Persian (Farsi) pipelines for spaCy

Trained spaCy pipelines for Persian, installable now. spaCy has never shipped an official one, and spacy.blank("fa") only gives you a tokenizer and stop words. Choose between fa_core_news_sm (full syntax + NER) or fa_dep_news_sm (syntax only).

pip install https://huggingface.co/Phazel/fa_core_news_sm/resolve/main/fa_core_news_sm-3.8.0-py3-none-any.whl

>>> import spacy
>>> nlp = spacy.load("fa_core_news_sm")

>>> doc = nlp("محمدرضا شجریان در مشهد به دنیا آمد.")
>>> [(t.text, t.pos_, t.lemma_, t.dep_) for t in doc][:2]
[('محمدرضا', 'PROPN', 'محمدرضا', 'nsubj'), ('شجریان', 'PROPN', 'شجریان', 'flat:name')]
>>> doc.ents
(محمدرضا شجریان, مشهد)

>>> doc = nlp("شرکت ایران خودرو تولید را ۲۰ درصد افزایش می‌دهد.")
>>> [(e.text, e.label_) for e in doc.ents]
[('ایران خودرو', 'ORG'), ('۲۰ درصد', 'PCT')]

Results

Compared against Hazm (the most-used Persian toolkit) and en_core_web_sm (English reference).

Metric	spacy-persian fa_core_news_sm	Hazm (Persian toolkit)	en_core_web_sm (English reference)
POS Accuracy (UPOS)	96.24%	~95.69%¹	97.21%²
Lemma Accuracy	97.91%	89.9%¹	—
Dependency LAS	85.15%	85.6%¹	91.85%²
NER F-score	71.87%	—	83.80%²
Package Size	13 MB (syntax+NER) 7.5 MB (syntax-only)	~7 MB	12 MB

¹ Hazm scores from its official README ² en_core_web_sm scores from spaCy's official model card

Note on comparability: These benchmarks come from different evaluation sets, treebanks, and test splits.

From spacy benchmark accuracy, stored in metrics/.

Package	Components	Licence	Score	Wheel
fa_dep_news_sm	tok2vec, tagger, morphologizer, trainable_lemmatizer, parser	CC BY-SA 4.0	LEMMA 97.91	7.5 MB
fa_core_news_sm	the above plus ner	CC BY-SA 4.0	ENTS_F 71.87	13 MB
fa_ent_news_sm	ner alone (own embedded tok2vec)	CC BY-SA 4.0	ENTS_F 71.87	5.6 MB
fa_dep_news_md	same as fa_dep_news_sm, plus floret vectors	CC BY-SA 4.0	LEMMA 97.96	62 MB
fa_core_news_md	same as fa_core_news_sm, plus floret vectors	CC BY-SA 4.0	ENTS_F 74.71	68 MB
fa_ent_news_md	ner alone (own embedded tok2vec), plus floret vectors	CC BY-SA 4.0	ENTS_F 74.71	58 MB

The md tier adds a 50k x 300d floret vector table trained on 400k Persian documents. Its config differs from sm by exactly one line (include_static_vectors), so the columns below isolate what the vectors buy. Full breakdown in docs/MODELS.md §6.

Metric	sm	md	lg	trf	Reference
TOKEN_ACC / TOKEN_F	99.96 / 99.11	99.96 / 99.11	99.96 / 99.11	99.96 / 99.11
TAG_ACC (XPOS)	95.96	96.25	96.55	97.62
POS_ACC (UPOS)	96.24	96.64	96.68	97.63
MORPH_ACC	96.29	96.64	96.70	97.82
LEMMA_ACC	97.91	97.96	98.08	97.31
SENTS_F	99.25	99.28	99.18	97.35
DEP_UAS	89.69	90.52	90.96	93.87	hazm+ParsBERT: 92.46
DEP_LAS	85.15	86.34	86.60	90.79	hazm+ParsBERT: 89.34
ENTS_P	77.67	76.56	81.51	84.06
ENTS_R	66.87	72.95	71.09	81.76
ENTS_F	71.87	74.71	75.94	82.89
Speed (940MX, batch 32)	10,235 words/s	9,058 words/s	9,215 words/s	see §Throughput
Wheel size	13.5 MB	68.5 MB	235 MB	608 MB

trf fine-tunes ParsBERT and wins everywhere except lemmatization and sentence segmentation, where lg's edit-tree lemmatizer over floret subwords still leads. It is the only tier to pass the hazm+ParsBERT DEP_LAS reference of 89.34. It needs a GPU and its encoder has no stated licence, so it is not redistributable; docs/MODELS.md §8 has both caveats.

Entity scores are fa_core_news_* on the PerDT NER test split; per-label breakdown and caveats are in Named entity recognition.

For comparison, en_core_web_sm scores TAG 97, LAS 90, ENTS_F 84 on a larger, cleaner corpus. Trained on a 4-core i5-7200U with no GPU: sm 1h27m syntax + 17 min NER, md 1h54m syntax

• 25 min NER (the two md runs overlapped, so wall clock overstates each).

Throughput

Median of repeated nlp.pipe passes over the 146-document PerDT test split (23,825 tokens), timing the pipe only, warmup discarded. Reproduce with python scripts/benchmark_throughput.py <model> --gpu-id <n>; raw records are in metrics/throughput-*.json.

Tier	CPU, i5-7200U	GPU, GeForce 940MX	GPU, Tesla T4
sm	5,484	10,235
md	5,408	9,058
lg	4,715	9,215
trf	187		8,320

The trf tier is a different kind of thing: 187 words/s on the same laptop CPU that runs sm at 5,484, so about 29x slower. On a T4 it reaches 8,320, and on that VM's own Xeon it manages 336, a 25x GPU speedup. Treat GPU as a requirement rather than an optimization. The 940MX cannot run trf at all, since current PyTorch wheels have dropped its sm_50 compute capability.

sm, md and lg are within about 15% of each other on CPU, which is smaller than the gap in vector-table size suggests: the tok2vec is not the bottleneck, the parser and lemmatizer are. Run-to-run spread on the laptop is roughly +/-10% depending on thermal state, so treat differences under that as noise.

Named entity recognition

Seven labels: LOC, PER, ORG, DAT, MON, TIM, PCT. They come from PerDT's own not-to-release/Dadegan with NER tag/ layer, transferred onto this pipeline's tokenization by difflib at a 99.86% alignment rate; spans that could not be aligned exactly were dropped rather than guessed (scripts/transfer_perdt_ner.py). That layer is silver: PerDT's README states it was produced by the BERT-based Beheshti-NER tagger with manual corrections for recall, so the ENTS_F numbers below partly reflect agreement with that tagger, not with human annotation.

ner runs standalone with its own embedded tok2vec (fa_ent_news_sm, fa_ent_news_md), or bundled into fa_core_news_sm/fa_core_news_md alongside the syntax pipeline.

Label	Gold in test	sm F	md F	Train examples
LOC	273	80.24	84.05	4,954
PER	297	65.29	68.18	4,847
ORG	144	68.77	70.25	2,643
DAT	69	74.45	76.19	1,323
MON	10	73.68	84.21	205
TIM	9	66.67	66.67	135
PCT	4	57.14	33.33	121

MON, TIM and PCT have single-digit support in the test split, so their deltas are one or two entities changing hands, not signal. PER, LOC and ORG carry the split and all improve with floret vectors; the md gain over sm (ENTS_F 71.87 to 74.71) is almost entirely recall (+6.08), the lexical prior static vectors give rare proper nouns that hash embeddings never had.

Install

pip install https://huggingface.co/Phazel/fa_core_news_sm/resolve/main/fa_core_news_sm-3.8.0-py3-none-any.whl
# or, without NER:
pip install https://huggingface.co/Phazel/fa_dep_news_sm/resolve/main/fa_dep_news_sm-3.8.0-py3-none-any.whl

Caveats

• Some lemmas contain a space. Multiword tokens were merged, so کتاب‌هایش is one token tagged N_IANM_PR_JOPER with lemma کتاب او. This affects about 1.5% of tokens.
• doc.noun_chunks under-fires. spacy/lang/fa/syntax_iterators.py upstream matches ClearNLP labels that do not exist in Universal Dependencies. Patch in docs/upstream/fa-noun-chunks.md.

Build

Everything is reproducible from checksummed assets. Python 3.12:

python -m venv .venv
.venv/bin/python -m pip install -U pip
.venv/bin/python -m pip install "spacy>=3.8,<3.9" spacy-lookups-data

.venv/bin/python -m spacy project assets      # download + checksum the corpora
.venv/bin/python -m spacy project run all     # -> fa_dep_news_sm + fa_core_news_sm
.venv/bin/python -m spacy project run ent     # -> fa_ent_news_sm, NER alone

Command	What it does
inspect	annotation coverage of the treebanks (scripts/inspect_treebanks.py)
convert-ud	CoNLL-U to DocBin with --merge-subtokens, plus the tokenizer-agreement report
transfer-ner	align PerDT's NER layer onto that tokenization by difflib (scripts/transfer_perdt_ner.py)
convert-ner	transferred IOB2 to DocBin
debug-data, debug-data-ner	spacy debug data on both corpora before spending CPU
train-dep	tagger + morphologizer + trainable_lemmatizer + parser
train-ner	the ner component, with its own embedded tok2vec
finalize-dep	write fa_dep_news_sm metadata: sources, licence, notes (scripts/finalize_pipeline.py)
evaluate-dep	spacy benchmark accuracy on the held-out UD test split
assemble-core	source ner into the dep pipeline to produce fa_core_news_sm
evaluate-core	score the assembled pipeline on both test splits
finalize-meta	re-run finalize on both, folding test scores into meta.json["performance"]
package	build wheels + sdists for both
smoke	run both pipelines over Persian text and print every annotation layer

The two training runs are single-threaded and independent, so they can run concurrently.

Design decisions

1. --merge-subtokens. spaCy has no multiword-token layer, and PerDT splits pronominal clitics (پدرم into پدر + م). Measured on dev, merging gives token F 0.9887 against 0.9823 for the split version, costing 34 composite XPOS tags on 1.5% of tokens. Without it, 1.5% of gold tokens are boundaries the shipped tokenizer can never produce. See scripts/tokenization_report.py.
2. ner carries its own tok2vec. A Tok2VecListener only resolves inside the pipeline it was trained in, so a listener-based component cannot be sourced elsewhere. configs/fa_ner_sm.cfg embeds the tok2vec instead, as en_core_web_sm does.
3. morphologizer + trainable_lemmatizer instead of attribute_ruler + rule lemmatizer. The English pipelines derive UPOS from PTB tags by rule because OntoNotes has no UPOS. UD gives gold UPOS, FEATS and lemmas, which yields real pos_acc, morph_acc and lemma_acc numbers instead of unmeasurable rule coverage.
4. PerDT, not Seraji: 3.7x more tokens, and Seraji has no PROPN tag.

Why not hazm's own models

hazm is the reference Persian NLP toolkit and publishes spaCy-format pipelines on the HF Hub, so it was the obvious starting point. Four problems:

• Its trainable models are pycrfsuite CRFs (hazm/sequence_tagger.py). The repo contains no config.cfg and no spacy train; the Spacy* classes only download pretrained pipelines.
• Those pipelines are three single-task models (transformer + tagger, transformer + parser, transformer + chunker), each version: 0.0.0 with an empty license field, pinned to spaCy 3.6. Using all three costs three ParsBERT forward passes and gives no shared Doc.
• Its tokenizer is incompatible with UD tokenization: the normaliser fuses ZWNJ affixes and join_verb_parts() glues multi-word verb chains into single tokens.
• Most corpora it reads (Bijankhan, Peykare, Hamshahri, raw PerDT) sit behind peykaregan.ir or dadegan.ir under research-only terms.

It did confirm the corpus choice. hazm's own spaCy parser was trained on modified_fa_perdt-ud-train.spacy, the same treebank used here.

More

• Pipeline inventory, corpus and licence analysis: docs/MODELS.md
• How spaCy models get published, and what upstream fa already has: docs/CONTRIBUTING-GUIDE.md
• The build: project.yml
• خلاصهٔ فارسی: README.fa.md
• Language data comes from spacy/lang/fa upstream, whose stop word list came from hazm.