Principle:Huggingface Datatrove Token Counting
| Sources | Domains | Last Updated |
|---|---|---|
| Huggingface Datatrove | Tokenization, Statistics | 2026-02-14 |
Overview
Counting the number of tokens in each document without saving tokenized output, for metrics and statistics.
Description
Token counting applies a tokenizer to each document's text in batches and records the token count in the document's metadata, without saving the actual tokenized output to disk. This makes it a lightweight, pass-through operation compared to full tokenization: the document pipeline continues flowing with each document enriched by a token_count metadata field.
Key characteristics:
- Batch tokenization -- documents are tokenized in configurable batches (default 10000) using tokenizer.encode_batch for throughput
- Pass-through semantics -- documents are yielded downstream with their text unchanged; only metadata is modified
- Optional EOS counting -- when count_eos_token is enabled, the count is incremented by 1 per document to account for the end-of-sequence token that would be appended during actual tokenization
- Statistics tracking -- total token counts across all documents are accumulated via stat_update for pipeline-level reporting
Usage
After deduplication to measure token-level dataset size, or at any pipeline stage for general dataset statistics. Commonly used to compute before/after metrics for filtering and deduplication steps by comparing token counts at different pipeline positions.
Theoretical Basis
Token counting provides a more accurate measure of dataset size for language model training than character or word counts, since the actual training cost and data budget are denominated in tokens. The count depends on the specific tokenizer's vocabulary and merge rules, so it must be computed with the same tokenizer that will be used for training.