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.
10 Dec 26, 2022
57 Dec 25, 2022
16 Aug 12, 2022
1 Dec 01, 2021
5.9k Jan 04, 2023
2 Dec 27, 2021
2 Nov 21, 2021
647 Dec 27, 2022
5 May 28, 2021
10 Oct 10, 2022
80 Dec 20, 2022
176 Jan 06, 2023
7.1k Dec 29, 2022
8 Sep 06, 2022
2 Jul 19, 2022
98 Dec 28, 2022
43 Nov 07, 2022
1.6k Jan 06, 2023
328 Jan 09, 2023
7 Nov 15, 2021