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Remove em dashes from lg tier docs and config headers

This commit is contained in:
Mohamad Fazeli 2026-08-12 22:07:49 +03:30
parent 9e8ed06361
commit f45d0db643
Signed by: fazel
GPG Key ID: 05E227BF4D6736DE
3 changed files with 12 additions and 12 deletions
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2
configs/fa_dep_news_lg.cfg
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@ -1,4 +1,4 @@
# fa_dep_news_lg — tagger, morphologizer, trainable_lemmatizer, parser, WITH the lg-tier
# fa_dep_news_lg: tagger, morphologizer, trainable_lemmatizer, parser, WITH the lg-tier
# static vectors.
#
# Byte-identical to configs/fa_dep_news_md.cfg. Only the vector table supplied at train time

2
configs/fa_ner_lg.cfg
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@ -1,4 +1,4 @@
# fa_ent_news_lg — Persian NER with the lg-tier static floret vectors.
# fa_ent_news_lg: Persian NER with the lg-tier static floret vectors.
#
# Identical to configs/fa_ner_md.cfg (which is identical to fa_ner_sm.cfg except
# include_static_vectors: true). Only the vector table supplied at train time via

20
docs/MODELS.md
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@ -289,7 +289,7 @@ each variant: it refuses to publish a `dep` pipeline containing `ner`, or a `cor
## 6. The `md` tier: floret static vectors
Built after the `sm` tier, from `fa_floret` — 50,000 rows x 300d, floret mode, `minn=maxn=5`,
Built after the `sm` tier, from `fa_floret`: 50,000 rows x 300d, floret mode, `minn=maxn=5`,
`hash_count=2`, trained on 400,000 Persian documents. The wheel is a vectors-only pipeline;
`scripts/unpack_vectors.py` unwraps it into a directory `--paths.vectors` can read, so nothing
needs pip-installing to train against it.
@ -354,7 +354,7 @@ or a Lambda cold start. Both tiers ship; pick per target.
## 7. The `lg` tier: bigger floret table, full pipeline
Built after `md`, from a new `fa_floret` table — 200,000 rows x 300d, floret mode,
Built after `md`, from a new `fa_floret` table: 200,000 rows x 300d, floret mode,
`minn=maxn=5`, `hash_count=2`, trained on the full Persian Wikipedia dump for 5 epochs (4x
the rows of `md`'s 50k-row table trained on 400k documents). Raw `.floret`/`.vec` and the
packaged spaCy wheel are at <https://huggingface.co/Phazel/fa-floret-wiki-vectors>. Unpacked
@ -377,10 +377,10 @@ table alone. Reproduce with `spacy project run lg`, or the tables alone with
| `DEP_UAS` | 89.69 | 90.52 | 90.96 | +1.27 | +0.44 |
| `DEP_LAS` | 85.15 | 86.34 | 86.60 | +1.45 | +0.26 |
`lg` beats `md` on every UD metric, same monotonic pattern as `md` beating `sm` in §6 — a
bigger, less collision-prone floret table keeps paying off, though the `md`-to-`lg` gains
(4x the vector rows) are smaller than the `sm`-to-`md` gains (going from none to 50k rows):
diminishing returns, as expected.
`lg` beats `md` on every UD metric, the same monotonic pattern as `md` beating `sm` in §6.
The bigger, less collision-prone floret table keeps paying off, though the `md`-to-`lg`
gains (4x the vector rows) are smaller than the `sm`-to-`md` gains (going from none to 50k
rows): diminishing returns, as expected.
### PerDT NER test split, `fa_ent_news_lg` (identical `ner` component embedded in `fa_core_news_lg`)
@ -416,18 +416,18 @@ vector table is ~240 MB uncompressed, so `fa_dep_news_lg` is a 219 MB wheel (vs
60 MB `md`), `fa_core_news_lg` 225 MB (vs 13 MB `sm`, 66 MB `md`), and `fa_ent_news_lg` alone
217 MB (vs 5.6 MB `sm`, 58 MB `md`). Training cost roughly doubled `md`'s: `dep_lg` ran to
early stop at step 12,000 of 20,000 over ~2h08m CPU wall time (vs `dep_md`'s single-digit
minutes territory implied by its architecture-identical config — `lg`'s extra time is
minutes territory implied by its architecture-identical config; `lg`'s extra time is
entirely the larger embedding table's per-step cost, not more steps). `ner_lg` early-stopped
at step 7,200, ~13 min, in line with `sm`/`md`.
`words/s` from `spacy benchmark accuracy` were noisier at this tier than `sm`-vs-`md`: dep/core
throughput dropped as expected (9,387 / 6,655 words/s vs `sm`'s 12,505 / 8,834, `md`'s
10,493 / 7,269 — the larger table costs real lookup time), but the standalone `ent_lg` run
10,493 / 7,269 words/s; the larger table costs real lookup time), but the standalone `ent_lg` run
showed 15,614 words/s, higher than `sm`/`md`'s ent runs despite an identical `ner`
architecture and the same larger table. Treat that one figure as single-run CPU contention
noise on shared hardware, not a real speedup, and re-benchmark before citing it.
For a 4x download over `md` (and up to 39x over `sm`) buying +1.45 DEP_LAS / +1.23 ENTS_F
over `md` (+1.45 DEP_LAS / +4.08 ENTS_F over `sm`), `lg` is a server/offline-batch pipeline,
not something to ship to a browser or a cold-start function. All three variants — `dep`,
`ent`, `core` — are built and evaluated at this tier, same as `md`.
not something to ship to a browser or a cold-start function. All three variants (`dep`,
`ent`, `core`) are built and evaluated at this tier, same as `md`.