Create README.md

README.md

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+---
+license: mit
+datasets:
+- databoyface/python-tf-ome-src-v4.1
+language:
+- en
+---
+
+# Orthogonal Model of Emotions
+
+A Text Classifier created using Sci-Kit Learn
+
+## Author
+
+C.J. Pitchford
+
+## Published
+
+18 June 2025
+
+## Usage
+
+
+    import numpy as np
+    import tensorflow as tf
+
+    import tensorflow.keras.preprocessing.text as text
+
+    import pickle
+    from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+    # 1. Load pre-trained model
+    model = tf.keras.models.load_model('OME4tf/ome-4a-model.h5')
+
+    # 2. Load tokenizer and label encoder
+    with open('OME4tf/ome-4a-tokenizer.pkl', 'rb') as f:
+        tokenizer = pickle.load(f)
+    with open('OME4tf/ome-4a-label_encoder.pkl', 'rb') as f:
+        label_encoder = pickle.load(f)
+
+    # 3. Test model with prediction on text "I failed to hide my distress."
+    text = "I failed to hide my distress."
+    text_seq = tokenizer.texts_to_sequences([text])
+    max_len = 1000
+    text_seq = pad_sequences(text_seq, maxlen=max_len, padding='post')
+    pred_probs = model.predict(text_seq)
+    pred_label = np.argmax(pred_probs, axis=1)
+    print(f"Statement: {text}\nPrediction: {label_encoder.classes_[pred_label][0]}")
+