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.
30 Dec 24, 2022
1.3k Dec 19, 2022
104 Nov 29, 2022
80 Dec 01, 2022
26.6k Dec 31, 2022
2 Jan 05, 2022
3k Dec 12, 2022
4 Dec 12, 2022
97 Jan 02, 2023
116 Jan 04, 2023
77 Nov 30, 2022
141 Dec 26, 2022
0 Oct 22, 2021
24 Oct 26, 2022
5 Jul 18, 2022
80 Dec 16, 2022
32 Nov 17, 2022
261 Dec 23, 2022
4 Sep 14, 2022
122 Dec 12, 2022