No trained Persian pipeline exists for spaCy: spacy.load("fa_core_news_sm") has
never worked. This builds one from openly-licensed data so the result can actually
be redistributed.
Test scores (held-out splits): TAG 95.96, POS 96.24, MORPH 96.29, LEMMA 97.91,
UAS 89.69, LAS 85.15, ENTS_F 67.22. ~9,250 words/s, 13 MB wheel. 1h27m on 4 CPU
cores, no GPU.
Corpus choices, with evidence:
- UD_Persian-PerDT (CC BY-SA 4.0) over Seraji: 3.7x more tokens (452k vs 121k) and
Seraji has no PROPN tag at all. hazm's own spaCy parser used PerDT too.
- ParsTwiNER (MIT) for NER. ARMAN/PEYMA/NSURL are research-only; spaCy never
shipped the 2018 Persian models precisely because of corpus licensing. Cost: NER
is trained on tweets, so it is the weak component.
- --merge-subtokens: measured token F 0.9887 vs 0.9823 split. Without it, 1.5% of
gold token boundaries are unreachable by the tokenizer we ship.
- morphologizer + trainable_lemmatizer instead of the English attribute_ruler +
rule lemmatizer, because UD gives gold UPOS/FEATS/lemmas to train and measure on.
The ner component embeds its own tok2vec rather than using a Tok2VecListener, so it
can be sourced into the core pipeline after being trained on a separate corpus.
Also documents an upstream bug: spacy/lang/fa/syntax_iterators.py matches ClearNLP
labels (dobj, pobj, nsubjpass, attr, dative) that do not exist in UD, so
doc.noun_chunks returns bare head nouns (1.31 vs 2.77 tokens/chunk). Patch and
tests in docs/upstream/fa-noun-chunks.md.
Mohamad Fazeli
92fc1c3002