Language training on a model
I’m trying to do some language training on a GPT model (distilgpt2). I’ve gone through the Transformers course here at huggingface, and I’ve dabbled with some article I found on the internet training a model from german recipes, but I’m having trouble adapting the code and integrating what I’ve learned for my own purposes.Here’s my code. The dataset is a csv file with the columns id and text, and I’m attempting to inherit from Dataset so that I can only use the text column. But the trainer throws an error.File "my-distgpt2/t.py", line 57, in <module>
    trainer.train()
AttributeError: 'MyDataset' object has no attribute '_data'. Did you mean: 'data'?What am I doing wrong?from datasets import load_dataset, Dataset
from transformers import set_seed
from transformers import AutoTokenizer, AutoModelWithLMHead
from transformers import DataCollatorForLanguageModeling
from transformers import Trainer, TrainingArguments

set_seed(42)

tokenizer = AutoTokenizer.from_pretrained('distilgpt2')
model = AutoModelWithLMHead.from_pretrained('distilgpt2')

# Dataset features: {'id', 'text'}
raw_dataset = load_dataset('csv', data_files='dataset.csv')
dataset = raw_dataset['train'].train_test_split(test_size=0.2)

class MyDataset(Dataset):
    def __init__(self, dataset):
        self.dataset = dataset
        self.tokenizer = tokenizer

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

    def __getitem__(self, id):
        text = self.dataset[id]['text']
        inputs = self.tokenizer(text, truncation=True)
        return {'input_ids': inputs['input_ids'], 'attention_mask': inputs['attention_mask']}

train_dataset = MyDataset(dataset['train'])
eval_dataset = MyDataset(dataset['test'])

data_collator = DataCollatorForLanguageModeling(
    tokenizer=tokenizer,
    mlm=False,
)

training_args = TrainingArguments(
    output_dir="./model",
    overwrite_output_dir=True,
    num_train_epochs=100,
    per_device_train_batch_size=32, # batch size for training
    per_device_eval_batch_size=64,  # batch size for evaluation
    eval_steps = 400, # Number of update steps between two evaluations.
    save_steps=800,   # after # steps model is saved
    warmup_steps=500, # number of warmup steps for learning rate scheduler
    prediction_loss_only=True,
)

trainer = Trainer(
    args=training_args,
    tokenizer=tokenizer,
    data_collator=data_collator,
    model=model,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
)
trainer.train()

trainer.save_model()
2023-08-27T00:34:27Z
I don’t see any replies, did you ever get a reply to this because I’m having the same problem you are an I found this in a google search