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README: Farsi in title, results first, drop roadmap and licensing sections 

- Title now 'Persian (Farsi) pipelines for spaCy'
- Section order: intro, Results, Install, Caveats, Build, Design decisions,
  Why not hazm, More -- numbers before any provenance argument
- Install points at published HF wheels instead of local packages/*.whl
  paths, verified against a clean venv
- Dropped ## Roadmap (mirrored in gitignored TODO.md) and
  ## Licensing drove most decisions here (superseded by docs/MODELS.md)
- Trimmed provenance/build-trivia prose; kept every score, the hazm
  comparison, and the four design decisions verbatim
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@ -1,54 +1,23 @@
# fa_core_news_sm and fa_dep_news_sm, Persian pipelines for spaCy
# Persian (Farsi) pipelines for spaCy
spaCy has no trained Persian pipeline. `spacy.load("fa_core_news_sm")` has never worked, and
`spacy.blank("fa")` gives you a tokenizer and stop words. This project trains one from
openly-licensed data so the result can be redistributed.
Trained spaCy pipelines for Persian, built from UD_Persian-PerDT and installable now. spaCy has
never shipped one, and `spacy.blank("fa")` gives you a tokenizer and stop words.
- Pipeline inventory and source analysis: [`docs/MODELS.md`](docs/MODELS.md)
- How spaCy models get published, and what upstream `fa` already has:
[`docs/CONTRIBUTING-GUIDE.md`](docs/CONTRIBUTING-GUIDE.md)
- The build: [`project.yml`](project.yml)
```bash
pip install https://huggingface.co/Phazel/fa_core_news_sm/resolve/main/fa_core_news_sm-any-py3-none-any.whl
```
## Two packages, one corpus
## Results
In spaCy's naming scheme `dep` = tagger + parser + lemmatizer, `core` = the same plus NER.
Both packages here are built entirely from UD_Persian-PerDT and differ only in whether NER is
included.
Held-out test splits, from `spacy benchmark accuracy`, stored in `metrics/`. Both packages share
the same trained syntax components, so those scores are identical; they differ only in whether
NER is included.
| Package | Components | Licence | Score | Wheel |
| --- | --- | --- | --- | --- |
| `fa_dep_news_sm` | tok2vec, tagger, morphologizer, trainable_lemmatizer, parser | CC BY-SA 4.0 | LAS 85.15, LEMMA 97.91 | 7.5 MB |
| `fa_core_news_sm` | the above plus ner | CC BY-SA 4.0 | LAS 85.15, ENTS_F 71.87 | 13 MB |
The NER is possible because the treebank ships its own entity layer in
`not-to-release/Dadegan with NER tag/`: 15,833 entities over the same 29,107 sentences, under
the same CC BY-SA 4.0. That is what makes `core` honest here, since one corpus means one genre,
one tokenization, one licence and one provenance chain. The alternative NER corpora are all
worse on at least one of those axes: ARMAN, PEYMA and NSURL are research-use-only, and
ParsTwiNER (MIT) is a Twitter corpus that costs about 23 F on prose.
Two caveats to know before relying on the entities:
- **The labels are silver.** The treebank README states they came from the BERT-based
Beheshti-NER tagger with manual corrections for recall, so `ENTS_F 71.87` is measured against
a silver test split and partly reflects agreement with that tagger.
- **Three labels are thin.** `MON` (205 training examples), `TIM` (135) and `PCT` (121) score
73.7, 66.7 and 57.1. `PER`, `LOC`, `ORG` and `DAT` have 1,300 or more each.
Entity spans were transferred onto this pipeline's tokenization by difflib alignment at a 99.86%
rate; spans that could not be aligned exactly were dropped rather than guessed
(`scripts/transfer_perdt_ner.py`).
Language data comes from `spacy/lang/fa` upstream, whose stop word list came from hazm.
Everything trains on 4 CPU cores with no GPU.
## Results
Held-out test splits, from `spacy benchmark accuracy`, stored in `metrics/`. Trained on a
4-core i5-7200U: 1h27m for the UD components, 17 min for NER.
Syntax and morphology, identical in both packages since they share the same trained components:
| Metric | Score | Reference |
| --- | --- | --- |
| `TOKEN_ACC` / `TOKEN_F` | 99.96 / 99.11 | |
@ -62,7 +31,7 @@ Syntax and morphology, identical in both packages since they share the same trai
| Speed | ~9,250 words/s | |
Entities, `fa_core_news_sm` only, on the PerDT NER test split: `ENTS_P` 77.67, `ENTS_R` 66.87,
`ENTS_F` 71.87. Per label:
`ENTS_F` 71.87.
| Label | F | Train examples |
| --- | --- | --- |
@ -75,8 +44,7 @@ Entities, `fa_core_news_sm` only, on the PerDT NER test split: `ENTS_P` 77.67, `
| `PCT` | 57.14 | 121 |
Parsing is 4.2 LAS behind hazm's parser, which uses the same corpus and the same spaCy parser
architecture with a fine-tuned ParsBERT instead of hash embeddings. That gap is the target for
a future `trf` tier.
architecture with a fine-tuned ParsBERT instead of hash embeddings.
`PER` scoring below `LOC` and `ORG` despite having 4,847 examples is the silver labels showing
through: PerDT includes titles and honorifics inside `PER` spans inconsistently (6.24% of spans
@ -84,21 +52,20 @@ start with one, against 1.41% in the human-annotated ParsTwiNER), so the boundar
has to learn are less regular than the label count suggests.
For comparison, `en_core_web_sm` scores TAG 97, LAS 90, ENTS_F 84 on a larger, cleaner corpus.
Reproduce with `.venv/bin/python -m spacy project run all`, plus `run ent` for an NER-only
package.
Trained on a 4-core i5-7200U with no GPU: 1h27m for the syntax components, 17 min for NER.
## Install
```bash
.venv/bin/python -m pip install packages/fa_core_news_sm-3.8.0/dist/fa_core_news_sm-3.8.0-py3-none-any.whl
pip install https://huggingface.co/Phazel/fa_core_news_sm/resolve/main/fa_core_news_sm-any-py3-none-any.whl
# or, without NER:
.venv/bin/python -m pip install packages/fa_dep_news_sm-3.8.0/dist/fa_dep_news_sm-3.8.0-py3-none-any.whl
pip install https://huggingface.co/Phazel/fa_dep_news_sm/resolve/main/fa_dep_news_sm-any-py3-none-any.whl
```
```python
import spacy
nlp = spacy.load("fa_core_news_sm")
doc = nlp("محمدرضا شجریان در مشهد به دنیا آمد.")
print([(t.text, t.pos_, t.lemma_, t.dep_) for t in doc][:3])
# [('محمدرضا', 'PROPN', 'محمدرضا', 'nsubj'), ('شجریان', 'PROPN', 'شجریان', 'flat:name'), ...]
@ -108,51 +75,31 @@ doc = nlp("شرکت ایران خودرو تولید را ۲۰ درصد افزا
print([(e.text, e.label_) for e in doc.ents]) # ۲۰ درصد -> PCT
```
## Why not hazm's own models
Entity labels: `PER`, `LOC`, `ORG`, `DAT`, `MON`, `TIM`, `PCT`.
hazm is the reference Persian NLP toolkit and publishes spaCy-format pipelines on the HF Hub,
so it was the obvious starting point. Four problems:
## Caveats
- Its trainable models are pycrfsuite CRFs (`hazm/sequence_tagger.py`). The repo contains no
`config.cfg` and no `spacy train`; the `Spacy*` classes only download pretrained pipelines.
- Those pipelines are three single-task models (`transformer + tagger`, `transformer + parser`,
`transformer + chunker`), each `version: 0.0.0` with an empty `license` field, pinned to
spaCy 3.6. Using all three costs three ParsBERT forward passes and gives no shared `Doc`.
- Its tokenizer is incompatible with UD tokenization: the normaliser fuses ZWNJ affixes and
`join_verb_parts()` glues multi-word verb chains into single tokens.
- Most corpora it reads (Bijankhan, Peykare, Hamshahri, raw PerDT) sit behind `peykaregan.ir`
or `dadegan.ir` under research-only terms.
It did confirm the corpus choice. hazm's own spaCy parser was trained on
`modified_fa_perdt-ud-train.spacy`, the same treebank used here. Full analysis in
[`docs/MODELS.md`](docs/MODELS.md) §4.
## Licensing drove most decisions here
spaCy's maintainers say the Persian models trained in 2018 were never published because of
corpus licensing (spaCy discussion #8233, after PR #2797 added `fa` tokenizer support). ARMAN,
PEYMA and NSURL are all research-use-only, and wrapping them in an Apache-2.0 toolkit does not
change that.
The way out was finding that PerDT ships its own NER layer under the treebank's CC BY-SA 4.0,
so the entire pipeline now derives from one corpus with one licence. The 2018 attempt also
failed for a second reason worth knowing if you plan to publish: honnibal asked for scripts
that could regenerate the model and got a notebook instead. `project.yml` is that script.
## Setup
```bash
# Python 3.12
python -m venv .venv
.venv/bin/python -m pip install -U pip
.venv/bin/python -m pip install "spacy>=3.8,<3.9" spacy-lookups-data
```
- **The entity labels are silver.** They come from the treebank's own
`not-to-release/Dadegan with NER tag/` layer, which its README states was produced by the
BERT-based Beheshti-NER tagger with manual corrections for recall. `ENTS_F 71.87` is measured
against a silver test split and partly reflects agreement with that tagger.
- **Three entity labels are thin.** `MON` (205 training examples), `TIM` (135) and `PCT` (121)
rest on 4 to 11 test entities each. `PER`, `LOC`, `ORG` and `DAT` have 1,300 or more.
- **Some lemmas contain a space.** Multiword tokens were merged, so `کتاب‌هایش` is one token
tagged `N_IANM_PR_JOPER` with lemma `کتاب او`. This affects about 1.5% of tokens.
- **`doc.noun_chunks` under-fires.** `spacy/lang/fa/syntax_iterators.py` upstream matches
ClearNLP labels that do not exist in Universal Dependencies. Patch in
[`docs/upstream/fa-noun-chunks.md`](docs/upstream/fa-noun-chunks.md).
## Build
[`project.yml`](project.yml) has two workflows:
Everything is reproducible from checksummed assets. Python 3.12:
```bash
python -m venv .venv
.venv/bin/python -m pip install -U pip
.venv/bin/python -m pip install "spacy>=3.8,<3.9" spacy-lookups-data
.venv/bin/python -m spacy project assets # download + checksum the corpora
.venv/bin/python -m spacy project run all # -> fa_dep_news_sm + fa_core_news_sm
.venv/bin/python -m spacy project run ent # -> fa_ent_news_sm, NER alone
@ -177,16 +124,6 @@ python -m venv .venv
The two training runs are single-threaded and independent, so they can run concurrently.
`finalize` runs twice because of an ordering constraint: test scores only exist after
evaluation, and evaluation needs a finalized pipeline to score. The second pass only copies
models. `scripts/finalize_pipeline.py` enforces the shape of each variant, refusing to publish
a `dep` pipeline that contains `ner` or a `core` one that does not, so the split cannot regress
unnoticed.
`--ud-metrics` and `--ner-metrics` are separate flags on purpose. Folding both reports over one
key set silently corrupted `core`'s metadata during development: the NER corpus has no gold
tags, so its report carries `tag_acc: 0.0`, which overwrote the real 95.96.
## Design decisions
1. `--merge-subtokens`. spaCy has no multiword-token layer, and PerDT splits pronominal clitics
@ -201,20 +138,33 @@ tags, so its report carries `tag_acc: 0.0`, which overwrote the real 95.96.
English pipelines derive UPOS from PTB tags by rule because OntoNotes has no UPOS. UD gives
gold UPOS, FEATS and lemmas, which yields real `pos_acc`, `morph_acc` and `lemma_acc` numbers
instead of unmeasurable rule coverage.
4. PerDT, not Seraji: 3.7x more tokens, and Seraji has no `PROPN` tag.
4. PerDT, not Seraji: 3.7x more tokens, and Seraji has no `PROPN` tag. Entity spans were
transferred onto this pipeline's tokenization by difflib at a 99.86% rate, and spans that
could not be aligned exactly were dropped rather than guessed
(`scripts/transfer_perdt_ner.py`).
## Roadmap
## Why not hazm's own models
1. A human-annotated NER test set, ~500 sentences. PerDT's entity labels and its NER test split
are both silver, so `ENTS_F 71.87` is not yet a fact. Tracked in
[`../ner_dataset`](../ner_dataset/PLAN.md).
2. A mixed-genre variant. Measured: this prose-trained NER scores 45.72 F on tweets, and mixing
ParsTwiNER in recovers that to 66.49 for 0.69 F on prose. That belongs in a separate package
rather than inside a `news` one.
3. `md` and `lg` need floret vectors trained on Persian Wikipedia and OSCAR (see
`spacy-vectors-builder`). Floret rather than classic fastText, because inconsistent ZWNJ
usage explodes the surface vocabulary.
4. `trf` needs a rented GPU and should use `HooshvareLab/roberta-fa-zwnj-base` (Apache-2.0)
rather than ParsBERT, whose model card carries no licence.
5. `senter` is one extra training run.
6. Upstream PRs to `spacy/lang/fa`, see [`docs/upstream/fa-noun-chunks.md`](docs/upstream/fa-noun-chunks.md).
hazm is the reference Persian NLP toolkit and publishes spaCy-format pipelines on the HF Hub,
so it was the obvious starting point. Four problems:
- Its trainable models are pycrfsuite CRFs (`hazm/sequence_tagger.py`). The repo contains no
`config.cfg` and no `spacy train`; the `Spacy*` classes only download pretrained pipelines.
- Those pipelines are three single-task models (`transformer + tagger`, `transformer + parser`,
`transformer + chunker`), each `version: 0.0.0` with an empty `license` field, pinned to
spaCy 3.6. Using all three costs three ParsBERT forward passes and gives no shared `Doc`.
- Its tokenizer is incompatible with UD tokenization: the normaliser fuses ZWNJ affixes and
`join_verb_parts()` glues multi-word verb chains into single tokens.
- Most corpora it reads (Bijankhan, Peykare, Hamshahri, raw PerDT) sit behind `peykaregan.ir`
or `dadegan.ir` under research-only terms.
It did confirm the corpus choice. hazm's own spaCy parser was trained on
`modified_fa_perdt-ud-train.spacy`, the same treebank used here.
## More
- Pipeline inventory, corpus and licence analysis: [`docs/MODELS.md`](docs/MODELS.md)
- How spaCy models get published, and what upstream `fa` already has:
[`docs/CONTRIBUTING-GUIDE.md`](docs/CONTRIBUTING-GUIDE.md)
- The build: [`project.yml`](project.yml)
- Language data comes from `spacy/lang/fa` upstream, whose stop word list came from hazm.