Goldilocks-Reproduce-Repository

This is the repo for the ECIR 2024 Reproducibility track Paper A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TAR

Datasets & Data Processing

How to get the datasets?

• RCV1-v2

  Please request the original dataset from https://trec.nist.gov/data/reuters/reuters.html, and check the necessary files provided under ./data-processing/rcv1-v2

• Jeb-Bush

  Please e-mail the author found at https://web.archive.org/web/20160220052206/http://plg.uwaterloo.ca/~gvcormac/total-recall/, and check the necessary files provided under ./data-processing/jeb-bush

• CLEF-TAR

  Please check the original data at the official CLEF-TAR repo https://github.com/CLEF-TAR/tar. We provide the clean version of title & abstract of all docs in all_clean.jsonl, and the processed clef collections used in the experiments can be downloaded from clef_for_goldilocks, the data processing process can be found under ./data-processing/clef.

Large files under ./data-processing/rcv1-v2 and ./data-processing/jeb-bush can be downloaded from rcv1_path.txt, id.txt, and athome1.md5.

We also provide the category information table of these three datasets used in this reproducibility paper.

Hyperparameters

The optimizer and loss function are the same as in the original paper.

• For BERT, we use ADAM as the optimizer with no weight decay and no warm-up period, and a learning rate of 5 * 10^-5 for further pre-training with mask-language modelling. The language model pre-training ranges from not pre-training at all on the target collection, to performing ten iterations over the collection. For classification fine-tuning, we use the ADAM optimizer with a linear weight decay of 0.01 with 50 warm-up steps and an initial learning rate of 0.001.
• For logistic regression, we use L2 regularization on the losses and fit to convergence with default settings in the active learning pipeline.
• In order to better fit our GPUs, we increased the training and evaluation batch sizes for BERT to 100 and 1000 respectively -- examining the original author's code, we could understand they instead used batch sizes of 26 and 600. Furthermore, we found that using mixed precision training (fp16) could largely reduce the training time.

TAR with BERT

We adapted the codes from the original authors to our own experiment environment, please check the comments inside each file if necessary.

• Environment

  For our setting: 3 * A100, we use python=3.8, cuda=11.7, run

  conda env create -f env.yml

  and install the dev version of libact for active learning as below.

  To install the env provided by the original authors, run

  conda create -n huggingface python=3.7
  conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
  pip install transformers
  conda install numpy scipy pandas scikit-learn nltk tqdm Cython joblib
  LIBACT_BUILD_HINTSVM=0 LIBACT_BUILD_VARIANCE_REDUCTION=0 pip install -e ~/repositories/libact

  N.B. The active learning library libact used is the dev version from the original authors https://github.com/eugene-yang/libact/tree/dev

• Tokenization

  • For reproducibility on RCV1-v2 and Jeb-Bush, run

    python3 bert.tokenize.py rcv1

  and change the option for both rcv1 and jb.

  • For generalizability on CLEF collections, run

    python3 clef.bert.tokenize.py 2017/test

    • With BioLinkBert-base, run

    python3 clef.biolinkbert.tokenize.py 2017/test

    and the option for CLEF collections ranges from 2017/test to 2019/intervention/train

• Further pre-training with mlm-finetuning

  • For reproducibility on RCV1-v2 and Jeb-Bush, run

    python3 mlm-finetuning.py

  and check the comments inside for both rcv1 and jb.

  • For generalizability on CLEF collections, run

    python3 clef-mlm-finetuning.py 2017/test

    • With BioLinkBert-base, check the comments inside clef-mlm-finetuning.py.

    and the option for CLEF collections ranges from 2017/test to 2019/intervention/train.

• Reproduce goldilocks-tar

  • For reproducibility on RCV1-v2 and Jeb-Bush, run

    python3 al_exp.py --category 434 \
        --cached_dataset ./cache_new/jb50sub_org_bert-base.512.pkl.gz \
        --dataset_path  ./jb_info/ \
        --model_path ./mlm-finetune/bert-base_jb50sub_ep10 \
        --output_path  ./results/jb/ep10/ \

    and change the options for rcv1 and jb with corresponding categories, ep refers to the further pre-training epochs from the previous stage.

  • For generalizability on CLEF collections, run

    python3 clef17_al_exp.py --topic CD012019 \
        --cached_dataset ./cache_new/clef/clef2017_test_CD012019_org_bert-base.512.pkl.gz \
        --dataset_path  ./clef_info/2017/test/CD012019 \
        --output_path  ./results/clef/ep2/clef17_test/ \
        --batch_size 25 \
        --model_path ./mlm-finetune/clef/2017/bert-base_clef_2017_test_CD012019_ep2 \

    and the options according to CLEF collections range from clef17_al_exp.py to clef19_al_exp.py with corresponding topics.

    • With BioLinkBert-base, run corresponding biolink version such as

    python3 clef17_biolink_exp.py --topic CD011984 \
      --cached_dataset ./cache_new/clef_biolink/clef2017_train_CD011984_biolink_bert-base.512.pkl.gz \
      --dataset_path  ./clef_info/2017/train/CD011984 \
      --output_path  ./results/biolink/ep0/clef17_train/ \
      --batch_size 25 \
      --model_path  michiyasunaga/BioLinkBERT-base \

    and the options according to CLEF collections range from clef17_biolink_exp.py to clef19_biolink_exp.py with corresponding topics.

Baseline

• Feature engineering

  • For reproducibility on RCV1-v2 and Jeb-Bush, run

    python3 lr.feature.py rcv1

    and change the option for both rcv1 and jb.

  • For generalizability on CLEF collections, run

    python3 clef.lr.feature.py 2019/intervention/train

    and the option for CLEF collections ranges from 2017/test to 2019/intervention/train.

• Reproduce goldilocks-tar baseline with logistic regression

  • For reproducibility on RCV1-v2 and Jeb-Bush, run

    python3 al_baseline.py --category 434 \
        --cached_dataset ./jb_info/jb_sampled_features.pkl \
        --dataset_path  ./jb_info \
        --output_path  ./results/baseline/jb/

    and change the options for rcv1 and jb with corresponding categories.

  • For generalizability on CLEF collections, run

    python3 clef_al_baseline.py --dataset clef2017_train \
        --topic CD011984 \
        --batch_size 25 \
        --cached_dataset ./clef_info/lr_features/clef2017_train_CD011984_features.pkl \
        --dataset_path  ./clef_info/2017/train/CD011984 \
        --output_path  ./results/baseline/clef/clef17_train/

    and change the options for CLEF collection features ranges from clef2017_train to clef2019_intervention_train with corresponding topics.

Analysis

• R-Precision

  Please refer to analysis_RP.py under ./utils.

• Review Cost

  Please refer to analysis_cost.py and analysis_cost_bin.py under ./utils.

• Statistical significance test

  Please refer to stat_RP.py and stat_cost.py under ./utils.