Neural networks, deep learning, NLP · insight-0457

Check where the label evidence sits before truncating long texts

Situation
Insurance claim narratives or incident reports often exceed the encoder's maximum length and are cut at the limit.
Action
Plot the token-length distribution and the share of examples over the limit, check where key phrases appear, and compare head, tail and head-plus-tail truncation or chunking on validation data.
Reason
Encoders have a fixed maximum length set in pretraining, so anything beyond it is invisible unless you restructure the input.
Boundary
Classification and extraction with encoder models on long documents.

Symptom or risk

If the decisive information is near the end, such as the final assessment line, truncation removes it and the model learns from the wrong part.

Short texts, or tasks where the start of the document carries the label.

Chunking multiplies inference cost and needs an aggregation rule.

Truncating silently in the tokenizer call without logging how many examples were cut.