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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-classification |
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--- |
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## Model Architecture |
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- **Embedding Layer**: Converts input text into dense vectors. |
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- **CNN Layers**: Extracts features from text sequences. |
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- **RNN, LSTM, and GRU Layers**: Capture temporal dependencies in text. |
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- **Dense Layers**: Classify text into sentiment categories. |
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## Usage |
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You can use this model for sentiment analysis on text data. Here's a sample code to load and use the model: |
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```python |
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from tensorflow.keras.models import load_model |
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import pickle |
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import numpy as np |
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from tensorflow.keras.preprocessing.sequence import pad_sequences |
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# Load the model |
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model = load_model('path_to_model/hybrid_model.h5') |
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# Load the tokenizer |
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with open('path_to_tokenizer/tokenizer.pkl', 'rb') as f: |
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tokenizer = pickle.load(f) |
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# Predict sentiment |
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def predict_sentiment(text): |
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text = text.lower() |
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text = re.sub(r'[^\w\s]', '', text) |
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sequence = tokenizer.texts_to_sequences([text]) |
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padded_sequence = pad_sequences(sequence, maxlen=100) |
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pred = model.predict(padded_sequence) |
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sentiment = np.argmax(pred) |
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return sentiment |
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# Example usage |
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text = "I love this product!" |
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print(predict_sentiment(text)) |
