README.md

---
license: mit
library_name: transformers
pipeline_tag: text-classification
---

# Sentiment Analysis Models

This repository contains multiple trained sentiment analysis models for binary sentiment classification (Positive / Negative).

## Repository Structure

```
Sentiment/
│
├── BERT/
├── Bi_LSTM/
├── Linear_Svm/
├── Logistic_Regression/
├── Lstm/
├── Naive Bayes/
├── XGBoost/
└── Notebooks/
```

---

# Available Models

- BERT
- LSTM
- Bi-LSTM
- Logistic Regression
- Linear SVM
- Naive Bayes
- XGBoost

---

# Using the BERT Model

```python
from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="Sudheer17/Sentiment/BERT"
)

result = classifier("I love this movie!")

print(result)
```

Example Output

```python
[{'label': 'Positive', 'score': 0.9848}]
```

---

# Example Inputs

```text
I love this movie!
```

```text
This is the worst experience ever.
```

```text
The service was average.
```

---

# Notes

- The BERT model can be loaded directly using the Hugging Face Transformers library.
- Classical Machine Learning and Deep Learning models are stored in their respective folders.
- Use the appropriate tokenizer and preprocessing pipeline when working with non-BERT models.

---

# Requirements

```
transformers
torch
tensorflow
scikit-learn
joblib
numpy
pandas
```

Install them using:

```bash
pip install transformers torch tensorflow scikit-learn joblib numpy pandas
```

---

# License

This repository is released under the MIT License.