distilbert-base-uncased-finetuned-rte Open Source Text Classification Model - Efficiently Complete Text Classification Tasks

Distilbert Base Uncased Finetuned Rte

Developed by anirudh21

This model is a text classification model fine-tuned on the GLUE dataset's RTE task based on DistilBERT, with an accuracy of 61.73%

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Release Time : 3/2/2022

Model Overview

A lightweight text classification model specifically designed for recognizing textual entailment relationships (RTE task)

Lightweight and Efficient

Based on the DistilBERT architecture, it is 40% smaller than standard BERT while retaining 97% of its performance

Task-Specific Optimization

Specially fine-tuned for the RTE (Textual Entailment Recognition) task in the GLUE dataset

Text Classification

Textual Entailment Recognition

Natural Language Inference

Natural Language Processing

Textual Entailment Judgment

Determine whether the premise text entails the hypothesis text

Achieved 61.73% accuracy on the GLUE RTE task

Question Answering System Support

Assist question-answering systems in determining whether an answer is relevant to a question

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	156	0.6921	0.5162
No log	2.0	312	0.6661	0.6173
No log	3.0	468	0.7794	0.5632
0.5903	4.0	624	0.8832	0.5921
0.5903	5.0	780	0.9376	0.5921

Property	Details
Model Type	distilbert-base-uncased-finetuned-rte
Training Data	glue
Metrics	accuracy

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