Create README.md

README.md

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+---
+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}")