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DataCollatorForLanguageModeling

As explained in Natural Language Processing in Action, Second Edition

DataCollatorForLanguageModeling is a Transformers data-collation class that prepares batches for language-model training, including padding and token-label construction.

DataCollatorForLanguageModeling prepares batches for language-model training and supplies the interface expected by a Hugging Face Trainer pipeline. For causal language modeling, configure it with mlm=False so training uses tokens in left-to-right order; use mlm=True for datasets containing randomly masked tokens, as used with bidirectional models such as BERT. section 10.2.2

A typical fine-tuning setup connects the collator to:

  • A tokenizer and tokenized PyTorch training and evaluation datasets. section 10.2.2
  • TrainingArguments, which specify batch size, epochs, output location, and checkpoint strategy. section 10.2.2
  • Trainer, which receives the collator alongside the model and datasets. section 10.2.2

Causal GPT-2 fine-tuning

collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)\ntrainer = Trainer(ft_model, training_args, data_collator=collator, train_dataset=train_dataset, eval_dataset=eval_dataset)\ntrainer.train() section 10.2.2

Listing 10.11 Defining training arguments for GPT-2 fine-tuning
>>> from nlpia2.constants import DATA_DIR                   #1
>>> from transformers import TrainingArguments
>>> from transformers import DataCollatorForLanguageModeling
>>> training_args = TrainingArguments(
...    output_dir=DATA_DIR / 'ch10_checkpoints',
...    per_device_train_batch_size=5,
...    num_train_epochs=5,
...    save_strategy='epoch')
>>> collator = DataCollatorForLanguageModeling(
...     tokenizer=tokenizer, mlm=False)            #2

Inspect the complete TrainingArguments and DataCollatorForLanguageModeling configuration used for GPT-2 fine-tuning. section 10.2.2

How Natural Language Processing in Action, Second Edition covers it

How deep it goes
Defines causal versus masked configuration and shows Trainer wiring, but does not detail the collator’s internal batching algorithm.
Examples
Uses tokenized text lines, a GPT-2 collator configuration, and generated completions as concrete fine-tuning artifacts.
What it assumes
Assumes tokenization, PyTorch datasets, train/evaluation splits, and Hugging Face Trainer; clarifies the mlm switch’s purpose.
What you can do after
Operate/configure it by passing a tokenizer and mlm=False to Trainer for causal GPT-2 fine-tuning.
How it connects
Connects tokenized datasets with TrainingArguments, Trainer, GPT-2, causal language modeling, and masked language modeling.
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