fa_dep_news_sm

Phazel/fa_dep_news_sm

fa_dep_news_sm is a token classification model from Phazel. Use it when you need labels on individual words, such as names. It is set up for spacy. The card lists the license as cc-by-sa-4.0.

Persian dependency pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, trainablelemmatizer, parser. No NER, see facorenewssm.

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.whl15.7 MB · 60%

At a glance

Task: Token Classification
Library: spacy
License: cc-by-sa-4.0
Access: Public
Created: Jul 31, 2026
Updated: Sep 19, 2026
SHA: 49d721ab

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Model card

From the Hugging Face model README

fa_dep_news_sm

Persian dependency pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, trainable_lemmatizer, parser. No NER, see fa_core_news_sm.

Install

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

Use

import spacy

nlp = spacy.load("fa_dep_news_sm")
doc = nlp("شرکت ایران خودرو اعلام کرد که تولید خود را افزایش می‌دهد.")

print([(t.text, t.pos_, t.tag_, t.lemma_, t.dep_) for t in doc])

Accuracy

Scored with spacy benchmark accuracy on the held-out PerDT test split.

Metric	Score
Tokenization accuracy	99.96
XPOS tag accuracy	95.96
UPOS tag accuracy	96.24
Morphological features	96.29
Lemma accuracy	97.91
Unlabelled attachment (UAS)	89.69
Labelled attachment (LAS)	85.15
Sentence segmentation F	99.25

Other packages in this family

Pipeline	Tier	LAS	ENTS_F	Wheel
`fa_dep_news_sm` (this one)	`sm`	85.15	-	7.9 MB
`fa_core_news_sm`	`sm`	85.15	71.87	13.5 MB
`fa_ent_news_sm`	`sm`	-	71.87	5.9 MB
`fa_dep_news_md`	`md`	86.34	-	62.6 MB
`fa_core_news_md`	`md`	86.34	74.71	68.5 MB
`fa_ent_news_md`	`md`	-	74.71	60.6 MB
`fa_dep_news_lg`	`lg`	86.60	-	229.3 MB
`fa_core_news_lg`	`lg`	86.60	75.94	235.2 MB
`fa_ent_news_lg`	`lg`	-	75.94	227.3 MB
`fa_core_news_trf`	`trf`	90.79	82.89	608.2 MB

Tiers: sm hash embeddings, no vectors, md 50k x 300d floret vectors, lg 200k x 300d floret vectors, trf fine-tuned transformer, GPU recommended.

Standalone vector tables, usable as --paths.vectors for your own training:

Vectors	Rows	Used by	Wheel
`fa_floret_400k`	50,000	`md` tier	54.5 MB
`fa_floret_full_wiki`	50,000	no shipped pipeline	54.9 MB
`fa_floret_wiki_200k`	200,000	`lg` tier	221.3 MB

Training scripts, configs and evaluation: https://github.com/Fazel94/spacy-persian.

Sources

Source	Author	Licence
UD_Persian-PerDT (PerUDT v1.0)	Mohammad Sadegh Rasooli, Pegah Safari, Amirsaeid Moloodi, Alireza Nourian	CC BY-SA 4.0
spaCy lang/fa language data (stop words originally from HAZM)	Explosion and spaCy contributors	MIT

Notes

Trained on UD_Persian-PerDT, licensed CC BY-SA 4.0; this pipeline is therefore distributed under CC BY-SA 4.0 with attribution to the treebank authors. Multiword tokens (pronominal clitics, enclitic copulas) were merged with spacy convert --merge-subtokens, so a small number of XPOS tags are composite (e.g. N_IANM_PR_JOPER) and ~1.5% of lemmas contain a space. doc.noun_chunks under-fires on this pipeline: spacy/lang/fa/syntax_iterators.py matches ClearNLP labels that do not exist in Universal Dependencies, see docs/upstream/fa-noun-chunks.md.

Task: Token Classification
Library: spacy
License: cc-by-sa-4.0
Languages: fa
Created: Jul 31, 2026
Updated: Sep 19, 2026