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@@ -18,6 +18,31 @@ C.J. Pitchford
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  18 August 2025
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  ## Usage
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@@ -47,3 +72,6 @@ C.J. Pitchford
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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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+
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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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+
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+ bidirectional (Bidirection (None, 1000, 128) 66048
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+ al)
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+
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+ global_max_pooling1d (Glob (None, 128) 0
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+ alMaxPooling1D)
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+
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+ dense (Dense) (None, 64) 8256
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+
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+ dropout (Dropout) (None, 64) 0
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+
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+ dense_1 (Dense) (None, 47) 3055
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+
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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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+
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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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+
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+ Tokenizer and label encoder included as JSON to avoid using `pickle` files.