transformers safetensors roberta text-classification en dataset:stanfordnlp/sst2 autotrain_compatible endpoints_compatible region:us
---
datasets:
- stanfordnlp/sst2
language:
- en
metrics:
- accuracy: 0.91789
---


# Fine-Tuned RoBERTa Model for Sentiment Analysis

## Overview

This is a fine-tuned [RoBERTa](https://huggingface.co/docs/transformers/model_doc/robertal) model for sentiment analysis, trained on the [SST-2 dataset](https://huggingface.co/datasets/stanfordnlp/sst2). It classifies text into two sentiment categories:
- **0**: Negative
- **1**: Positive

The model achieves an accuracy of **91.789%** on the SST-2 test set, making it a robust choice for sentiment classification tasks.

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## Model Details

- **Model architecture**: RoBERTa
- **Dataset**: `stanfordnlp/sst2`
- **Language**: English
- **Model size**: 125 million parameters
- **Precision**: FP32
- **File format**: [SafeTensor](https://github.com/huggingface/safetensors)
- **Tags**: Text Classification, Transformers, SafeTensors, SST-2, English, RoBERTa, Inference Endpoints

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## Usage

### Installation

Ensure you have the necessary libraries installed:

```bash
pip install transformers torch safetensors
```

### Loading the Model

The model can be loaded from Hugging Face's `transformers` library as follows:

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load the tokenizer and model
model_name = "syedkhalid076/RoBERTa-Sentimental-Analysis-v1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example text
text = "This is an amazing product!"

# Tokenize input
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

# Perform inference
outputs = model(**inputs)
logits = outputs.logits
predicted_class = logits.argmax().item()

# Map the prediction to sentiment
sentiments = {0: "Negative", 1: "Positive"}
print(f"Sentiment: {sentiments[predicted_class]}")
```

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## Performance

### Dataset

The model was trained and evaluated on the **SST-2** dataset, which is widely used for sentiment analysis tasks.

### Metrics

| Metric   | Value    |
|----------|----------|
| Accuracy | 91.789%  |

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## Deployment

The model is hosted on Hugging Face and can be used directly via their [Inference Endpoints](https://huggingface.co/inference-endpoints).

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## Applications

This model can be used in a variety of applications, such as:
- Customer feedback analysis
- Social media sentiment monitoring
- Product review classification
- Opinion mining for research purposes

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## Limitations

While the model performs well on the SST-2 dataset, consider these limitations:
1. It may not generalize well to domains with language or sentiment nuances different from the training data.
2. It supports only binary sentiment classification (positive/negative).

For fine-tuning on custom datasets or additional labels, refer to the [Hugging Face documentation](https://huggingface.co/docs/transformers/training).

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## Model Card

| **Feature**        | **Details**                                                                 |
|---------------------|-----------------------------------------------------------------------------|
| **Language**        | English                                                                    |
| **Model size**      | 125M parameters                                                           |
| **File format**     | SafeTensor                                                                 |
| **Precision**       | FP32                                                                       |
| **Dataset**         | stanfordnlp/sst2                                                          |
| **Accuracy**         | 91.789%                                                         |

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## Contributing

Contributions to improve the model or extend its capabilities are welcome. Fork this repository, make your changes, and submit a pull request.

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## Acknowledgments

- The [Hugging Face Transformers library](https://github.com/huggingface/transformers) for model implementation and fine-tuning utilities.
- The [Stanford Sentiment Treebank 2 (SST-2)](https://huggingface.co/datasets/stanfordnlp/sst2) dataset for providing high-quality sentiment analysis data.

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**Author**: Syed Khalid Hussain
