BERT_Tweet_Sentiment_50k_2eps Open-Source Model - Free and Accurate Sentiment Classification for Twitter Texts

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BERT Tweet Sentiment 50k 2eps

Developed by joe5campbell

A sentiment analysis model fine-tuned on BERT-base-uncased, specifically designed for Twitter text classification

Text Classification

Transformers

Open Source License:Apache-2.0 #Twitter Sentiment Analysis #BERT Fine-tuning #English Text Processing

Downloads 14

Release Time : 3/2/2022

Model Overview

This model is a sentiment analysis model obtained by fine-tuning the bert-base-uncased pre-trained model with 50k Twitter samples over 2 epochs. It can be used to determine the sentiment tendency of Twitter text.

Model Features

Efficient Fine-tuning

Achieves 82.29% accuracy on the validation set with just 2 epochs of training

Lightweight Deployment

Based on BERT-base architecture, suitable for deployment in resource-limited scenarios

Twitter Text Optimization

Specially optimized for short Twitter text

Model Capabilities

Text Sentiment Classification

Short Text Analysis

English Text Processing

Use Cases

Social Media Analysis

Twitter Sentiment Monitoring

Real-time analysis of Twitter users' sentiment tendencies towards specific topics

82.29% accuracy

Brand Reputation Management

Monitor users' emotional feedback on brands

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
0.3420	0.8511	0.4293	0.8299	0
0.1131	0.9596	0.6972	0.8229	1

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BERT Tweet Sentiment 50k 2eps

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 BERT_Tweet_Sentiment_50k_2eps

🚀 Quick Start

📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

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📄 License