DataCollatorForLanguageModeling is a data collator class used in the Hugging Face Transformers library. It is specifically designed for preparing batches of text data for training and fine-tuning language models. It takes care of tokenization, masking, and batching of input sequences, making it easy to train language models with a variety of architectures, such as BERT, GPT, and RoBERTa.

How to use DataCollatorForLanguageModeling

  1. Import the necessary classes: Import the required classes from the Transformers library.
    from transformers import DataCollatorForLanguageModeling, AutoTokenizer
  2. Initialize the tokenizer: Load a pre-trained tokenizer for your language model. This tokenizer will be used by the data collator to tokenize the input text data.
    tokenizer = AutoTokenizer.from_pretrained('gpt2')
  3. Create a DataCollatorForLanguageModeling instance: Instantiate the DataCollatorForLanguageModeling class with the tokenizer and specify whether to use masked language modeling (for models like BERT) or not (for models like GPT and GPT-2).
    data_collator = DataCollatorForLanguageModeling(
        mlm=False  # Set to True for masked language models like BERT
  4. Prepare your dataset: Load and preprocess your dataset to create a list of text samples.
    # This is just an example, replace this with your actual dataset loading and preprocessing
    dataset = ["This is a sample text.", "Another example of text data."]
  5. Tokenize and collate the dataset: Use the data_collator instance to tokenize and collate your dataset into a format suitable for training your language model.
    batch = data_collator(dataset)
  6. Train your language model: Now you can use the prepared batch of data to train your language model.

By using DataCollatorForLanguageModeling, you can streamline the process of preparing text data for training language models, ensuring that the data is correctly tokenized, masked, and batched for efficient training.

Examples on DataCollatorForLanguageModeling

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