ADAPET

This repository contains the official code for the paper: "Improving and Simplifying Pattern Exploiting Training".

The model improves and simplifies PET with a decoupled label objective and label-conditioned MLM objective.

Model

Decoupled Label Loss Label Conditioned Masked Language Modelling

Updates

[November 2021] You can run ADAPET on your own dataset now! See instructions here

Setup

Setup environment by running source bin/init.sh. This will

Download the FewGLUE and SuperGLUE datasets in data/fewglue/{task} and data/superglue/{task} respectively.
Install and setup environment with correct dependencies.

Training

First, create a config JSON file with the necessary hyperparameters. For reference, please see config/BoolQ.json.

Then, to train the model, run the following commands:

sh bin/setup.sh
sh bin/train.sh {config_file}

The output will be in the experiment directory exp_out/fewglue/{task_name}/albert-xxlarge-v2/{timestamp}/. Once the model has been trained, the following files can be found in the directory:

exp_out/fewglue/{task_name}/albert-xxlarge-v2/{timestamp}/
    |
    |__ best_model.pt
    |__ dev_scores.json
    |__ config.json
    |__ dev_logits.npy
    |__ src

To aid reproducibility, we provide the JSON files to replicate the paper's results at config/{task_name}.json.

Evaluation

To evaluate the model on the SuperGLUE dev set, run the following command:

sh bin/dev.sh exp_out/fewglue/{task_name}/albert-xxlarge-v2/{timestamp}/

The dev scores can be found in exp_out/fewglue/{task_name}/albert-xxlarge-v2/{timestamp}/dev_scores.json.

To evaluate the model on the SuperGLUE test set, run the following command.

sh bin/test.sh exp_out/fewglue/{task_name}/albert-xxlarge-v2/{timestamp}/

The generated predictions can be found in exp_out/fewglue/{task_name}/albert-xxlarge-v2/{timestamp}/test.json.

Train your own ADAPET

Setup your dataset in the data folder as

data/{dataset_name}/
    |
    |__ train.jsonl
    |__ val.jsonl
    |__ test.jsonl

Each jsonl file consists of lines of dictionaries. Each dictionaries should have the following format:

{
    "TEXT1": (insert text), 
    "TEXT2": (insert text), 
    "TEXT3": (insert text), 
    ..., 
    "TEXTN": (insert text), 
    "LBL": (insert label)
}

Run the experiment

python cli.py --data_dir data/{dataset_name} \
              --pattern '(INSERT PATTERN)' \
              --dict_verbalizer '{"lbl_1": "verbalizer_1", "lbl_2": "verbalizer_2"}'

Here, INSERT PATTERN consists of [TEXT1], [TEXT2], [TEXT3], ..., [LBL]. For example, if the new dataset had two text inputs and one label, a sample pattern would be [TEXT1] and [TEXT2] imply [LBL].

Fine-tuned Models

Our fine-tuned models can be found in this link.

To evaluate these fine-tuned models for different tasks, run the following command:

python src/run_pretrained.py -m {finetuned_model_dir}/{task_name} -c config/{task_name}.json -k pattern={best_pattern_for_task}

The scores can be found in exp_out/fewglue/{task_name}/albert-xxlarge-v2/{timestamp}/dev_scores.json. Note: The best_pattern_for_task can be found in Table 4 of the paper.

Contact

For any doubts or questions regarding the work, please contact Derek (dtredsox@cs.unc.edu) or Rakesh (rrmenon@cs.unc.edu). For any bug or issues with the code, feel free to open a GitHub issue or pull request.

Citation

Please cite us if ADAPET is useful in your work:

@inproceedings{tam2021improving,
          title={Improving and Simplifying Pattern Exploiting Training},
          author={Tam, Derek and Menon, Rakesh R and Bansal, Mohit and Srivastava, Shashank and Raffel, Colin},
          journal={Empirical Methods in Natural Language Processing (EMNLP)},
          year={2021}
}

rrmenon10/ADAPET