tr.py

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)