Fine-tune with hugging face trainer

[SangRyul]

Hello.

First, Thank you for your great work on the task. I could get many insights from this project.
I'm Just wondering

1. is genre-kilt model in huggingface differ from model in this repository? if so how they are different?

2. I have my custom document retrieval dataset in kilt style. How can I finetune with hugging face model? I just want to transport with trainer api in huggingface. Can you give me a guide?

3. I also tried finetuning with this script
   mine is at below

#!/bin/bash

# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

#DATASET=$1
#NAME=$2
DATASET=/userhomes/sangryul/project/contrastive-retrieval/GENRE/data_fair
BASED_MODEL=/userhomes/sangryul/project/contrastive-retrieval/GENRE/models/fairseq_wikipage_retrieval
NAME=nq_100_finetune
STEP=10000

fairseq-train $DATASET/bin/ \
    --wandb-project multiperspective \
    --no-epoch-checkpoints \
    --keep-best-checkpoints 1 \
    --save-dir /userhomes/sangryul/project/contrastive-retrieval/GENRE/models/$NAME \
    --restore-file $BASED_MODEL/model.pt \
    --arch bart_large  \
    --task translation  \
    --criterion label_smoothed_cross_entropy  \
    --source-lang source  \
    --target-lang target  \
    --truncate-source  \
    --label-smoothing 0.1  \
    --max-tokens 1024  \
    --update-freq 1  \
    --max-update $STEP  \
    --required-batch-size-multiple 1  \
    --dropout 0.1  \
    --attention-dropout 0.1  \
    --relu-dropout 0.0  \
    --weight-decay 0.01  \
    --optimizer adam  \
    --adam-betas "(0.9, 0.999)"  \
    --adam-eps 1e-08  \
    --clip-norm 0.1  \
    --lr-scheduler polynomial_decay  \
    --lr 3e-05  \
    --total-num-update $STEP  \
    --warmup-updates 500  \
    --num-workers 20  \
    --share-all-embeddings \
    --layernorm-embedding \
    --share-decoder-input-output-embed  \
    --skip-invalid-size-inputs-valid-test  \
    --log-format json  \
    --log-interval 10  \
    --patience 200  \

But I found that the training loss is decreasing while evaluating loss is increasing.
I used Natural question kilt train and dev dataset. and Is this because of overfitting?

Thank you for your effort on this project again.

Thank you very much