fa_dep_news_md

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

Install

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

Use

import spacy

nlp = spacy.load("fa_dep_news_md")
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	96.25
UPOS tag accuracy	96.64
Morphological features	96.64
Lemma accuracy	97.96
Unlabelled attachment (UAS)	90.52
Labelled attachment (LAS)	86.34
Sentence segmentation F	99.28

Throughput

Median of repeated nlp.pipe passes over the 146-document PerDT test split (23,825 tokens), timing the pipe only. Warmup pass discarded.

Device	Batch	Words/s
cpu (Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz)	32	9,528
gpu:0 (NVIDIA GeForce 940MX, 2048 MiB)	32	18,512

Other packages in this family

Pipeline	Tier	LAS	ENTS_F	Wheel
fa_dep_news_sm	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 (this one)	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
fa_floret static vectors (50k rows x 300d, floret mode, 400,000 Persian Wikipedia articles, trained with floret-torch)	Kiyarash Fazeli	CC BY-SA 4.0

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. This is the md tier: identical architecture to the sm pipeline plus static floret vectors (50,000 rows x 300 dimensions, minn=maxn=5, hash_count=2) trained with floret-torch on 400,000 Persian Wikipedia articles (fawiki, WikiExtractor --no-templates, spaCy blank('fa') tokenization, ~163M tokens). floret hashes subwords into a fixed table, so there are no out-of-vocabulary tokens and token.has_vector is always True. That matters for Persian, where inconsistent ZWNJ (U+200C) usage splits one word across several surface forms (mi-ravad written joined, with ZWNJ, or with a space) that a classic word-vector table would miss.