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README.md
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18 August 2025
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## Usage
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pred_label = np.argmax(pred_probs, axis=1)
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print(f"Statement: {text}\nPrediction: {label_encoder.classes_[pred_label][0]}")
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18 August 2025
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## Model and Weights
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Model: "sequential"
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_________________________________________________________________
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Layer (type) Output Shape Param #
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=================================================================
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embedding (Embedding) (None, 1000, 64) 6400000
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bidirectional (Bidirection (None, 1000, 128) 66048
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al)
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global_max_pooling1d (Glob (None, 128) 0
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alMaxPooling1D)
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dense (Dense) (None, 64) 8256
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dropout (Dropout) (None, 64) 0
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dense_1 (Dense) (None, 47) 3055
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=================================================================
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Total params: 6477359 (24.71 MB)
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Trainable params: 6477359 (24.71 MB)
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Non-trainable params: 0 (0.00 Byte)
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## Usage
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pred_label = np.argmax(pred_probs, axis=1)
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print(f"Statement: {text}\nPrediction: {label_encoder.classes_[pred_label][0]}")
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## Additional
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Tokenizer and label encoder included as JSON to avoid using `pickle` files.
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