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Trainer

As explained in Natural Language Processing in Action, Second Edition

Trainer is a high-level Transformers API class that orchestrates model training and evaluation from configured arguments and data components.

Trainer is a high-level Transformers API class that assembles a model, training configuration, batch collator, and datasets into a fine-tuning workflow. It replaces much of the manual PyTorch setup with configured presets, leaving batch size and epoch count as key choices. section 10.2.2

A practical Trainer pipeline connects:

  • TrainingArguments for output locations, batch size, epochs, and checkpoint strategy. section 10.2.2
  • DataCollatorForLanguageModeling for dynamic batching, padding, and causal-language-model settings such as mlm=False. section 10.2.2
  • A model plus training and evaluation datasets, commonly prepared from tokenized text and a train/evaluation split. section 10.2.2
Listing 10.12 Fine-tuning GPT-2 with Hugging Face’s Trainer class
>>> from transformers import Trainer
>>> ft_model = GPT2LMHeadModel.from_pretrained("gpt2")    #1

>>> trainer = Trainer(
...        ft_model,
...        training_args,
...        data_collator=collator,           #2
...        train_dataset=train_dataset,    #3
...        eval_dataset=eval_dataset)
>>> trainer.train()

The configuration becomes executable by constructing Trainer with GPT-2, training arguments, a collator, and datasets, then calling trainer.train(). section 10.2.2

How Natural Language Processing in Action, Second Edition covers it

How deep it goes
Explains configuration and invocation, but does not detail the underlying optimizer or learning-rate implementation.
Examples
Uses tokenized NLPiA lines, split datasets, GPT-2, and generated comparisons between vanilla and fine-tuned models.
What it assumes
Assumes a tokenizer, model, datasets, and training configuration; demonstrates dataset preparation and API-compatible components.
What you can do after
Operate/configure a Hugging Face fine-tuning pipeline and train a GPT-2 model with trainer.train().
How it connects
Connects TrainingArguments, DataCollatorForLanguageModeling, PyTorch datasets, tokenization, and GPT-2.
Cover of Natural Language Processing in Action, Second Edition
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Natural Language Processing in Action, Second Edition. The section links point to the parts of the book it draws on.