README.md

---
license: mit
metrics:
- accuracy
pipeline_tag: text-classification
library_name: transformers
---
# Sentiment Analysis Model

This repository contains a fine-tuned sentiment analysis model based on the DistilBERT architecture. The model is capable of classifying text as either positive or negative sentiment.

## Model code
 - https://colab.research.google.com/drive/10SLRb11P0GGA9ScQyzWEbseNfDWdWKGB?usp=sharing
## Model Information

- **Model Name**: rohansb10/sentiment_analysis_model
- **Base Model**: DistilBERT
- **Task**: Binary Sentiment Classification (Positive/Negative)
- **Training Data**: IMDB Dataset (sample of 1000 reviews)

## Installation

To use this model, you'll need to install the following dependencies:

```bash
pip install transformers torch
```

## Usage

Here's a sample code to use the sentiment analysis model:

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load the model and tokenizer from the Hugging Face Hub
model_name = "rohansb10/sentiment_analysis_model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Set device to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

# Function to predict sentiment
def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
    with torch.no_grad():
        outputs = model(**inputs)
    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(probabilities, dim=-1).item()
    return predicted_class, probabilities[0].tolist()

# Test the model with some sample texts
sample_texts = [
    "I absolutely loved the movie! It was fantastic.",
    "The product did not meet my expectations. Very disappointing.",
    "It's okay, not great but not terrible either.",
]

# Run predictions on the sample texts
for text in sample_texts:
    predicted_class, probabilities = predict_sentiment(text)
    sentiment = "positive" if predicted_class == 1 else "negative"
    print(f"Text: {text}")
    print(f"Predicted Sentiment: {sentiment}, Probabilities: {probabilities}\n")
```

## Example Output

When you run the code above, you should see output similar to this:

```
Text: I absolutely loved the movie! It was fantastic.
Predicted Sentiment: positive, Probabilities: [0.01234, 0.98766]

Text: The product did not meet my expectations. Very disappointing.
Predicted Sentiment: negative, Probabilities: [0.99876, 0.00124]

Text: It's okay, not great but not terrible either.
Predicted Sentiment: negative, Probabilities: [0.67890, 0.32110]
```

## Model Performance

The model was trained on a sample of 1000 reviews from the IMDB dataset. For detailed performance metrics, including accuracy, precision, recall, and F1-score, please refer to the model card on the Hugging Face Hub.

## Limitations

- The model was trained on a small sample of movie reviews, which may limit its generalization to other domains.
- It performs binary classification (positive/negative) and does not handle neutral sentiments explicitly.
- Performance may vary on texts that are significantly different from movie reviews.

## Contributing

Contributions to improve the model or extend its capabilities are welcome. Please feel free to open an issue or submit a pull request.

## License

Please refer to the model card on the Hugging Face Hub for licensing information.

## Contact

For any questions or feedback, please open an issue in this repository.