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Error database

Token indices sequence length is longer than the specified maximum (HuggingFace)

Your text tokenized to more tokens than the model can accept, and the crash will come later if you ignore this warning. Truncate, chunk, or switch to a longer-context model.

The message you saw
Token indices sequence length is longer than the specified maximum (HuggingFace)

By Updated

The error

Output
Token indices sequence length is longer than the specified maximum sequence length for this model (2318 > 1024). Running this sequence through the model will result in indexing errors

What it means

The tokenizer converted your text into 2318 tokens, but the model's positional machinery handles at most 1024. The tokenizer itself does not crash — it warns and hands over the full sequence anyway. The model is the thing that will fail, later, with an indexing error on CPU or a device-side assert on GPU. Treat the warning as the real error; the crash is only its echo.

Why it happens

Every model has a fixed context window baked in at training time. Older or smaller models — GPT-2 at 1024, early BERTs at 512 — meet modern-length documents and lose. Summarisation and classification pipelines hit it on long articles; RAG pipelines hit it when too many retrieved passages get stuffed into one prompt.

How to fix it

1. Truncate at tokenization. The one-line fix when losing the tail is acceptable.

python
enc = tokenizer(text, truncation=True, max_length=1024, return_tensors="pt")

For classification, the opening of a document usually carries enough signal. For summarisation or extraction, truncation silently ignores content — decide consciously.

2. Chunk long documents with overlap, and process each chunk.

python
enc = tokenizer(
    text,
    truncation=True,
    max_length=1024,
    stride=128,
    return_overflowing_tokens=True,
    return_tensors="pt",
)
print(enc["input_ids"].shape)     # e.g. [3, 1024] — three overlapping chunks

The stride overlap keeps sentences at chunk borders from being cut blind. Aggregate per-chunk results afterwards — vote for classification, merge for extraction, summarise the summaries.

3. Switch to a model with a bigger window. Many modern models accept 8k, 32k or far more. Check tokenizer.model_max_length and the model card before engineering around a small window that a different checkpoint would remove.

4. In RAG pipelines, retrieve less. Fewer, better passages beat many truncated ones — cap the retrieved context so prompt plus generation fits the window with room to spare.

How to prevent it

Count before you send: len(tokenizer(text)["input_ids"]) against tokenizer.model_max_length. Make truncation policy an explicit parameter of your pipeline rather than a surprise inside it, and log whenever truncation actually fired — silent truncation is how models quietly answer questions about half a document.