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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-any-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')]

Why spacy-persian?

  • ⚡ Performance – 96.24% POS · 97.91% Lemma · 85.15% LAS – competitive with English en_core_web_sm on syntax.
  • 🚀 Speed – ~9,250 words/sec on a standard CPU. No GPU required.
  • 📦 Flexibility – Choose fa_core_news_sm (13MB, syntax + NER) or fa_dep_news_sm (7.5MB, syntax-only).
  • 🔁 Reproducibility – Checksummed, versioned builds from UD_Persian-PerDT – no black boxes.
  • 🔌 Native spaCy – Drop-in replacement. spacy.load() works instantly with standard Doc objects.

Results

spacy-persian delivers production‑ready Persian NLP that stands alongside Hazm—the most popular Persian toolkit—while bringing the full power of the spaCy ecosystem.

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.

Metric Score Reference
TOKEN_ACC / TOKEN_F 99.96 / 99.11
TAG_ACC (XPOS) 95.96
POS_ACC (UPOS) 96.24
MORPH_ACC 96.29
LEMMA_ACC 97.91
SENTS_F 99.25
DEP_UAS 89.69 hazm+ParsBERT: 92.46
DEP_LAS 85.15 hazm+ParsBERT: 89.34
Speed ~9,250 words/s

Entities, fa_core_news_sm only, on the PerDT NER test split: ENTS_P 77.67, ENTS_R 66.87, ENTS_F 71.87.

Label F Train examples
LOC 80.24 4,954
DAT 74.45 1,323
MON 73.68 205
ORG 68.77 2,643
TIM 66.67 135
PER 65.29 4,847
PCT 57.14 121

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: 1h27m for the syntax components, 17 min for NER.

Install

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

Caveats

  • The entity labels are silver. They come from the treebank's own not-to-release/Dadegan with NER tag/ layer, which its README states was produced by the BERT-based Beheshti-NER tagger with manual corrections for recall. ENTS_F 71.87 is measured against a silver test split and partly reflects agreement with that tagger.
  • Three entity labels are thin. MON (205 training examples), TIM (135) and PCT (121) rest on 4 to 11 test entities each. PER, LOC, ORG and DAT have 1,300 or more.
  • 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. Entity spans were transferred onto this pipeline's tokenization by difflib at a 99.86% rate, and spans that could not be aligned exactly were dropped rather than guessed (scripts/transfer_perdt_ner.py).

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.

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