app.py

import torch
from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer

# Load the data
train_data = ... # load your training data here
eval_data = ... # load your evaluation data here

# Define the model architecture
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=8)

# Set up the training arguments
training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=3,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=64,
    warmup_steps=500,
    weight_decay=0.01,
    logging_dir='./logs',
    logging_first_step=True,
    logging_steps=50,
    save_total_limit=2,
    save_steps=500,
    eval_steps=500,
    learning_rate=5e-5,
    seed=42,
)

# Create the trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_data,
    eval_dataset=eval_data,
    compute_metrics=lambda pred: {'accuracy': torch.tensor(pred).argmax().item()},
)

# Train the model
trainer.train()

# Evaluate the model
loss, metrics = trainer.evaluate()
print(f'Loss: {loss}')
print(f'Metrics: {metrics}')