The “Dont Stop Pretraining” code associated with the ACL 2020 paper provides powerful tools for adapting language models to various domains and tasks. This blog will guide you step-by-step through the installation, usage, and troubleshooting of this essential toolkit. Let’s dive in!

Installation

To get started with the “Dont Stop Pretraining” code, you need to set up your environment. Follow these steps:

• First, create a new conda environment using the provided configuration file:

bash
conda env create -f environment.yml

• Next, activate your new environment:

bash
conda activate domains

Using the Latest AllenNLP Version

This repository is optimized for reproducibility with a specific version of AllenNLP and requires compatibility with the pytorch-transformers==1.2.0 package. If you want to use the latest version, check out the latest-allennlp branch. But be cautious, as results may vary:

• To stay on the reliable version, continue reading below.
• If you want the latest experience with AllenNLP, make sure to transition to the latest-allennlp branch cautiously.

Pretrained Models Overview

There are several pretrained models available for DAPT and TAPT tasks. Here’s a summary:

• DAPT Models Include:
  • allenaics_roberta_base
  • allenaibiomed_roberta_base
  • allenaireviews_roberta_base
  • allenainews_roberta_base
• TAPT Models Include:
  • allenaidsp_roberta_base_dapt_news_tapt_ag_115K
  • allenaidsp_roberta_base_tapt_ag_115K
  • allenaidsp_roberta_base_dapt_reviews_tapt_imdb_20000
  • …and many more!

Downloading Pretrained Models

To download a pretrained model, utilize the following command format:

For instance, if you want to download a specific DAPT model:

bash
python -m scripts.download_model \
  --model allenaidsp_roberta_base_dapt_cs_tapt_citation_intent_1688 \
  --serialization_dir $(pwd)/pretrained_models/dsp_roberta_base_dapt_cs_tapt_citation_intent_1688

Training Your Model

To train a RoBERTa classifier on the Citation Intent corpus, use the command below:

bash
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

Hyperparameter Search

For optimizations, install Allentune from GitHub, adjust search_space/classifier.jsonnet, and run:

bash
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

Troubleshooting

If you encounter issues during installation or execution, consider the following:

• Ensure that your conda environment is correctly activated.
• Check package versions to ensure compatibility.
• Review command syntax for errors.
• For model downloading issues, verify your internet connection and the Hugging Face repository links.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.