milestone_3.py

from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch
from torch.utils.data import Dataset
# from torch.optim import AdamW
import pandas as pd
from sklearn.model_selection import train_test_split


# assignment 3
model_name = "distilbert-base-uncased"

class ToxicDataset(Dataset):

    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels
    
    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        item["labels"] = torch.tensor(self.labels[idx])
        print(item)
        return item

    def __len__(self):
        return len(self.labels)

print("Reading data...")
data = pd.read_csv("./data/train.csv")
toxic_data = pd.DataFrame()
toxic_data["text"] = data["comment_text"]
toxic_data["labels"] = data.iloc[:, 2:].values.tolist()

print("Data read. Splitting data...")
train_texts, val_texts, train_labels, val_labels = train_test_split(toxic_data.text.to_list(), toxic_data.labels.to_list(), test_size=.2)


print("Data split. Tokenizing data...")
tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)

train_encodings = tokenizer.batch_encode_plus(train_texts, truncation=True, padding=True, return_tensors='pt')
val_encodings = tokenizer.batch_encode_plus(val_texts, truncation=True, padding=True, return_tensors='pt')


train_dataset = ToxicDataset(train_encodings, train_labels)
val_dataset = ToxicDataset(val_encodings, val_labels)

print("Data tokenized. Beginning training...")

training_args = TrainingArguments(
    output_dir="./results",          
    num_train_epochs=2,             
    per_device_train_batch_size=4, 
    per_device_eval_batch_size=16, 
    warmup_steps=500,               
    weight_decay=0.01,             
    logging_dir="./logs",            
    logging_steps=10,
)

# device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

model = DistilBertForSequenceClassification.from_pretrained(model_name, num_labels=6)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
)

trainer.train()

# model = DistilBertForSequenceClassification.from_pretrained(model_name, num_labels=6)

# model.to(device)
# model.train()

# train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)

# optim = AdamW(model.parameters(), lr=5e-5)

# num_train_epochs = 2

# for epoch in range(num_train_epochs):
#     for batch in train_loader:
#         optim.zero_grad()
#         input_ids = batch["input_ids"].to(device)
#         attention_mask = batch["attention_mask"].to(device)
#         labels = batch["labels"].to(device)

#         outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
        
#         loss = outputs[0]
#         loss.backward()
#         optim.step()

# model.eval()




print("Training complete. Saving model...")

save_directory = ".results/model"
model.save_pretrained(save_directory)

print("Model saved.")