Text Classification
Transformers
Safetensors
English
roberta
sentiment-analysis
custom-dataset
text-embeddings-inference
Instructions to use raghavv2710/sentiment-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raghavv2710/sentiment-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="raghavv2710/sentiment-roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("raghavv2710/sentiment-roberta-base") model = AutoModelForSequenceClassification.from_pretrained("raghavv2710/sentiment-roberta-base") - Notebooks
- Google Colab
- Kaggle
π 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.
β Use this model to build sentiment-aware applications, feedback classifiers, social media monitoring tools, or LLM content filters.
π§ 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 | π€ Transformers |
| Model Size | ~125M parameters |
π Evaluation (on 20% held-out test set)
| Metric | Score |
|---|---|
| Accuracy | 91% |
| F1 Score | 90% |
| Precision | 92% |
| Recall | 89% |
βοΈ Quick Start
π‘ Install Required Libraries
pip install transformers torch
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