Verbalizer benchmarking for text classification

This repository contains the code for benchmarking verbalizer baselines for text classification problems, published at LREC-COLING 2024, the Joint International Conference on Computational Linguistics, Language Resources and Evaluation. 20-25 MAY, 2024 / TORINO, ITALIA.

📑 Contents

💻 Requirments

🚆 Training

📋 Evaluation

🛠️ Customize

🔖 Citation

💻 Requirements

All requirements can be found in requirements.txt. You can install required packages with pip install -r requirements.txt

🚆 Training

To train a model in few-learning with verbalizers, simply run the following commands:

python3 main.py \\
$MODEL_TYPE \\
$MODEL_PATH \\
$DATASET \\
--experiment_name $EXPERIMENT_NAME\\
--seed $SEED \\
--do_train \\
--do_test \\
--do_zeroshot \\
--train_size $TRAIN_SIZE \\
--train_to_valid $TRAIN_TO_VALID \\
--split $SPLIT_0 $SPLIT_1 \\
--template_id $TEMPLATE_ID \\
--verbalizer_type $VERBALIZER_TYPE \\
--batchsize_train $BATCHSIZE_TRAIN \\
--batchsize_eval $BATCHSIZE_EVAL \\
--learning_rate 1e-5 $LEARNING_RATE \\
--epochs $EPOCHS \\
--device $DEVICE

where

• $MODEL_TYPE and $MODEL_PATH are the model name family (e.g. roberta) and the path of the pretrained model parsed to transformers.ModelForMaskedLM.from_pretrained (e.g. roberta-large).
• $DATASET is the dataset, see data/processor.py for more details.
• $TRAIN_SIZE is the number of train examples, $TRAIN_TO_VALID is the ratio train/validation examples. $SPLIT0 is the random state for sampling labeled examples, $SPLIT1 is the random state for spliting train and validation.
• $TEMPLATE_ID specifies the template to use, see data/processor.py for more details.
• $VERBALIZER_TYPE specifies the bverbalizer baseline, must be one of: manual, soft, auto.
• $BATCHSIZE_TRAIN, $BATCHSIZE_EVAL, $LEARNING_RATE, $EPOCHS are training parameters.
• $DEVICE is the device parsed to torch.device.

If $VERBALIZER_TYPE is auto, you can specify the number of label words per class via --num_labelword.

Termination of running the above command creates a folder with random-generated name ./outputs/$DATASET/$EXPERIMENT_NAME/XXXXXXXXXX containing:

• checkpoint/: folder resulted from transformers.Trainer.save_model.
• info.json: all information related to the run, including arguments, training history, predictions and metrics.
• logfile.log: log file.
• verbalizer.json: automatic label words if $VERBALIZER_TYPE is auto.

📋 Evaluation

To load and evaluate a pretrained model, run the following commands:

python3 evaluate.py \\
$DIR \\
--data_path $DATA_PATH

where

• $DIR is the directory created by training command above.
• $DATA_PATH is the test data path, if not identic to the test split of the training data.

By default, necessary arguments will be collected from $DIR/info.json for evaluation.

🛠️ Customize

To use your cusom template or verbalizer, create text files and parse them through --template_file or --verbalizer_file in the training command. The formatting of these files follow template and verbalizer.

🔖 Citation

@inproceedings{nguyen2024enhancing,
  title={Enhancing Few-Shot Topic Classification with Verbalizers. A Study on Automatic Verbalizer and Ensemble Methods},
  author={Nguyen, Quang Anh and Tomeh, Nadi and Lebbah, Mustapha and Charnois, Thierry and Azzag, Hanane and Munoz, Santiago Cordoba},
  booktitle={LREC-COLING 2024. Joint International Conference on Computational Linguistics, Language Resources and Evaluation},
  year={2024},
  month={5},
  day={20-25},
  address={Torino, Italia},
}