BERT_Tweet_Sentiment_10k Open-Source Tweet Sentiment Analysis Model - Free to Use, with an Evaluation Accuracy of 80.73%

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BERT Tweet Sentiment 10k

Developed by joe5campbell

A BERT-base-uncased fine-tuned tweet sentiment analysis model achieving 80.73% accuracy on the evaluation set

Text Classification

Transformers

Open Source License:Apache-2.0 #Twitter Sentiment Analysis #BERT Fine-tuning #High Accuracy

Downloads 14

Release Time : 3/2/2022

Model Overview

This model is a text classification model fine-tuned based on the BERT-base-uncased architecture, specifically designed for analyzing the sentiment tendencies of tweets.

Model Features

High Accuracy

Achieves 80.73% accuracy on the validation set

BERT Fine-tuning

Utilizes BERT's powerful language understanding capabilities for sentiment analysis

Lightweight

Based on BERT-base rather than the larger BERT-large version

Model Capabilities

Tweet Sentiment Analysis

Text Classification

Use Cases

Social Media Analysis

Tweet Sentiment Monitoring

Analyze the sentiment tendencies of user tweets

80.73% accuracy

Brand Reputation Management

Monitor brand evaluations on social media

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
0.3891	0.8273	0.4749	0.8073	0

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BERT Tweet Sentiment 10k

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 BERT_Tweet_Sentiment_10k

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📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

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📄 License