Rename H1 to match the repo slug

3a9c1ac verified 2 months ago

6.01 kB

---
license: afl-3.0
base_model: Xenova/bert-base-multilingual-cased-ner-hrl
base_model_relation: quantized
library_name: transformers.js
pipeline_tag: token-classification
tags:
  - token-classification
  - named-entity-recognition
  - ner
  - onnx
  - transformers.js
  - multilingual
  - on-device
language:
  - de
  - en
  - es
  - fr
  - it
  - lv
  - nl
  - pt
---
# minuscule

**A Latin-script vocabulary prune of
[`bert-base-multilingual-cased-ner-hrl`](https://huggingface.co/Xenova/bert-base-multilingual-cased-ner-hrl).**
Specifically of
[`Xenova/bert-base-multilingual-cased-ner-hrl`](https://huggingface.co/Xenova/bert-base-multilingual-cased-ner-hrl),
the int8 ONNX export of `Davlan/bert-base-multilingual-cased-ner-hrl`, stripped
of the vocabulary a Latin-script deployment can never tokenize.

**178.5 MB → 139.6 MB (−21.8%), with identical output** for the languages it
still covers. No encoder weight was touched and nothing was re-quantized. This
is a packaging change, not a distillation — there is no accuracy trade to
evaluate.

## Why it works

`bert-base-multilingual-cased` carries a 119,547-token vocabulary spanning 104
languages. At 768 dimensions that embedding table is **~92 MB of the 178 MB
file — more than half the model**, and every user downloads all of it. If you
ship Latin-script languages, the Cyrillic, CJK, Arabic, Hangul, Hebrew,
Devanagari, Greek, Armenian and Thai rows are downloaded by everyone and
gathered by no one.

Removing them is exact rather than approximate, for two reasons:

- The embedding is **per-tensor quantized** — a scalar scale and zero-point
  shared by the whole tensor — and is read by a plain `Gather`. Selecting a
  subset of rows preserves both the int8 values and their dequantization. No
  weight is re-quantized and nothing drifts.
- **WordPiece is longest-match over the available pieces.** Only pieces
  unreachable from a kept script were removed, so segmentation of Latin text is
  unchanged too.

Vocabulary: **119,547 → 68,875 tokens** (57.6% kept).

## Verification

Not asserted — measured. Both models were run over the same 77 documents —
synthetic contracts, financial statements and medical records across 7
languages — comparing **raw logits** rather than entity lists, because entity
lists can agree by luck:

| | |
| --- | --- |
| documents | 77 |
| entities compared | 2,219 |
| segmentation differences | **0** |
| entity differences | **0** |
| max abs. logit delta | **0.0000000000** |

The comparison is over raw logits and fails on any nonzero difference.

Two limits worth stating plainly. The corpus covers **7 of the 8 kept
languages — Latvian is not represented in it**, and while the argument above
applies to Latvian exactly as it does to the rest, it has not been measured
there. And the documents are synthetic: they are shaped like real contracts and
records rather than drawn from them.

So: **verify on your own corpus before shipping.** The guarantee is exact for
kept scripts, but "kept" depends on which scripts you selected, and a corpus
containing a dropped script will (correctly) diverge.

## ⚠️ Latin script only

Text in a dropped script is **not supported** — it degrades to `[UNK]`, not to
"slightly worse". Relative to the original model:

- **Kept:** German, English, Spanish, French, Italian, Latvian, Dutch,
  Portuguese — and any other Latin-script language the base model handled.
- **Removed:** **Arabic and Chinese**, which the original supports and this
  model does not.

If you need those, use
[`Xenova/bert-base-multilingual-cased-ner-hrl`](https://huggingface.co/Xenova/bert-base-multilingual-cased-ner-hrl)
— it is the model this one is built from, unchanged apart from the vocabulary.

The same technique keeps whichever scripts you do ship: retaining kana and
ideographs alongside Latin lands at ~151 MB, still 27 MB under the original.

## Labels

`PER`, `ORG`, `LOC`, `DATE` — unchanged from the base model, as `B-`/`I-` tags.

## Usage (transformers.js)

```js
import { pipeline } from "@huggingface/transformers";
const ner = await pipeline(
  "token-classification",
  "promptshield/minuscule",
  { dtype: "q8" },
);
await ner("Le contrat a été signé par Jean-Baptiste Lefèvre à Bordeaux.", {
  aggregation_strategy: "simple",
});
```

It also loads in `onnxruntime` directly — it is a standard BERT token-classification
graph with `input_ids`, `attention_mask` and `token_type_ids`.

## Provenance and licence

| | |
| --- | --- |
| base | [`google-bert/bert-base-multilingual-cased`](https://huggingface.co/google-bert/bert-base-multilingual-cased) — Apache-2.0 |
| fine-tune | [`Davlan/bert-base-multilingual-cased-ner-hrl`](https://huggingface.co/Davlan/bert-base-multilingual-cased-ner-hrl) — **AFL-3.0** |
| ONNX export + int8 quantization | [`Xenova/bert-base-multilingual-cased-ner-hrl`](https://huggingface.co/Xenova/bert-base-multilingual-cased-ner-hrl) — the direct parent of this repo |
| this repo | vocabulary pruning only — **AFL-3.0**, inherited |

All credit for the model itself belongs to **David Adelani (Davlan)** for the
fine-tune, to the Google Research team for the base model, and to
**Joshua Lochner (Xenova)** for the ONNX export and int8 quantization this repo
is built directly on. We changed the vocabulary and nothing else.

The upstream card declares no training datasets; if your use has data-provenance
requirements, check with the original authors rather than relying on this card.

## Who made this and why

Built for [promptShield](https://promptshield.ca), an offline document
anonymizer. Its web app runs detection **entirely in the browser** — no document
ever leaves the device — so the model is a first-visit download, and 39 MB of
unreachable vocabulary was 39 MB charged to every visitor.

Published because the technique generalises: the embedding table is over half of
most multilingual encoders, and almost every deployment ships a fraction of the
104 languages.

	---
	license: afl-3.0
	base_model: Xenova/bert-base-multilingual-cased-ner-hrl
	base_model_relation: quantized
	library_name: transformers.js
	pipeline_tag: token-classification
	tags:
	- token-classification
	- named-entity-recognition
	- ner
	- onnx
	- transformers.js
	- multilingual
	- on-device
	language:
	- de
	- en
	- es
	- fr
	- it
	- lv
	- nl
	- pt
	---

	# minuscule

	**A Latin-script vocabulary prune of
	[`bert-base-multilingual-cased-ner-hrl`](https://huggingface.co/Xenova/bert-base-multilingual-cased-ner-hrl).**
	Specifically of
	[`Xenova/bert-base-multilingual-cased-ner-hrl`](https://huggingface.co/Xenova/bert-base-multilingual-cased-ner-hrl),
	the int8 ONNX export of `Davlan/bert-base-multilingual-cased-ner-hrl`, stripped
	of the vocabulary a Latin-script deployment can never tokenize.

	178.5 MB → 139.6 MB (−21.8%), with identical output for the languages it
	still covers. No encoder weight was touched and nothing was re-quantized. This
	is a packaging change, not a distillation — there is no accuracy trade to
	evaluate.

	## Why it works

	`bert-base-multilingual-cased` carries a 119,547-token vocabulary spanning 104
	languages. At 768 dimensions that embedding table is **~92 MB of the 178 MB
	file — more than half the model**, and every user downloads all of it. If you
	ship Latin-script languages, the Cyrillic, CJK, Arabic, Hangul, Hebrew,
	Devanagari, Greek, Armenian and Thai rows are downloaded by everyone and
	gathered by no one.

	Removing them is exact rather than approximate, for two reasons:

	- The embedding is per-tensor quantized — a scalar scale and zero-point
	shared by the whole tensor — and is read by a plain `Gather`. Selecting a
	subset of rows preserves both the int8 values and their dequantization. No
	weight is re-quantized and nothing drifts.
	- WordPiece is longest-match over the available pieces. Only pieces
	unreachable from a kept script were removed, so segmentation of Latin text is
	unchanged too.

	Vocabulary: 119,547 → 68,875 tokens (57.6% kept).

	## Verification

	Not asserted — measured. Both models were run over the same 77 documents —
	synthetic contracts, financial statements and medical records across 7
	languages — comparing raw logits rather than entity lists, because entity
	lists can agree by luck:

	\| \| \|
	\| --- \| --- \|
	\| documents \| 77 \|
	\| entities compared \| 2,219 \|
	\| segmentation differences \| 0 \|
	\| entity differences \| 0 \|
	\| max abs. logit delta \| 0.0000000000 \|

	The comparison is over raw logits and fails on any nonzero difference.

	Two limits worth stating plainly. The corpus covers **7 of the 8 kept
	languages — Latvian is not represented in it**, and while the argument above
	applies to Latvian exactly as it does to the rest, it has not been measured
	there. And the documents are synthetic: they are shaped like real contracts and
	records rather than drawn from them.

	So: verify on your own corpus before shipping. The guarantee is exact for
	kept scripts, but "kept" depends on which scripts you selected, and a corpus
	containing a dropped script will (correctly) diverge.

	## ⚠️ Latin script only

	Text in a dropped script is not supported — it degrades to `[UNK]`, not to
	"slightly worse". Relative to the original model:

	- Kept: German, English, Spanish, French, Italian, Latvian, Dutch,
	Portuguese — and any other Latin-script language the base model handled.
	- Removed: Arabic and Chinese, which the original supports and this
	model does not.

	If you need those, use
	[`Xenova/bert-base-multilingual-cased-ner-hrl`](https://huggingface.co/Xenova/bert-base-multilingual-cased-ner-hrl)
	— it is the model this one is built from, unchanged apart from the vocabulary.

	The same technique keeps whichever scripts you do ship: retaining kana and
	ideographs alongside Latin lands at ~151 MB, still 27 MB under the original.

	## Labels

	`PER`, `ORG`, `LOC`, `DATE` — unchanged from the base model, as `B-`/`I-` tags.

	## Usage (transformers.js)

	```js
	import { pipeline } from "@huggingface/transformers";

	const ner = await pipeline(
	"token-classification",
	"promptshield/minuscule",
	{ dtype: "q8" },
	);

	await ner("Le contrat a été signé par Jean-Baptiste Lefèvre à Bordeaux.", {
	aggregation_strategy: "simple",
	});
	```

	It also loads in `onnxruntime` directly — it is a standard BERT token-classification
	graph with `input_ids`, `attention_mask` and `token_type_ids`.

	## Provenance and licence

	\| \| \|
	\| --- \| --- \|
	\| base \| [`google-bert/bert-base-multilingual-cased`](https://huggingface.co/google-bert/bert-base-multilingual-cased) — Apache-2.0 \|
	\| fine-tune \| [`Davlan/bert-base-multilingual-cased-ner-hrl`](https://huggingface.co/Davlan/bert-base-multilingual-cased-ner-hrl) — AFL-3.0 \|
	\| ONNX export + int8 quantization \| [`Xenova/bert-base-multilingual-cased-ner-hrl`](https://huggingface.co/Xenova/bert-base-multilingual-cased-ner-hrl) — the direct parent of this repo \|
	\| this repo \| vocabulary pruning only — AFL-3.0, inherited \|

	All credit for the model itself belongs to David Adelani (Davlan) for the
	fine-tune, to the Google Research team for the base model, and to
	Joshua Lochner (Xenova) for the ONNX export and int8 quantization this repo
	is built directly on. We changed the vocabulary and nothing else.

	The upstream card declares no training datasets; if your use has data-provenance
	requirements, check with the original authors rather than relying on this card.

	## Who made this and why

	Built for [promptShield](https://promptshield.ca), an offline document
	anonymizer. Its web app runs detection entirely in the browser — no document
	ever leaves the device — so the model is a first-visit download, and 39 MB of
	unreachable vocabulary was 39 MB charged to every visitor.

	Published because the technique generalises: the embedding table is over half of
	most multilingual encoders, and almost every deployment ships a fraction of the
	104 languages.