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--- |
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license: mit |
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datasets: |
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- databoyface/python-tf-ome-src-v4.1 |
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language: |
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- en |
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--- |
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# Orthogonal Model of Emotions |
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A Text Classifier created using TensorFlow and Keras |
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## Author |
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C.J. Pitchford |
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## Published |
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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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import numpy as np |
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import tensorflow as tf |
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import tensorflow.keras.preprocessing.text as text |
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import pickle |
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from tensorflow.keras.preprocessing.sequence import pad_sequences |
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# 1. Load pre-trained model |
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model = tf.keras.models.load_model('OME4tf/ome-4a-model.h5') |
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# 2. Load tokenizer and label encoder |
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with open('OME4tf/ome-4a-tokenizer.pkl', 'rb') as f: |
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tokenizer = pickle.load(f) |
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with open('OME4tf/ome-4a-label_encoder.pkl', 'rb') as f: |
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label_encoder = pickle.load(f) |
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# 3. Test model with prediction on text "I failed to hide my distress." |
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text = "I failed to hide my distress." |
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text_seq = tokenizer.texts_to_sequences([text]) |
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max_len = 1000 |
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text_seq = pad_sequences(text_seq, maxlen=max_len, padding='post') |
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pred_probs = model.predict(text_seq) |
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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. |
