Tweet_eval_bench Open-source Text Classification Model - Accurately Conduct Tweet Sentiment Analysis for Free Use

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Tweet Eval Bench

Developed by marcolatella

A text classification model fine-tuned on distilbert-base-uncased, trained on the tweet_eval sentiment analysis dataset

Text Classification

Transformers

Open Source License:Apache-2.0 #Tweet sentiment analysis #Lightweight BERT #High accuracy

Downloads 16

Release Time : 3/2/2022

Model Overview

This model is a text classifier optimized for tweet sentiment analysis tasks, capable of categorizing tweet content into different sentiment classes

Model Features

Efficient and lightweight

Based on the DistilBERT architecture, reducing model size while maintaining performance

Sentiment analysis

Specially optimized for sentiment classification of social media tweets

Fast inference

Distilled model architecture provides faster inference speed

Model Capabilities

Text classification

Sentiment analysis

Social media text processing

Use Cases

Social media analysis

Tweet sentiment monitoring

Analyze user sentiment tendencies in social media tweets

Accuracy reaches 71.6%

Brand sentiment analysis

Monitor user emotions in brand-related tweets

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7022	1.0	1426	0.6581	0.7105
0.5199	2.0	2852	0.6835	0.706
0.2923	3.0	4278	0.7941	0.7075
0.1366	4.0	5704	1.0761	0.7115
0.0645	5.0	7130	1.5530	0.716

Property	Details
Model Type	Fine - tuned version of distilbert - base - uncased
Training Data	tweet_eval
Metrics	accuracy
Results	Accuracy: 0.716 on tweet_eval dataset (sentiment task)

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