fa_core_news_md

Persian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, trainable_lemmatizer, parser, ner. Entity labels: PER, LOC, ORG, DAT, MON, TIM, PCT.

Install

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

import spacy
nlp = spacy.load("fa_core_news_md")
doc = nlp("شرکت ایران خودرو اعلام کرد که تولید خود را افزایش می‌دهد.")
print([(t.text, t.pos_, t.lemma_, t.dep_) for t in doc])
print([(e.text, e.label_) for e in doc.ents])

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
NER precision	76.56
NER recall	72.95
NER F-score	74.71

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
gpu:0 (NVIDIA GeForce 940MX, 2048 MiB)	32	7,349
cpu (Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz)	32	5,856

Sources

Source	Author	Licence
UD_Persian-PerDT (PerUDT v1.0)	Mohammad Sadegh Rasooli, Pegah Safari, Amirsaeid Moloodi, Alireza Nourian	CC BY-SA 4.0
UD_Persian-PerDT NER layer (not-to-release/Dadegan with NER tag/)	PerDT authors, tagged with Beheshti-NER (Taher, Hoseini, Shamsfard 2020)	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, 400k Persian documents)	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. The ner component is trained on the NER layer shipped in UD_Persian-PerDT's not-to-release/ directory, so it shares the treebank's genre, tokenization and licence. Those labels are SILVER: the treebank README states they were produced by the BERT-based Beheshti-NER tagger (Taher et al., 2020) with manual corrections to extend recall. They were transferred onto this pipeline's tokenization by difflib alignment at a 99.86% transfer rate (scripts/transfer_perdt_ner.py); spans that could not be aligned exactly were dropped rather than guessed. Labels PER, LOC, ORG and DAT have 1,300 or more training examples each; MON (205), TIM (135) and PCT (121) are thin and their scores in performance.ents_per_type should be read before relying on them. 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 on 400,000 Persian documents. 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.