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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