed general detection algorithm can be augmented with subject-specific filtering, but is shown to already be effective at detecting individual entities in crowds of people, insects, and animals. This paper and the associated video examine the implementation and experiments of our motion clustering framework.',\n",
              "   'corpus_id': 11636674,\n",
              "   'score': 1},\n",
              "  {'doc_id': '9560504',\n",
              "   'title': 'Floor Fields for Tracking in High Density Crowd Scenes',\n",
              "   'abstract': 'This paper presents an algorithm for tracking individual targets in high density crowd scenes containing hundreds of people. Tracking in such a scene is extremely challenging due to the small number of pixels on the target, appearance ambiguity resulting from the dense packing, and severe inter-object occlusions. The novel tracking algorithm, which is outlined in this paper, will overcome these challenges using a scene structure based force model. In this force model an individual, when moving in a particular scene, is subjected to global and local forces that are functions of the layout of that scene and the locomotive behavior of other individuals in the scene. The key ingredients of the force model are three floor fields, which are inspired by the research in the field of evacuation dynamics, namely Static Floor Field (SFF), Dynamic Floor Field (DFF), and Boundary Floor Field (BFF). These fields determine the probability of move from one location to another by converting the long-range forces into local ones. The SFF specifies regions of the scene which are attractive in nature (e.g. an exit location). The DFF specifies the immediate behavior of the crowd in the vicinity of the individual being tracked. The BFF specifies influences exhibited by the barriers in the scene (e.g. walls, no-go areas). By combining cues from all three fields with the available appearance information, we track individual targets in high density crowds.',\n",
              "   'corpus_id': 9560504,\n",
              "   'score': 0},\n",
              "  {'doc_id': '8190391',\n",
              "   'title': 'A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis',\n",
              "   'abstract': 'This paper proposes a framework in which Lagrangian particle dynamics is used for the segmentation of high density crowd flows and detection of flow instabilities. For this purpose, a flow field generated by a moving crowd is treated as an aperiodic dynamical system. A grid of particles is overlaid on the flow field, and is advected using a numerical integration scheme. The evolution of particles through the flow is tracked using a flow map, whose spatial gradients are subsequently used to setup a Cauchy Green deformation tensor for quantifying the amount by which the neighboring particles have diverged over the length of the integration. The maximum eigenvalue of the tensor is used to construct a finite time Lyapunov exponent (FTLE) field, which reveals the Lagrangian coherent structures (LCS) present in the underlying flow. The LCS divide flow into regions of qualitatively different dynamics and are used to locate boundaries of the flow segments in a normalized cuts framework. Any change in the number of flow segments over time is regarded as an instability, which is detected by establishing correspondences between flow segments over time. The experiments are conducted on a challenging set of videos taken from Google Video and a National Geographic documentary.',\n",
              "   'corpus_id': 8190391,\n",
              "   'score': 0},\n",
              "  {'doc_id': '4242592',\n",
              "   'title': 'Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models',\n",
              "   'abstract': 'We propose a novel unsupervised learning framework to model activities and interactions in crowded and complicated scenes. Hierarchical Bayesian models are used to connect three elements in visual surveillance: low-level visual features, simple \"atomic\" activities, and interactions. Atomic activities are modeled as distributions over low-level visual features, and multi-agent interactions are modeled as distributions over atomic activities. These models are learnt in an unsupervised way. Given a long video sequence, moving pixels are clustered into different atomic activities and short video clips are clustered into different interactions. In this paper, we propose three hierarchical Bayesian models, Latent Dirichlet Allocation (LDA) mixture model, Hierarchical Dirichlet Process (HDP) mixture model, and Dual Hierarchical Dirichlet Processes (Dual-HDP) model. They advance existing language models, such as LDA [1] and HDP [2]. Our data sets are challenging video sequences from crowded traffic scenes and train station scenes with many kinds of activities co-occurring. Without tracking and human labeling effort, our framework completes many challenging visual surveillance tasks of board interest such as: (1) discovering typical atomic activities and interactions; (2) segmenting long video sequences into different interactions; (3) segmenting motions into different activities; (4) detecting abnormality; and (5) supporting high-level queries on activities and interactions.',\n",
              "   'corpus_id': 4242592,\n",
              "   'score': 0},\n",
              "  {'doc_id': '3085328',\n",
              "   'title': 'Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents',\n",
              "   'abstract': \"In this paper, a new Mixture model of Dynamic pedestrian-Agents (MDA) is proposed to learn the collective behavior patterns of pedestrians in crowded scenes. Collective behaviors characterize the intrinsic dynamics of the crowd. From the agent-based modeling, each pedestrian in the crowd is driven by a dynamic pedestrian-agent, which is a linear dynamic system with its initial and termination states reflecting a pedestrian's belief of the starting point and the destination. Then the whole crowd is modeled as a mixture of dynamic pedestrian-agents. Once the model is unsupervisedly learned from real data, MDA can simulate the crowd behaviors. Furthermore, MDA can well infer the past behaviors and predict the future behaviors of pedestrians given their trajectories only partially observed, and classify different pedestrian behaviors in the scene. The effectiveness of MDA and its applications are demonstrated by qualitative and quantitative experiments on the video surveillance dataset collected from the New York Grand Central Station.\",\n",
              "   'corpus_id': 3085328,\n",
              "   'score': 0},\n",
              "  {'doc_id': '4231589',\n",
              "   'title': 'Random field topic model for semantic region analysis in crowded scenes from tracklets',\n",
              "   'abstract': 'In this paper, a Random Field Topic (RFT) model is proposed for semantic region analysis from motions of objects in crowded scenes. Different from existing approaches of learning semantic regions either from optical flows or from complete trajectories, our model assumes that fragments of trajectories (called tracklets) are observed in crowded scenes. It advances the existing Latent Dirichlet Allocation topic model, by integrating the Markov random fields (MR-F) as prior to enforce the spatial and temporal coherence between tracklets during the learning process. Two kinds of MRF, pairwise MRF and the forest of randomly spanning trees, are defined. Another contribution of this model is to include sources and sinks as high-level semantic prior, which effectively improves the learning of semantic regions and the clustering of tracklets. Experiments on a large scale data set, which includes 40, 000+ tracklets collected from the crowded New York Grand Central station, show that our model outperforms state-of-the-art methods both on qualitative results of learning semantic regions and on quantitative results of clustering tracklets.',\n",
              "   'corpus_id': 4231589,\n",
              "   'score': 0},\n",
              "  {'doc_id': '16967272',\n",
              "   'title': 'Learning Semantic Scene Models by Trajectory Analysis',\n",
              "   'abstract': 'In this paper, we describe an unsupervised learning framework to segment a scene into semantic regions and to build semantic scene models from long-term observations of moving objects in the scene. First, we introduce two novel similarity measures for comparing trajectories in far-field visual surveillance. The measures simultaneously compare the spatial distribution of trajectories and other attributes, such as velocity and object size, along the trajectories. They also provide a comparison confidence measure which indicates how well the measured image-based similarity approximates true physical similarity. We also introduce novel clustering algorithms which use both similarity and comparison confidence. Based on the proposed similarity measures and clustering methods, a framework to learn semantic scene models by trajectory analysis is developed. Trajectories are first clustered into vehicles and pedestrians, and then further grouped based on spatial and velocity distributions. Different trajectory clusters represent different activities. The geometric and statistical models of structures in the scene, such as roads, walk paths, sources and sinks, are automatically learned from the trajectory clusters. Abnormal activities are detected using the semantic scene models. The system is robust to low-level tracking errors.',\n",
              "   'corpus_id': 16967272,\n",
              "   'score': 0},\n",
              "  {'doc_id': '61244467',\n",
              "   'title': 'Learning visual flows: A Lie algebraic approach',\n",
              "   'abstract': 'We present a novel method for modeling dynamic visual phenomena, which consists of two key aspects. First, the integral motion of constituent elements in a dynamic scene is captured by a common underlying geometric transform process. Second, a Lie algebraic representation of the transform process is introduced, which maps the transformation group to a vector space, and thus overcomes the difficulties due to the group structure. Consequently, the statistical learning techniques based on vector spaces can be readily applied. Moreover, we discuss the intrinsic connections between the Lie algebra and the Linear dynamical processes, showing that our model induces spatially varying fields that can be estimated from local motions without continuous tracking. Following this, we further develop a statistical framework to robustly learn the flow models from noisy and partially corrupted observations. The proposed methodology is demonstrated on real world phenomenon, inferring common motion patterns from surveillance videos of crowded scenes and satellite data of weather evolution.',\n",
              "   'corpus_id': 61244467,\n",
              "   'score': 0},\n",
              "  {'doc_id': '9945811',\n",
              "   'title': 'Learning motion patterns in crowded scenes using motion flow field',\n",
              "   'abstract': 'Learning typical motion patterns or activities from videos of crowded scenes is an important visual surveillance problem. To detect typical motion patterns in crowded scenarios, we propose a new method which utilizes the instantaneous motions of a video, i.e, the motion flow field, instead of long-term motion tracks. The motion flow field is a union of independent flow vectors computed in different frames. Detecting motion patterns in this flow field can therefore be formulated as a clustering problem of the motion flow fields, where each motion pattern consists of a group of flow vectors participating in the same process or motion. We first construct a directed neighborhood graph to measure the closeness of flow vectors. A hierarchical agglomerative clustering algorithm is applied to group flow vectors into desired motion patterns.',\n",
              "   'corpus_id': 9945811,\n",
              "   'score': 0},\n",
              "  {'doc_id': '8527449',\n",
              "   'title': 'Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models',\n",
              "   'abstract': 'We propose a novel framework of using a nonparametric Bayesian model, called Dual Hierarchical Dirichlet Processes (Dual-HDP) (Wang et al. in IEEE Trans. Pattern Anal. Mach. Intell. 31:539–555, 2009), for unsupervised trajectory analysis and semantic region modeling in surveillance settings. In our approach, trajectories are treated as documents and observations of an object on a trajectory are treated as words in a document. Trajectories are clustered into different activities. Abnormal trajectories are detected as samples with low likelihoods. The semantic regions, which are subsets of paths commonly taken by objects and are related to activities in the scene, are also modeled. Under Dual-HDP, both the number of activity categories and the number of semantic regions are automatically learnt from data. In this paper, we further extend Dual-HDP to a Dynamic Dual-HDP model which allows dynamic update of activity models and online detection of normal/abnormal activities. Experiments are evaluated on a simulated data set and two real data sets, which include 8,478 radar tracks collected from a maritime port and 40,453 visual tracks collected from a parking lot.',\n",
              "   'corpus_id': 8527449,\n",
              "   'score': 0},\n",
              "  {'doc_id': '124790682',\n",
              "   'title': 'A Benchmark for the Comparison of 3-D Motion Segmentation Algorithms',\n",
              "   'abstract': 'Over the past few years, several methods for segmenting a scene containing multiple rigidly moving objects have been proposed. However, most existing methods have been tested on a handful of sequences only, and each method has been often tested on a different set of sequences. Therefore, the comparison of different methods has been fairly limited. In this paper, we compare four 3-D motion segmentation algorithms for affine cameras on a benchmark of 155 motion sequences of checkerboard, traffic, and articulated scenes.',\n",
              "   'corpus_id': 124790682,\n",
              "   'score': 1},\n",
              "  {'doc_id': '696914',\n",
              "   'title': 'Generalized principal component analysis (GPCA)',\n",
              "   'abstract': 'This paper presents an algebro-geometric solution to the problem of segmenting an unknown number of subspaces of unknown and varying dimensions from sample data points. We represent the subspaces with a set of homogeneous polynomials whose degree is the number of subspaces and whose derivatives at a data point give normal vectors to the subspace passing through the point. When the number of subspaces is known, we show that these polynomials can be estimated linearly from data; hence, subspace segmentation is reduced to classifying one point per subspace. We select these points optimally from the data set by minimizing certain distance function, thus dealing automatically with moderate noise in the data. A basis for the complement of each subspace is then recovered by applying standard PCA to the collection of derivatives (normal vectors). Extensions of GPCA that deal with data in a high-dimensional space and with an unknown number of subspaces are also presented. Our experiments on low-dimensional data show that GPCA outperforms existing algebraic algorithms based on polynomial factorization and provides a good initialization to iterative techniques such as k-subspaces and expectation maximization. We also present applications of GPCA to computer vision problems such as face clustering, temporal video segmentation, and 3D motion segmentation from point correspondences in multiple affine views.',\n",
              "   'corpus_id': 696914,\n",
              "   'score': 0},\n",
              "  {'doc_id': '16608752',\n",
              "   'title': 'Object Segmentation by Long Term Analysis of Point Trajectories',\n",
              "   'abstract': 'Unsupervised learning requires a grouping step that defines which data belong together. A natural way of grouping in images is the segmentation of objects or parts of objects. While pure bottom-up segmentation from static cues is well known to be ambiguous at the object level, the story changes as soon as objects move. In this paper, we present a method that uses long term point trajectories based on dense optical flow. Defining pair-wise distances between these trajectories allows to cluster them, which results in temporally consistent segmentations of moving objects in a video shot. In contrast to multi-body factorization, points and even whole objects may appear or disappear during the shot. We provide a benchmark dataset and an evaluation method for this so far uncovered setting.',\n",
              "   'corpus_id': 16608752,\n",
              "   'score': 0},\n",
              "  {'doc_id': '972888',\n",
              "   'title': 'Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography',\n",
              "   'abstract': 'A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing',\n",
              "   'corpus_id': 972888,\n",
              "   'score': 0},\n",
              "  {'doc_id': '37698619',\n",
              "   'title': 'Decision-making processes: The case of collective movements',\n",
              "   'abstract': \"Besides focusing on the adaptive significance of collective movements, it is crucial to study the mechanisms and dynamics of decision-making processes at the individual level underlying the higher-scale collective movements. It is now commonly admitted that collective decisions emerge from interactions between individuals, but how individual decisions are taken, i.e. how far they are modulated by the behaviour of other group members, is an under-investigated question. Classically, collective movements are viewed as the outcome of one individual's initiation (the leader) for departure, by which all or some of the other group members abide. Individuals assuming leadership have often been considered to hold a specific social status. This hierarchical or centralized control model has been challenged by recent theoretical and experimental findings, suggesting that leadership can be more distributed. Moreover, self-organized processes can account for collective movements in many different species, even in those that are characterized by high cognitive complexity. In this review, we point out that decision-making for moving collectively can be reached by a combination of different rules, i.e. individualized (based on inter-individual differences in physiology, energetic state, social status, etc.) and self-organized (based on simple response) ones for any species, context and group size.\",\n",
              "   'corpus_id': 37698619,\n",
              "   'score': 0},\n",
              "  {'doc_id': '23420153',\n",
              "   'title': 'The neurophysiology of figure-ground segregation in primary visual cortex',\n",
              "   'abstract': 'The activity of neurons in the primary visual cortex of the awake macaque monkey was recorded while the animals were viewing full screen arrays of either oriented line segments or moving random dots. A square patch of the screen was made to perceptually pop out as a circumscribed figure by virtue of differences between the orientation or the direction of motion of the texture elements within that patch and the surround. The animals were trained to identify the figure patches by making saccadic eye movements towards their positions. Almost every cell gave a significantly larger response to elements belonging to the figure than to similar elements belonging to the background. The figure- ground response enhancement was present along the entire extent of the patch and was absent as soon as the receptive field was outside the patch. The strength of the effect had no relation with classical receptive field properties like orientation or direction selectivity or receptive field size. The response enhancement had a latency of 30–40 msec relative to the onset of the neuronal response itself. The results show that context modulation within primary visual cortex has a highly sophisticated nature, putting the image features the cells are responding to into their fully evaluated perceptual context.',\n",
              "   'corpus_id': 23420153,\n",
              "   'score': 0},\n",
              "  {'doc_id': '14848918',\n",
              "   'title': 'Normalized cuts and image segmentation',\n",
              "   'abstract': 'We propose a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at extracting the global impression of an image. We treat image segmentation as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups. We show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion. We have applied this approach to segmenting static images and found results very encouraging.',\n",
              "   'corpus_id': 14848918,\n",
              "   'score': 0},\n",
              "  {'doc_id': '691081',\n",
              "   'title': 'Mean Shift: A Robust Approach Toward Feature Space Analysis',\n",
              "   'abstract': 'A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.',\n",
              "   'corpus_id': 691081,\n",
              "   'score': 0},\n",
              "  {'doc_id': '8973749',\n",
              "   'title': 'Clustering of time series data - a survey',\n",
              "   'abstract': 'Time series clustering has been shown effective in providing useful information in various domains. There seems to be an increased interest in time series clustering as part of the effort in temporal data mining research. To provide an overview, this paper surveys and summarizes previous works that investigated the clustering of time series data in various application domains. The basics of time series clustering are presented, including general-purpose clustering algorithms commonly used in time series clustering studies, the criteria for evaluating the performance of the clustering results, and the measures to determine the similarity/dissimilarity between two time series being compared, either in the forms of raw data, extracted features, or some model parameters. The past researchs are organized into three groups depending upon whether they work directly with the raw data either in the time or frequency domain, indirectly with features extracted from the raw data, or indirectly with models built from the raw data. The uniqueness and limitation of previous research are discussed and several possible topics for future research are identified. Moreover, the areas that time series clustering have been applied to are also summarized, including the sources of data used. It is hoped that this review will serve as the steppingstone for those interested in advancing this area of research.',\n",
              "   'corpus_id': 8973749,\n",
              "   'score': 0},\n",
              "  {'doc_id': '5755403',\n",
              "   'title': 'A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance',\n",
              "   'abstract': 'This paper presents a survey of trajectory-based activity analysis for visual surveillance. It describes techniques that use trajectory data to define a general set of activities that are applicable to a wide range of scenes and environments. Events of interest are detected by building a generic topographical scene description from underlying motion structure as observed over time. The scene topology is automatically learned and is distinguished by points of interest and motion characterized by activity paths. The methods we review are intended for real-time surveillance through definition of a diverse set of events for further analysis triggering, including virtual fencing, speed profiling, behavior classification, anomaly detection, and object interaction.',\n",
              "   'corpus_id': 5755403,\n",
              "   'score': 0},\n",
              "  {'doc_id': '3068944',\n",
              "   'title': 'Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions',\n",
              "   'abstract': \"This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings. We first identify several application scenarios for the resultant 'knowledge reuse' framework that we call cluster ensembles. The cluster ensemble problem is then formalized as a combinatorial optimization problem in terms of shared mutual information. In addition to a direct maximization approach, we propose three effective and efficient techniques for obtaining high-quality combiners (consensus functions). The first combiner induces a similarity measure from the partitionings and then reclusters the objects. The second combiner is based on hypergraph partitioning. The third one collapses groups of clusters into meta-clusters which then compete for each object to determine the combined clustering. Due to the low computational costs of our techniques, it is quite feasible to use a supra-consensus function that evaluates all three approaches against the objective function and picks the best solution for a given situation. We evaluate the effectiveness of cluster ensembles in three qualitatively different application scenarios: (i) where the original clusters were formed based on non-identical sets of features, (ii) where the original clustering algorithms worked on non-identical sets of objects, and (iii) where a common data-set is used and the main purpose of combining multiple clusterings is to improve the quality and robustness of the solution. Promising results are obtained in all three situations for synthetic as well as real data-sets.\",\n",
              "   'corpus_id': 3068944,\n",
              "   'score': 0},\n",
              "  {'doc_id': '16060712',\n",
              "   'title': 'Improving Data Association by Joint Modeling of Pedestrian Trajectories and Groupings',\n",
              "   'abstract': 'We consider the problem of data association in a multiperson tracking context. In semi-crowded environments, people are still discernible as individually moving entities, that undergo many interactions with other people in their direct surrounding. Finding the correct association is therefore difficult, but higher-order social factors, such as group membership, are expected to ease the problem. However, estimating group membership is a chicken-and-egg problem: knowing pedestrian trajectories, it is rather easy to find out possible groupings in the data, but in crowded scenes, it is often difficult to estimate closely interacting trajectories without further knowledge about groups. To this end, we propose a third-order graphical model that is able to jointly estimate correct trajectories and group memberships over a short time window. A set of experiments on challenging data underline the importance of joint reasoning for data association in crowded scenarios.',\n",
              "   'corpus_id': 16060712,\n",
              "   'score': 0}]}"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "72VvOCcd6YdO"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}