QuestionAnswering.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/0xJCarlos/QuestionAnswering/blob/main/QuestionAnswering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Primeramente instalamos las librerias necesarias"
      ],
      "metadata": {
        "id": "4QZPw3213dIU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
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          "height": 49,
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        },
        "id": "jS88QlEMSEhn",
        "outputId": "be8d5e55-7aab-4a81-a135-63f627d3d5cf"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Cargar dataset\n",
        "from datasets import load_dataset\n",
        "squad = load_dataset(\"squad\", split=\"train[:5000]\")"
      ],
      "metadata": {
        "id": "gzPUJQiSSOYa"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Dividir el dataset en sets de train y test\n",
        "\n",
        "squad = squad.train_test_split(test_size=0.2)"
      ],
      "metadata": {
        "id": "0dzTm97u3_8w"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Observar un ejemplo\n",
        "squad[\"train\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DBcyoWZ44oGN",
        "outputId": "c687c58b-457e-418f-d133-12704bb52bbe"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': '56cfed4d234ae51400d9c0df',\n",
              " 'title': 'New_York_City',\n",
              " 'context': \"New York City has the largest European and non-Hispanic white population of any American city. At 2.7 million in 2012, New York's non-Hispanic white population is larger than the non-Hispanic white populations of Los Angeles (1.1 million), Chicago (865,000), and Houston (550,000) combined. The European diaspora residing in the city is very diverse. According to 2012 Census estimates, there were roughly 560,000 Italian Americans, 385,000 Irish Americans, 253,000 German Americans, 223,000 Russian Americans, 201,000 Polish Americans, and 137,000 English Americans. Additionally, Greek and French Americans numbered 65,000 each, with those of Hungarian descent estimated at 60,000 people. Ukrainian and Scottish Americans numbered 55,000 and 35,000, respectively. People identifying ancestry from Spain numbered 30,838 total in 2010. People of Norwegian and Swedish descent both stood at about 20,000 each, while people of Czech, Lithuanian, Portuguese, Scotch-Irish, and Welsh descent all numbered between 12,000–14,000 people. Arab Americans number over 160,000 in New York City, with the highest concentration in Brooklyn. Central Asians, primarily Uzbek Americans, are a rapidly growing segment of the city's non-Hispanic white population, enumerating over 30,000, and including over half of all Central Asian immigrants to the United States, most settling in Queens or Brooklyn. Albanian Americans are most highly concentrated in the Bronx.\",\n",
              " 'question': 'NYC has the largest white population by how many people?',\n",
              " 'answers': {'text': ['2.7 million'], 'answer_start': [98]}}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Cargar DistilBERT tokenizer para procesar los campos \"question\" y \"context\"\n",
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")"
      ],
      "metadata": {
        "id": "i9jtIjBH4skL"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### HAYALGUNOS PASOS DE PREPROCESAMIENTO PARA QUESTION-ANSWERING DE LOS QUE SE DEBERÍA ESTÁR CONSCIENTES:\n",
        "\n",
        "#1. Some examples in a dataset may have a very long context that exceeds the maximum input length of the model.\n",
        "#To deal with longer sequences, truncate only the context by setting truncation=\"only_second\"\n",
        "\n",
        "#2. Next, map the start and end positions of the answer to the original context by setting\n",
        "# return_offset_mapping=True\n",
        "\n",
        "#3. With the mapping in hand, now you can find the start and end tokens of the answer.\n",
        "#Use the sequence_ids method to find which part of the offset corresponds to the question and which corresponds to the context."
      ],
      "metadata": {
        "id": "ci3Iw6a4674i"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_function(examples):\n",
        "    questions = [q.strip() for q in examples[\"question\"]]\n",
        "    inputs = tokenizer(\n",
        "        questions,\n",
        "        examples[\"context\"],\n",
        "        max_length=384,\n",
        "        truncation=\"only_second\",\n",
        "        return_offsets_mapping=True,\n",
        "        padding=\"max_length\",\n",
        "    )\n",
        "\n",
        "    offset_mapping = inputs.pop(\"offset_mapping\")\n",
        "    answers = examples[\"answers\"]\n",
        "    start_positions = []\n",
        "    end_positions = []\n",
        "\n",
        "    for i, offset in enumerate(offset_mapping):\n",
        "        answer = answers[i]\n",
        "        start_char = answer[\"answer_start\"][0]\n",
        "        end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n",
        "        sequence_ids = inputs.sequence_ids(i)\n",
        "\n",
        "        # Find the start and end of the context\n",
        "        idx = 0\n",
        "        while sequence_ids[idx] != 1:\n",
        "            idx += 1\n",
        "        context_start = idx\n",
        "        while sequence_ids[idx] == 1:\n",
        "            idx += 1\n",
        "        context_end = idx - 1\n",
        "\n",
        "        # If the answer is not fully inside the context, label it (0, 0)\n",
        "        if offset[context_start][0] > end_char or offset[context_end][1] < start_char:\n",
        "            start_positions.append(0)\n",
        "            end_positions.append(0)\n",
        "        else:\n",
        "            # Otherwise it's the start and end token positions\n",
        "            idx = context_start\n",
        "            while idx <= context_end and offset[idx][0] <= start_char:\n",
        "                idx += 1\n",
        "            start_positions.append(idx - 1)\n",
        "\n",
        "            idx = context_end\n",
        "            while idx >= context_start and offset[idx][1] >= end_char:\n",
        "                idx -= 1\n",
        "            end_positions.append(idx + 1)\n",
        "\n",
        "    inputs[\"start_positions\"] = start_positions\n",
        "    inputs[\"end_positions\"] = end_positions\n",
        "    return inputs\n"
      ],
      "metadata": {
        "id": "ExMGnS7H8FG8"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Usar la funcion map para aplicar la función de preprocesamiento en todo el dataset. Puedes acelerarlo usando batched=True\n",
        "tokenized_squad = squad.map(preprocess_function, batched=True, remove_columns=squad[\"train\"].column_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "a72de37483af45eaa8b6f9d007303a60",
            "2b434c0577e343038b3396c960a78d43",
            "60a4902aa29f4893bb9b1060e35cfea4",
            "b887564f23584abb9a76045c83ec7c8c",
            "aa2812fc900b4565accbfe2fd7de9d59",
            "28b7fe7f9d944f5fa39f8ddefcc71e7f",
            "e29aa49338354d128c86554bf90abf35",
            "23d2c15fd8874831a4363ab9e2fcab49",
            "b542a2cde9c149af837f68fa95cd79bf",
            "638d71198a7748c3bd7a4c34a10d2875",
            "b0ff36f470424b5d9dcd57c331942a77",
            "5566f04594564849b9e94b5d6bf53f70",
            "74541463d3f449d2835babc084524c70",
            "69e03752dabb49019cab8be562d137f8",
            "b41b0c810b0a44dcb60d1581a1ff057e",
            "8289df1a3fb444ee9648f257ef4da14e",
            "0928280164594cf88d33ea7fb26688e6",
            "2add097d86c7408dabf6f9ba4fbf9d54",
            "b87ca736302d44569738034eb5c3ca9a",
            "ddc40f03e4b749329f2a024aebfbb9b0",
            "d02e0d2c690145259f4fc8f72eb513c4",
            "5fac01dc235442bb8e828b7f5b09d9c2"
          ]
        },
        "id": "c0yXz12nBdDO",
        "outputId": "a688d24a-bc03-4bfd-ba4f-5772d66305ee"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/4000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a72de37483af45eaa8b6f9d007303a60"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5566f04594564849b9e94b5d6bf53f70"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Crear un batch de ejemplos usando DefaultDataCollator. No aplica ningún preprocesamiento adicional como padding\n",
        "from transformers import DefaultDataCollator\n",
        "data_collator = DefaultDataCollator(return_tensors=\"tf\")"
      ],
      "metadata": {
        "id": "Co6nnrnKDFsZ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Entrenar el modelo!"
      ],
      "metadata": {
        "id": "0wicEmGoJ1xo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import  create_optimizer\n",
        "\n",
        "batch_size = 16\n",
        "num_epochs = 2\n",
        "total_train_steps = (len(tokenized_squad[\"train\"]) // batch_size) * num_epochs\n",
        "optimizer, schedule = create_optimizer(\n",
        "    init_lr = 2e-5,\n",
        "    num_warmup_steps = 0,\n",
        "    num_train_steps = total_train_steps,\n",
        ")"
      ],
      "metadata": {
        "id": "N5qmHRH0EMug"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TFAutoModelForQuestionAnswering\n",
        "\n",
        "model = TFAutoModelForQuestionAnswering.from_pretrained(\"distilbert-base-uncased\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PLlLuMN-KDGh",
        "outputId": "6e566284-6034-492f-c1f6-081f3da4e8a7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFDistilBertForQuestionAnswering: ['vocab_layer_norm.weight', 'vocab_transform.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_projector.bias']\n",
            "- This IS expected if you are initializing TFDistilBertForQuestionAnswering from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForQuestionAnswering from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights or buffers of the TF 2.0 model TFDistilBertForQuestionAnswering were not initialized from the PyTorch model and are newly initialized: ['qa_outputs.weight', 'qa_outputs.bias']\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": [
        "#Convertir dataset a formato tf.data.Dataset con prepare_tf_dataset():\n",
        "tf_train_set = model.prepare_tf_dataset(\n",
        "    tokenized_squad[\"train\"],\n",
        "    shuffle = True,\n",
        "    batch_size = 16,\n",
        "    collate_fn = data_collator,\n",
        ")\n",
        "\n",
        "tf_validation_set = model.prepare_tf_dataset(\n",
        "    tokenized_squad[\"test\"],\n",
        "    shuffle = False,\n",
        "    batch_size = 16,\n",
        "    collate_fn = data_collator,\n",
        ")"
      ],
      "metadata": {
        "id": "j9oHONsBLZmg"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Configura el modelo para entrenamiento con compile\n",
        "import tensorflow as tf\n",
        "model.compile(optimizer=optimizer)"
      ],
      "metadata": {
        "id": "focz_HlXMOAw"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers.keras_callbacks import PushToHubCallback\n",
        "\n",
        "callback = PushToHubCallback(\n",
        "    output_dir=\"my_awesome_qa_model\",\n",
        "    tokenizer=tokenizer,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xINPqPs_RgBa",
        "outputId": "a80273c5-d9d7-4d4d-bb76-ebedb2c0d9d6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:127: FutureWarning: 'Repository' (from 'huggingface_hub.repository') is deprecated and will be removed from version '1.0'. Please prefer the http-based alternatives instead. Given its large adoption in legacy code, the complete removal is only planned on next major release.\n",
            "For more details, please read https://huggingface.co/docs/huggingface_hub/concepts/git_vs_http.\n",
            "  warnings.warn(warning_message, FutureWarning)\n",
            "/content/my_awesome_qa_model is already a clone of https://huggingface.co/0xJCarlos/my_awesome_qa_model. Make sure you pull the latest changes with `repo.git_pull()`.\n",
            "WARNING:huggingface_hub.repository:/content/my_awesome_qa_model is already a clone of https://huggingface.co/0xJCarlos/my_awesome_qa_model. Make sure you pull the latest changes with `repo.git_pull()`.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=[callback])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J0BqXLSJMxsL",
        "outputId": "62098181-9fa1-4172-fc86-86572ad7c1ba"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "250/250 [==============================] - 231s 843ms/step - loss: 3.5919 - val_loss: 2.3177\n",
            "Epoch 2/3\n",
            "250/250 [==============================] - 218s 873ms/step - loss: 1.9745 - val_loss: 1.8597\n",
            "Epoch 3/3\n",
            "250/250 [==============================] - 209s 837ms/step - loss: 1.6940 - val_loss: 1.8597\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7cf0f1069330>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "question = \"What is La Michoacana?\"\n",
        "context = \"La Michoacana is a group of different Mexican ice cream parlors, with an estimated 8 to 15 thousand locations in Mexico.\""
      ],
      "metadata": {
        "id": "0wbOITuoNAVt"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "question_answerer = pipeline(\"question-answering\", model=\"my_awesome_qa_model\")\n",
        "\n",
        "question_answerer(question=question, context=context)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KsZkDPHcRPtk",
        "outputId": "3fdb5d47-e5e2-4232-8e5e-c9812fbdb7f0"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at my_awesome_qa_model were not used when initializing TFDistilBertForQuestionAnswering: ['dropout_19']\n",
            "- This IS expected if you are initializing TFDistilBertForQuestionAnswering 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 TFDistilBertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFDistilBertForQuestionAnswering were not initialized from the model checkpoint at my_awesome_qa_model and are newly initialized: ['dropout_119']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'score': 0.08526315540075302,\n",
              " 'start': 38,\n",
              " 'end': 63,\n",
              " 'answer': 'Mexican ice cream parlors'}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CP6-uBjORXs7"
      },
      "execution_count": 26,
      "outputs": []
    }
  ]
}