BERT-base-uncased-finetuned-RTE Open-source Text Classification Model - Accurately Complete Text Classification Tasks

Bert Base Uncased Finetuned Rte

Developed by anirudh21

A text classification model fine-tuned on the GLUE RTE task based on the BERT base model

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Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of bert-base-uncased on the GLUE dataset, specifically designed for the Textual Entailment Recognition (RTE) task.

GLUE Dataset Fine-tuning

Specially optimized for the RTE task in the GLUE benchmark

BERT Architecture

Based on the powerful BERT-base architecture with excellent text understanding capabilities

Lightweight Fine-tuning

Achieves good performance with just 5 training epochs

Text Classification

Textual Entailment Recognition

Natural Language Understanding

Natural Language Processing

Textual Entailment Judgment

Determine whether an entailment relationship exists between two texts

Achieves 66.43% accuracy on the GLUE RTE test set

Question Answering System Support

Assists QA systems in determining whether answers are entailed in given texts

Property	Details
Model Name	bert-base-uncased-finetuned-rte
Task	Text Classification
Dataset	glue (rte)
Metric	Accuracy: 0.6642599277978339

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	63	0.6777	0.5668
No log	2.0	126	0.6723	0.6282
No log	3.0	189	0.7238	0.6318
No log	4.0	252	0.7993	0.6354
No log	5.0	315	0.8075	0.6643

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