dont-stop-pretraining

Code associated with the Don't Stop Pretraining ACL 2020 paper

Citation

@inproceedings{dontstoppretraining2020,
 author = {Suchin Gururangan and Ana Marasović and Swabha Swayamdipta and Kyle Lo and Iz Beltagy and Doug Downey and Noah A. Smith},
 title = {Don't Stop Pretraining: Adapt Language Models to Domains and Tasks},
 year = {2020},
 booktitle = {Proceedings of ACL},
}

Installation

conda env create -f environment.yml
conda activate domains

Working with the latest allennlp version

This repository works with a pinned allennlp version for reproducibility purposes. This pinned version of allennlp relies on pytorch-transformers==1.2.0, which requires you to manually download custom transformer models on disk.

To run this code with the latest allennlp/ transformers version (and use the huggingface model repository to its full capacity) checkout the branch latest-allennlp. Caution that we haven't tested out all models on this branch, so your results may vary from what we report in paper.

If you'd like to use this pinned allennlp version, read on. Otherwise, checkout latest-allennlp.

Available Pretrained Models

We've uploaded DAPT and TAPT models to huggingface.

DAPT models

Available DAPT models:

allenai/cs_roberta_base
allenai/biomed_roberta_base
allenai/reviews_roberta_base
allenai/news_roberta_base

TAPT models

Available TAPT models:

allenai/dsp_roberta_base_dapt_news_tapt_ag_115K
allenai/dsp_roberta_base_tapt_ag_115K
allenai/dsp_roberta_base_dapt_reviews_tapt_amazon_helpfulness_115K
allenai/dsp_roberta_base_tapt_amazon_helpfulness_115K
allenai/dsp_roberta_base_dapt_biomed_tapt_chemprot_4169
allenai/dsp_roberta_base_tapt_chemprot_4169
allenai/dsp_roberta_base_dapt_cs_tapt_citation_intent_1688
allenai/dsp_roberta_base_tapt_citation_intent_1688
allenai/dsp_roberta_base_dapt_news_tapt_hyperpartisan_news_5015
allenai/dsp_roberta_base_dapt_news_tapt_hyperpartisan_news_515
allenai/dsp_roberta_base_tapt_hyperpartisan_news_5015
allenai/dsp_roberta_base_tapt_hyperpartisan_news_515
allenai/dsp_roberta_base_dapt_reviews_tapt_imdb_20000
allenai/dsp_roberta_base_dapt_reviews_tapt_imdb_70000
allenai/dsp_roberta_base_tapt_imdb_20000
allenai/dsp_roberta_base_tapt_imdb_70000
allenai/dsp_roberta_base_dapt_biomed_tapt_rct_180K
allenai/dsp_roberta_base_tapt_rct_180K
allenai/dsp_roberta_base_dapt_biomed_tapt_rct_500
allenai/dsp_roberta_base_tapt_rct_500
allenai/dsp_roberta_base_dapt_cs_tapt_sciie_3219
allenai/dsp_roberta_base_tapt_sciie_3219

The final numbers in each model above are the dataset sizes. Larger dataset sizes (e.g. imdb_70000 vs. imdb_20000) are curated TAPT models. These only exist for imdb, rct, and hyperpartisan_news.

Downloading Pretrained models

You can download a pretrained model using the scripts/download_model.py script.

Just supply a model type and serialization directory, like so:

python -m scripts.download_model \
        --model allenai/dsp_roberta_base_dapt_cs_tapt_citation_intent_1688 \
        --serialization_dir $(pwd)/pretrained_models/dsp_roberta_base_dapt_cs_tapt_citation_intent_1688

This will output the allenai/dsp_roberta_base_dapt_cs_tapt_citation_intent_1688 model for Citation Intent corpus in $(pwd)/pretrained_models/dsp_roberta_base_dapt_cs_tapt_citation_intent_1688

Downloading data

All task data is available on a public S3 url; check environments/datasets.py.

If you run the scripts/train.py command (see next step), we will automatically download the relevant dataset(s) using the URLs in environments/datasets.py. However, if you'd like to download the data for use outside of this repository, you will have to curl each dataset individually:

curl -Lo train.jsonl https://allennlp.s3-us-west-2.amazonaws.com/dont_stop_pretraining/data/chemprot/train.jsonl
curl -Lo dev.jsonl https://allennlp.s3-us-west-2.amazonaws.com/dont_stop_pretraining/data/chemprot/dev.jsonl
curl -Lo test.jsonl https://allennlp.s3-us-west-2.amazonaws.com/dont_stop_pretraining/data/chemprot/test.jsonl

Example commands

Run basic RoBERTa model

The following command will train a RoBERTa classifier on the Citation Intent corpus. Check environments/datasets.py for other datasets you can pass to the --dataset flag.

python -m scripts.train \
        --config training_config/classifier.jsonnet \
        --serialization_dir model_logs/citation_intent_base \
        --hyperparameters ROBERTA_CLASSIFIER_SMALL \
        --dataset citation_intent \
        --model roberta-base \
        --device 0 \
        --perf +f1 \
        --evaluate_on_test

You can supply other downloaded models to this script, by providing a path to the model:

python -m scripts.train \
        --config training_config/classifier.jsonnet \
        --serialization_dir model_logs/citation-intent-dapt-dapt \
        --hyperparameters ROBERTA_CLASSIFIER_SMALL \
        --dataset citation_intent \
        --model $(pwd)/pretrained_models/dsp_roberta_base_dapt_cs_tapt_citation_intent_1688 \
        --device 0 \
        --perf +f1 \
        --evaluate_on_test

Perform hyperparameter search

First, install allentune: https://github.com/allenai/allentune

Modify search_space/classifier.jsonnet accordingly.

Then run:

allentune search \
            --experiment-name ag_search \
            --num-cpus 56 \
            --num-gpus 4 \
            --search-space search_space/classifier.jsonnet \
            --num-samples 100 \
            --base-config training_config/classifier.jsonnet  \
            --include-package dont_stop_pretraining

Modify --num-gpus and --num-samples accordingly.