Create tr.py

tr.py

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+import tensorflow as tf
+from tensorflow.keras.layers import Dense, Embedding, GlobalAveragePooling1D
+from tensorflow.keras.models import Sequential
+from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, pipeline
+
+# Sample data for sentiment analysis
+texts = ["I love deep learning!", "I hate Mondays.", "This movie is fantastic.", "The weather is terrible."]
+
+labels = [1, 0, 1, 0]  # 1 for positive sentiment, 0 for negative sentiment
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+model = TFAutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
+
+# Tokenize the texts
+inputs = tokenizer(texts, padding=True, truncation=True, return_tensors='tf')
+
+# Compile the model
+model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
+
+# Train the model
+model.fit(inputs, labels, epochs=3, batch_size=2)
+
+# Save the model to Hugging Face Model Hub
+model.save_pretrained("./my-text-classifier")
+
+# Load the saved model from disk
+loaded_model = TFAutoModelForSequenceClassification.from_pretrained("./my-text-classifier")
+
+# Use the loaded model for prediction
+classifier = pipeline('text-classification', model=loaded_model, tokenizer=tokenizer)
+result = classifier("I'm feeling great!")
+print(result)