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

Last update: May 22, 2022

Related tags

Deep Learning RerrFact

Overview

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.

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

Related tags

Overview

RerrFact

Setup for Inference

1. Download SciFact database

2. Installing requirements

3. Download trained models

4. Using pre-trained models

Retrain models

Owner

Ashish Rana

Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)

A basic reminder tool written in Python.

Imaginaire - NVIDIA's Deep Imagination Team's PyTorch Library

CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule Routing

Public implementation of the Convolutional Motif Kernel Network (CMKN) architecture

History Aware Multimodal Transformer for Vision-and-Language Navigation

Official PyTorch implementation of "Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning" (AAAI 2021)

This repository contains code to run experiments in the paper "Signal Strength and Noise Drive Feature Preference in CNN Image Classifiers."

Alpha-Zero - Telegram Group Manager Bot Written In Python Using Pyrogram

Heart Arrhythmia Classification

DockStream: A Docking Wrapper to Enhance De Novo Molecular Design

Tutorial for the PERFECTING FACTORY 5.0 WITH EDGE-POWERED AI workshop

code for our ECCV-2020 paper: Self-supervised Video Representation Learning by Pace Prediction

Self-supervised Multi-modal Hybrid Fusion Network for Brain Tumor Segmentation

A configurable, tunable, and reproducible library for CTR prediction

PAMI stands for PAttern MIning. It constitutes several pattern mining algorithms to discover interesting patterns in transactional/temporal/spatiotemporal databases

Pytorch reimplementation of the Mixer (MLP-Mixer: An all-MLP Architecture for Vision)

PyTorch implementation for View-Guided Point Cloud Completion

This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?”

Kinetics-Data-Preprocessing