RerrFact

This repository contains the code for: RerrFact model for SciVer shared task.

Setup for Inference

1. Download SciFact database

Download the SciFact database from here.

2. Installing requirements

Install the requirements using the following command for abstract retrieval and rationale selection module.

pip install -r abstract,rationale_requirements.txt

Install the requirements using the following command for label prediction module.

pip install -r label_requirements.txt 

3. Download trained models

Download the trained models using this link.

4. Using pre-trained models

Abstract Retrieval

python ./inference/abstract-retrieval.py \
--corpus ./data/corpus.jsonl \
--dataset ./data/claims_test.jsonl \
--model ./saved_models/abstract_retrieval_model_here \
--output ./prediction/abstract_retrieval_test_predictions.jsonl

Rationale Selection

python ./inference/rationale-selection.py \
--corpus ./data/corpus.jsonl \
--dataset ./data/claims_test.jsonl \
--abstract ./prediction/abstract_retrieval_test_predictions.jsonl \
--model ./saved_models/rationale_selection_model_here \
--output ./prediction/

Label Prediction

python inference/label-prediction.py \
--corpus '/data/corpus.jsonl' \
--dataset './data/claims_test.jsonl' \
--rationale-selection './prediction/rationale_selection.jsonl' \
--model_n './saved_models/neutral_classifer_here' \
--model_s './saved_models/support_classifier_here' \
--output './prediction/label_pred_test.jsonl'

Retrain models

Refer to training/Abstract-retrieval.ipynb for training abstract retrieval module.

Refer to training/Rationale-selection.ipynb for training rationale selection module.

Refer to training/Label-prediction.ipynb for training label prediction module.