synthiumjp

The EWT-only parser: POS and dependency models trained on UD English-EWT alone with GloVe (CC BY-SA 4.0), ONNX archive and PyTorch files; model card, LICENSE and NOTICE for it

980effa verified 14 days ago

preview code

Raw History Blame Contribute Delete

5.6 kB

---
license: cc-by-sa-4.0
language:
- en
library_name: onnxruntime
tags:
- stanza
- dependency-parsing
- part-of-speech
- lemmatization
- onnx
- universal-dependencies
datasets:
- universal_dependencies
---
# sourcedrecall English parser models (UD English-EWT, ONNX)

An English tokenizer, multi-word token expander, part-of-speech tagger, lemmatizer and
dependency parser in the architecture of [Stanza](https://github.com/stanfordnlp/stanza)
1.14.0, trained only on openly licensed data and converted so they run on
[ONNX Runtime](https://onnxruntime.ai) and numpy without PyTorch.

They are used by [sourcedrecall](https://github.com/synthiumjp/sourcedrecall), a local
memory for AI assistants whose grammar rules read the user's messages through these
parses. Running them without PyTorch cuts its install by about 600 MB.

## What they were trained on

- Part-of-speech tagger and dependency parser: trained for sourcedrecall on
  [UD English-EWT](https://github.com/UniversalDependencies/UD_English-EWT) v2.18 alone
  (CC BY-SA 4.0), with the GloVe word vectors Stanza packages for English (100,000 words,
  100 dimensions; GloVe is under the Open Data Commons Public Domain Dedication and
  License), no character language model, using Stanza 1.14.0's training scripts
  (`tools/stanza_ort/train_ewt.sh` in the sourcedrecall repository). The parser's
  training data was tagged by this tagger.
- Tokenizer, multi-word token expander and lemmatizer: Stanza's own English EWT models
  (`tokenize/ewt`, `mwt/ewt`, `lemma/ewt_nocharlm`), trained by the Stanford NLP Group on
  UD English-EWT.

So every model here is trained on UD English-EWT and nothing else, and the archive
includes no data or vectors under a non-commercial licence. An earlier archive in this
repository (`sourcedrecall-parser-en-stanza1.14.0.tar.gz`, at commit 72e0cff) holds
Stanza's English "combined" models instead, which are partly trained on text under
non-commercial terms; sourcedrecall no longer uses it.

## Accuracy

UD English-EWT test set, gold tokenization (tagging and parsing only), every word:

| | UPOS | UAS | LAS |
|---|---|---|---|
| these models | 96.41 | 91.05 | 88.83 |
| Stanza's own EWT models without a character model | 96.68 | 91.25 | 88.77 |
| Stanza's default English "combined" models | 96.77 | 92.20 | 90.04 |

In sourcedrecall, the same code with these models and with Stanza's combined models
gave the same LoCoMo test score (854 of 1,307 questions; 44 questions only one got right,
44 only the other) and the same counts on its pasted-text sets (2026-10-15).

## Same parses as Stanza

The runtime that loads these files (`stanza_ort`, in the sourcedrecall repository) was
checked against Stanza 1.14.0 on PyTorch with the same models on 7,755 texts (246,425
words): every LoCoMo conversation turn, the sourcedrecall benchmark cases, its parser
test sentences, and long and unusual inputs (lists, code mixed with prose, emoji,
8,000-character messages). Every field of every word (text, upos, xpos, feats, lemma,
head, deprel, character offsets) and every sentence split was identical, one text at a
time and in batches of 64. Checked on macOS arm64.

The PyTorch files the archive was converted from are in `pytorch/` here, for that check
and for anyone who wants to use them with Stanza itself. The trained parser's saved
config has `resolve_head_constraints` removed: Stanza's training writes it as off, which
makes Stanza's pipeline skip the head repair it does for its own models.

## Files

`sourcedrecall-parser-en-ewt.tar.gz` unpacks to:

| file | what it is |
|---|---|
| `tok.onnx` | tokenizer and sentence splitter network |
| `pos.onnx`, `pos_char.onnx` | part-of-speech and morphological features tagger, its character model |
| `dep.onnx`, `dep_char.onnx` | dependency parser (arc and label scorers), its character model |
| `pretrain_emb.npy` | the word vectors the tagger and parser use |
| `lemma.npz`, `mwt.npz` | weights of the lemmatizer and multi-word token sequence models |
| `config.json` | vocabularies, model settings and the lemma dictionaries |
| `NOTICE` | where each part comes from and its licence |

sha256 of the archive: `3cde3a9b29b6da81e72ad3208874fcdbb1961df82130829d3b8f7dd01ef362b1`

## How they were made

`tools/stanza_ort/convert.py` in the sourcedrecall repository loads the models with
Stanza 1.14.0 (`STANZA_PACKAGES=ours`, `tools/stanza_ort/pkgs.py`) and exports each
network with `torch.onnx.export`; two layers were rewritten in exactly equivalent form
for export (`nn.Bilinear` as an einsum, a boolean identity mask from `arange`).
`tools/stanza_ort/parity.py` is the comparison above.

## Licence and credit

The model weights are released under CC BY-SA 4.0, the licence of the UD English-EWT
data they are trained on. The conversion code and the runtime are Apache-2.0, in the
sourcedrecall repository. The architecture, training code, and the tokenizer, multi-word
token and lemma models are Stanza's, by the Stanford NLP Group (Apache-2.0).

Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton and Christopher D. Manning. 2020.
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages.
In Proceedings of ACL 2020: System Demonstrations.

Natalia Silveira, Timothy Dozat, Marie-Catherine de Marneffe, Samuel Bowman, Miriam
Connor, John Bauer and Christopher D. Manning. 2014. A Gold Standard Dependency Corpus
for English. In Proceedings of LREC 2014. (UD English-EWT)

Jeffrey Pennington, Richard Socher and Christopher D. Manning. 2014. GloVe: Global
Vectors for Word Representation. In Proceedings of EMNLP 2014.

	---
	license: cc-by-sa-4.0
	language:
	- en
	library_name: onnxruntime
	tags:
	- stanza
	- dependency-parsing
	- part-of-speech
	- lemmatization
	- onnx
	- universal-dependencies
	datasets:
	- universal_dependencies
	---

	# sourcedrecall English parser models (UD English-EWT, ONNX)

	An English tokenizer, multi-word token expander, part-of-speech tagger, lemmatizer and
	dependency parser in the architecture of [Stanza](https://github.com/stanfordnlp/stanza)
	1.14.0, trained only on openly licensed data and converted so they run on
	[ONNX Runtime](https://onnxruntime.ai) and numpy without PyTorch.

	They are used by [sourcedrecall](https://github.com/synthiumjp/sourcedrecall), a local
	memory for AI assistants whose grammar rules read the user's messages through these
	parses. Running them without PyTorch cuts its install by about 600 MB.

	## What they were trained on

	- Part-of-speech tagger and dependency parser: trained for sourcedrecall on
	[UD English-EWT](https://github.com/UniversalDependencies/UD_English-EWT) v2.18 alone
	(CC BY-SA 4.0), with the GloVe word vectors Stanza packages for English (100,000 words,
	100 dimensions; GloVe is under the Open Data Commons Public Domain Dedication and
	License), no character language model, using Stanza 1.14.0's training scripts
	(`tools/stanza_ort/train_ewt.sh` in the sourcedrecall repository). The parser's
	training data was tagged by this tagger.
	- Tokenizer, multi-word token expander and lemmatizer: Stanza's own English EWT models
	(`tokenize/ewt`, `mwt/ewt`, `lemma/ewt_nocharlm`), trained by the Stanford NLP Group on
	UD English-EWT.

	So every model here is trained on UD English-EWT and nothing else, and the archive
	includes no data or vectors under a non-commercial licence. An earlier archive in this
	repository (`sourcedrecall-parser-en-stanza1.14.0.tar.gz`, at commit 72e0cff) holds
	Stanza's English "combined" models instead, which are partly trained on text under
	non-commercial terms; sourcedrecall no longer uses it.

	## Accuracy

	UD English-EWT test set, gold tokenization (tagging and parsing only), every word:

	\| \| UPOS \| UAS \| LAS \|
	\|---\|---\|---\|---\|
	\| these models \| 96.41 \| 91.05 \| 88.83 \|
	\| Stanza's own EWT models without a character model \| 96.68 \| 91.25 \| 88.77 \|
	\| Stanza's default English "combined" models \| 96.77 \| 92.20 \| 90.04 \|

	In sourcedrecall, the same code with these models and with Stanza's combined models
	gave the same LoCoMo test score (854 of 1,307 questions; 44 questions only one got right,
	44 only the other) and the same counts on its pasted-text sets (2026-10-15).

	## Same parses as Stanza

	The runtime that loads these files (`stanza_ort`, in the sourcedrecall repository) was
	checked against Stanza 1.14.0 on PyTorch with the same models on 7,755 texts (246,425
	words): every LoCoMo conversation turn, the sourcedrecall benchmark cases, its parser
	test sentences, and long and unusual inputs (lists, code mixed with prose, emoji,
	8,000-character messages). Every field of every word (text, upos, xpos, feats, lemma,
	head, deprel, character offsets) and every sentence split was identical, one text at a
	time and in batches of 64. Checked on macOS arm64.

	The PyTorch files the archive was converted from are in `pytorch/` here, for that check
	and for anyone who wants to use them with Stanza itself. The trained parser's saved
	config has `resolve_head_constraints` removed: Stanza's training writes it as off, which
	makes Stanza's pipeline skip the head repair it does for its own models.

	## Files

	`sourcedrecall-parser-en-ewt.tar.gz` unpacks to:

	\| file \| what it is \|
	\|---\|---\|
	\| `tok.onnx` \| tokenizer and sentence splitter network \|
	\| `pos.onnx`, `pos_char.onnx` \| part-of-speech and morphological features tagger, its character model \|
	\| `dep.onnx`, `dep_char.onnx` \| dependency parser (arc and label scorers), its character model \|
	\| `pretrain_emb.npy` \| the word vectors the tagger and parser use \|
	\| `lemma.npz`, `mwt.npz` \| weights of the lemmatizer and multi-word token sequence models \|
	\| `config.json` \| vocabularies, model settings and the lemma dictionaries \|
	\| `NOTICE` \| where each part comes from and its licence \|

	sha256 of the archive: `3cde3a9b29b6da81e72ad3208874fcdbb1961df82130829d3b8f7dd01ef362b1`

	## How they were made

	`tools/stanza_ort/convert.py` in the sourcedrecall repository loads the models with
	Stanza 1.14.0 (`STANZA_PACKAGES=ours`, `tools/stanza_ort/pkgs.py`) and exports each
	network with `torch.onnx.export`; two layers were rewritten in exactly equivalent form
	for export (`nn.Bilinear` as an einsum, a boolean identity mask from `arange`).
	`tools/stanza_ort/parity.py` is the comparison above.

	## Licence and credit

	The model weights are released under CC BY-SA 4.0, the licence of the UD English-EWT
	data they are trained on. The conversion code and the runtime are Apache-2.0, in the
	sourcedrecall repository. The architecture, training code, and the tokenizer, multi-word
	token and lemma models are Stanza's, by the Stanford NLP Group (Apache-2.0).

	Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton and Christopher D. Manning. 2020.
	Stanza: A Python Natural Language Processing Toolkit for Many Human Languages.
	In Proceedings of ACL 2020: System Demonstrations.

	Natalia Silveira, Timothy Dozat, Marie-Catherine de Marneffe, Samuel Bowman, Miriam
	Connor, John Bauer and Christopher D. Manning. 2014. A Gold Standard Dependency Corpus
	for English. In Proceedings of LREC 2014. (UD English-EWT)

	Jeffrey Pennington, Richard Socher and Christopher D. Manning. 2014. GloVe: Global
	Vectors for Word Representation. In Proceedings of EMNLP 2014.