1import spacy
2nlp = spacy.load("fa_dep_news_md")3doc = nlp("شرکت ایران خودرو اعلام کرد که تولید خود را افزایش میدهد.")4print([(t.text, t.pos_, t.lemma_, t.dep_)for t in doc])5print([(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
Throughput
Median of repeated nlp.pipe passes over the 146-document PerDT test
split (23,825 tokens), timing the pipe only. Warmup pass discarded.
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 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.
