ROBERTA_Tweet_Sentiment_50_2eps Open-source Model - Accurately Analyze the Sentiment Tendency of Tweets

ROBERTA Tweet Sentiment 50 2eps

Developed by joe5campbell

A fine-tuned version of the Twitter RoBERTa base sentiment model for tweet sentiment analysis

Text Classification

Transformers

#Twitter Sentiment Analysis #RoBERTa Fine-tuning #Short Text Classification

Downloads 16

Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on an unknown dataset, primarily used for tweet sentiment classification tasks

Model Features

Twitter Specialized

A sentiment analysis model specifically optimized for Twitter text

Based on RoBERTa

Uses the powerful RoBERTa architecture as the base model

Model Capabilities

Tweet sentiment classification

Text sentiment analysis

Use Cases

Social Media Analysis

Tweet Sentiment Monitoring

Analyze sentiment trends on Twitter regarding specific topics

🚀 ROBERTA_Tweet_Sentiment_50_2eps

This model is a fine - tuned version of [cardiffnlp/twitter - roberta - base - sentiment](https://huggingface.co/cardiffnlp/twitter - roberta - base - sentiment) on an unknown dataset. It can evaluate sentiment based on tweet data, providing useful insights for social media analysis and opinion mining.

🚀 Quick Start

This model is a fine - tuned version of [cardiffnlp/twitter - roberta - base - sentiment](https://huggingface.co/cardiffnlp/twitter - roberta - base - sentiment) on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 0.6625
Train Accuracy: 0.6310
Validation Loss: 0.8607
Validation Accuracy: 0.25
Epoch: 1

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning_rate': 3e - 05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e - 08, 'amsgrad': False}
training_precision: float32