When dealing with documents longer than the `max_length` in training translation Transformer models, chunking is a crucial technique. Chunking involves splitting the long document into smaller segments or "chunks" that are within the `max_length` limit of the model. This is necessary because Transformer models have a fixed input length limit, and exceeding this limit can lead to errors or sub - optimal performance. One common approach is to divide the document into non - overlapping chunks. For example, if the `max_length` is 512 tokens and the document has 1500 tokens, it can be split into three chunks of approximately 512 tokens each (with the last chunk potentially being shorter). In code, using the Hugging Face Transformers library, the following is a simple example of how to perform chunking: ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('bert - base - uncased') document = "This is a very long document that needs to be chunked for training a Transformer model. It contains a lot of text and exceeds the max_length limit." max_length = 128 tokens = tokenizer.tokenize(document) chunks = [tokens[i:i + max_length] for i in range(0, len(tokens), max_length)] for chunk in chunks: input_ids = tokenizer.convert_tokens_to_ids(chunk) print(input_ids) ``` The benefits of chunking are multi - fold. It allows the model to process long documents by breaking them into manageable parts. Each chunk can be independently processed by the model, and then the results can be aggregated to get an overall translation. Regarding relevant documentation, the Hugging Face Transformers documentation provides detailed information on handling long sequences and chunking. The official documentation also has examples and best practices for dealing with input length limitations in Transformer - based models.