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---
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: 0.90
eval_precision: 0.92
eval_recall: 0.89
---
# 🚀 Sentiment-RoBERTa-Base
A fine-tuned [RoBERTa-base](https://huggingface.co/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
```bash
pip install transformers torch |