README.md

---
license: apache-2.0
language:
- en
pipeline_tag: text-classification
tags:
- Sentiment Analysis
- Language Models
---
## Model Architecture
- **Embedding Layer**: Converts input text into dense vectors.
- **CNN Layers**: Extracts features from text sequences.
- **RNN, LSTM, and GRU Layers**: Capture temporal dependencies in text.
- **Dense Layers**: Classify text into sentiment categories.

## Usage
You can use this model for sentiment analysis on text data. Here's a sample code to load and use the model:

```python
from huggingface_hub import from_pretrained_keras
import re
import numpy as np
from tensorflow.keras.preprocessing.sequence import pad_sequences

# Load model
model = from_pretrained_keras("Ravinthiran/DistilSenti-Net42M")

# Example prediction function
def predict_sentiment(text, model, tokenizer, label_encoder):
    text = text.lower()
    text = re.sub(r'[^\w\s]', '', text)
    sequence = tokenizer.texts_to_sequences([text])
    padded_sequence = pad_sequences(sequence, maxlen=100)
    pred = model.predict(padded_sequence)
    sentiment = label_encoder.inverse_transform(pred.argmax(axis=1))
    sentiment_score = pred[0]
    return sentiment[0], sentiment_score

# Example usage
new_text = "I recently started a new fitness program at a local wellness center, and it has been an incredibly positive experience."
predicted_sentiment, sentiment_score = predict_sentiment(new_text, model, tokenizer, label_encoder)

print(f"Predicted Sentiment: {predicted_sentiment}")
print(f"Sentiment Scores: {sentiment_score}")