metadata
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]}")