app.py

import pandas as pd
import torch

from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments

# Load the pre-trained BERT model and tokenizer
model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=6)

# Load the train and test data
train_data = pd.read_csv("train.csv")
test_data = pd.read_csv("test.csv")

# Define the function to preprocess the text
def preprocess(text):
    inputs = tokenizer(text, padding=True, truncation=True, max_length=128, return_tensors="pt")
    return inputs["input_ids"], inputs["attention_mask"]

# Preprocess the train and test data
X_train = train_data["comment_text"].tolist()
y_train = train_data[["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]].values.tolist()
train_encodings = tokenizer(X_train, padding=True, truncation=True, max_length=128, return_tensors="pt")
train_dataset = torch.utils.data.TensorDataset(train_encodings["input_ids"], train_encodings["attention_mask"], torch.tensor(y_train))

X_test = test_data["comment_text"].tolist()
test_encodings = tokenizer(X_test, padding=True, truncation=True, max_length=128, return_tensors="pt")
test_dataset = torch.utils.data.TensorDataset(test_encodings["input_ids"], test_encodings["attention_mask"])

# Define the training arguments
training_args = TrainingArguments(
    output_dir='./results',
    evaluation_strategy="epoch",
    num_train_epochs=3,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=64,
    logging_dir='./logs',
)

# Define the trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=test_dataset,
)

# Train the model
trainer.train()

# Evaluate the model
eval_results = trainer.evaluate()

# Print the evaluation results
print(eval_results)