When working with documents longer than a model’s maximum sequence length (e.g., 512 tokens for BERT), the most common approach is to split the text into smaller chunks. Most models can’t process sequences beyond their token limit, so splitting ensures each segment fits within the model’s capacity. For example, you might divide a 1,000-token document into two 512-token chunks, allowing some overlap (e.g., 50 tokens) to preserve context between sections. Tools like Hugging Face’s transformers library provide utilities to split text, and frameworks like LangChain offer text splitters that handle this programmatically. The key is to balance chunk size and overlap to avoid breaking up critical information while staying under the model’s limit.
Another strategy involves adjusting how the model processes the chunks. For tasks like classification, you might process each chunk separately and aggregate results (e.g., averaging probabilities for sentiment analysis). For question answering, you could run inference on each chunk and select the answer with the highest confidence score. A sliding window approach—processing overlapping segments incrementally—can also help maintain context. For instance, if a model accepts 512 tokens, you might slide the window by 256 tokens each time, ensuring continuity. However, this increases computational cost, as the same text is processed multiple times. Some models, like Longformer or Reformer, are designed for longer sequences using sparse attention mechanisms, but these may require retraining or adapting existing workflows.
Finally, hierarchical methods or summarization can reduce text length. For example, split the document into sections, generate embeddings for each, then combine them (e.g., using max-pooling or averaging) for tasks like document retrieval. Alternatively, summarize chunks first and process the summaries. This works well for tasks where local context matters, like named entity recognition, but may lose global coherence. Always test chunking strategies against your specific task—some applications tolerate truncation, while others need careful context preservation. Libraries like SpaCy or NLTK can help split text by sentences or paragraphs, and custom logic can prioritize critical sections (e.g., keeping paragraphs with keywords intact).