metadata
license: apache-2.0
language:
- en
metrics:
- accuracy
- f1
- precision
- recall
base_model: FacebookAI/roberta-base
pipeline_tag: text-classification
library_name: transformers
tags:
- roberta
- sentiment-analysis
- transformers
- text-classification
- custom-dataset
eval_results:
eval_accuracy: 0.91
eval_f1: 0.9
eval_precision: 0.92
eval_recall: 0.89
🚀 Sentiment-RoBERTa-Base
A fine-tuned RoBERTa-base model for binary sentiment classification (positive/negative).
Trained on a custom dataset across multiple sources including tweets, social comments, and headlines to handle real-world tone detection.
✅ Use this model to build sentiment-aware applications, feedback classifiers, social media monitoring tools, or LLM content filters.
🧠 Model Details
Property | Value |
---|---|
Base Model | roberta-base |
Fine-tuned Tasks | Binary Sentiment Analysis |
Classes | 0 = Negative , 1 = Positive |
Language | English (en ) |
Dataset | Custom multi-source |
Framework | 🤗 Transformers |
Model Size | ~125M parameters |
📊 Evaluation (on 20% held-out test set)
Metric | Score |
---|---|
Accuracy | 91% |
F1 Score | 90% |
Precision | 92% |
Recall | 89% |
⚙️ Quick Start
💡 Install Required Libraries
pip install transformers torch