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--- |
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license: apache-2.0 |
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datasets: |
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- imdb |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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--- |
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# LSTM Text Classification Model for Sentiment Analysis |
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This repository contains a Long Short-Term Memory (LSTM) text classification model trained on the IMDB dataset for sentiment analysis. The model has achieved an accuracy of 96% on the test dataset and is available for use as a TensorFlow model. |
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## Model Details |
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- **Architecture**: Long Short-Term Memory (LSTM) Neural Network |
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- **Dataset**: IMDB Movie Reviews (Sentiment Classification) |
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- **Accuracy**: 96% |
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## Usage |
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You can use this model for sentiment analysis tasks. Below are some code snippets to help you get started: |
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```python |
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# Load the model and perform inference |
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import tensorflow as tf |
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model = tf.keras.models.load_model('imdb_lstm_model.h5') |
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# Perform inference |
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prediction = model.predict([text]) |
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# Get the predicted sentiment (e.g., 'Positive' or 'Negative') |
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predicted_sentiment = "Positive" if prediction > 0.5 else "Negative" |
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