hf_tokenizers 1.0.4

HuggingFace tokenizers for Dart over FFI. Load any tokenizer.json and get byte-exact BPE, WordPiece, and Unigram encoding, backed by the Rust crate.

hf_tokenizers

Run a model on device, count tokens against a context window, or chunk text for retrieval, and you need the tokenizer the model was trained with. A reimplementation that is nearly right hands back ids the model never saw, and nothing downstream complains.

This package reimplements nothing. It binds the Rust tokenizers crate that HuggingFace itself ships, through a thin C ABI, and reads any model's tokenizer.json.

dart pub add hf_tokenizers

import 'package:hf_tokenizers/hf_tokenizers.dart';

final tk = Tokenizer.fromFile('bert-base-uncased/tokenizer.json');

tk.encode('The naïve café in São Paulo', addSpecialTokens: false);
// [1996, 15743, 7668, 1999, 7509, 9094]

Both rows above come out of the same tokenizer.json. The top one is this package. The bottom one is the shortest thing that looks correct: lowercase the text, then look each word up in the vocabulary. That file declares a normalizer which strips accents before any lookup happens, and half the sentence depends on it. tool/parity_shot.dart draws the picture from the fixture in this repository, and it re-checks every id on the top row against the vocabulary table parsed straight out of the JSON, so the numbers are measured rather than typed in.

dart run example/parity.dart prints those two rows in a terminal and checks the top one against the ids above.

Use it

final ids = tk.encode('hello world');   // [101, 7592, 2088, 102]  (bert)
final text = tk.decode(ids);            // "hello world"
print(tk.vocabSize);                    // 30522

tk.close(); // optional; a finalizer also frees the native tokenizer

encode adds the model's special tokens by default (BERT's [CLS]/[SEP], and so on); pass addSpecialTokens: false to skip them. Tokenizer.fromBytes takes the tokenizer.json bytes directly, for assets loaded at runtime.

dart run example/hf_tokenizers_example.dart

Single tokens

To look a token up without encoding a whole string, use tokenToId and its inverse idToToken. Both return null when the token or id is not in the vocabulary.

tk.tokenToId('[CLS]');   // 101
tk.tokenToId('hello');   // 7592
tk.idToToken(101);       // "[CLS]"
tk.idToToken(7592);      // "hello"

idToToken returns the raw token, keeping the sub-word markers the model uses. That is the difference from decode, which detokenizes back to plain text:

final ids = tk.encode('tokenization', addSpecialTokens: false);
ids.map(tk.idToToken).toList();  // ["token", "##ization"]  (## kept)
tk.decode(ids);                  // "tokenization"          (## resolved)

Offsets: map tokens back to the text

encodeWithOffsets returns each token together with the [start, end) span of the input it came from, which is what token-accurate chunking, span highlighting, and entity extraction need.

final text = 'hello world';
final bytes = utf8.encode(text);
for (final t in tk.encodeWithOffsets(text, addSpecialTokens: false)) {
  print(utf8.decode(bytes.sublist(t.start, t.end))); // "hello", then "world"
}

The offsets are UTF-8 byte offsets, which is what the underlying crate reports. They are not UTF-16 indices. Slice utf8.encode(text) rather than calling text.substring, or the math goes wrong on any non-ASCII input. Special tokens like [CLS] come back with an empty span (start == end).

Token budgets

Context windows and embedding endpoints are measured in tokens. Dart strings are measured in UTF-16 units, and the usual "about four characters per token" guess either wastes budget or overshoots it. These two cut where the tokenizer says.

// The longest prefix that fits, cut on a token boundary.
final head = tk.truncateToTokens(document, 512);

// Or split the whole document into pieces that each fit an embedding model's
// input limit, with a little context repeated across the seams.
for (final chunk in tk.chunkByTokens(document, 256, overlapTokens: 32)) {
  await embed(chunk);
}

Both count what the model counts. BERT adds [CLS] and [SEP], so a budget of 512 leaves 510 for text, and a budget smaller than the markers themselves is rejected rather than quietly answered with an empty string that would still encode to two tokens. Cuts land on token boundaries, which are UTF-8 boundaries. No character is split. With no overlap the pieces concatenate back to the original text, whitespace included.

chunkByTokens re-encodes every piece instead of trusting the count it already has, because tokenization is context-dependent: ##ization is one token inside internationalization and two when the same bytes begin a chunk. The Example tab has the diagram.

Feeding a retrieval pipeline

chunkByTokens returns strings. A retrieval pipeline usually also wants to know where each chunk came from, to highlight the passage it cited. example/rag_chunking.dart builds that: a Chunker for rag_kit on top of encodeWithOffsets, carrying both the exact token count and a real range in the source.

The interesting part is the conversion. TokenOffset is in UTF-8 bytes and rag_kit's Chunk promises source.substring(start, end) == text, which is UTF-16. The two agree on ASCII and diverge the moment the text is not. The example maps between them and then asserts the contract, because that is the mistake worth catching once rather than in production.

Run it to see why a character budget cannot be tuned: on one mixed English-and-Japanese paragraph, the chunks came out between 2.2 and 4.8 characters per token.

What it supports

Whatever the tokenizer.json declares. Because it is the real Rust library, the full pipeline is applied exactly: normalizers, pre-tokenizers, the model itself (BPE, byte-level BPE, WordPiece, Unigram), and post-processors. Load a GPT-2, BERT, Llama, or sentence-transformers tokenizer, and the ids match Python.

Correctness

The test suite loads a real bert-base-uncased tokenizer.json and asserts the ids against known-good values from HuggingFace, including WordPiece splitting and a decode round-trip. Token ids that match the reference are the whole point. They are checked, never assumed.

Platforms

The native library loads one of two ways: a prebuilt binary fetched from the GitHub release (no toolchain needed), or a source build with cargo as a fallback. The source build compiles for the host, which means it needs a Rust toolchain and a host that matches the target.

Target	How it loads	Needs a toolchain
macOS arm64, macOS x64	prebuilt	no
Linux x64 (glibc ≥ 2.34)	prebuilt	no
Windows x64	prebuilt	no
Any of the above, offline	source build (fallback)	yes (Rust)
Linux arm64	source build	yes (Rust)
Android, iOS	not supported yet	—

The prebuilt Linux binary needs glibc 2.34 or newer. That floor was measured rather than assumed: the released .so references no glibc symbol above GLIBC_2.34, and links nothing beyond libc.so.6 and libgcc_s.so.1. What it means for the distributions people actually deploy on:

Distribution	glibc	Prebuilt
Ubuntu 24.04	2.39	loads
Debian 12	2.36	loads
Ubuntu 22.04	2.35	loads
Debian 11	2.31	fails
Alpine	musl	fails

On Debian 11 the loader reports version 'GLIBC_2.34' not found. Alpine fails for a different reason: it ships musl and has no libc.so.6, so the load stops at the missing library rather than at a version. Check yours with ldd --version.

The source build does not rescue this. The hook falls back to cargo only when the prebuilt cannot be downloaded; a download that succeeds is used as-is, and on an older distribution the failure then surfaces when the library is loaded rather than at build time. Installing Rust does not change that on its own. Lowering the floor means rebuilding the release binaries against an older glibc.

Android and iOS need cross-compiled prebuilts, which are not published yet. Adding the package to a mobile Flutter target fails at build time with a message that says exactly that, rather than a confusing toolchain error. This is a server-side and desktop package today. The sdk:flutter tag covers Flutter desktop apps only.

When to use something else

The tables above name three places this package does not go: a phone, Alpine, and a Linux older than glibc 2.34. The web is a fourth, because FFI does not run there. A native library also costs a download and a build hook that a pure-Dart package does not.

dart_sentencepiece_tokenizer is the pure-Dart option, and today it is the more widely used of the two. It has no dependencies, ships Android and iOS, and reads tokenizer.json as well as SentencePiece's own .model file. Its loader handles the BPE and Unigram algorithms that Gemma and Llama ship. Hand it bert-base-uncased/tokenizer.json and version 1.3.2 stops with FormatException: Missing model type: BERT leaves model.type out of that file, and the loader has no WordPiece branch to fall back on. WordPiece is what BERT and the sentence-transformers embedding models use.

That is the line. Reach for this package when the model is BERT-shaped, or when matching the reference pipeline exactly is the requirement. Reach for that one when the target is a phone.

License

MIT. The bound library is HuggingFace's tokenizers crate (Apache-2.0).