Complete lg tier: fa_dep_news_lg / fa_core_news_lg

Same 200k-row floret table as fa_ent_news_lg, now with dep/core too.

- configs/fa_dep_news_lg.cfg (fa_dep_news_md.cfg unchanged except vectors)
- project.yml: lg workflow expanded to train-dep-lg, finalize-dep-lg,
  assemble-core-lg, evaluate-lg, finalize-meta-lg, package-lg,
  smoke-lg (mirrors the md tier's dep/core commands)
- scripts/compare_tiers.py: dep/core UD test groups now 3-way (sm/md/lg)
- scripts/finalize_pipeline.py: FLORET_LG url points at the published
  vectors, https://huggingface.co/Phazel/fa-floret-wiki-vectors

UD test: DEP_UAS 90.96 (sm 89.69, md 90.52), DEP_LAS 86.60 (sm 85.15,
md 86.34). NER unchanged from the earlier fa_ent_news_lg run, ENTS_F
75.94. docs/MODELS.md §7 rewritten from ent-only to the full tier.

configs/fa_dep_news_lg.cfg

@@ -0,0 +1,231 @@
+# 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
+# via --paths.vectors differs: fa_floret, 200k rows x 300d, floret mode, trained on the full
+# Persian Wikipedia dump for 5 epochs (assets/vectors/fa_floret_lg), vs md's 50k rows x 300d
+# trained on 400k Persian documents. Seed, widths, rows, batcher, patience, eval_frequency all
+# held constant so the delta measures the vector table and nothing else.
+#
+# No `ner` here by design; see configs/fa_ner_lg.cfg and project.yml.
+
+[paths]
+train = null
+dev = null
+vectors = null
+init_tok2vec = null
+
+[system]
+gpu_allocator = null
+seed = 0
+
+[nlp]
+lang = "fa"
+pipeline = ["tok2vec", "tagger", "morphologizer", "trainable_lemmatizer", "parser"]
+batch_size = 1000
+disabled = []
+before_creation = null
+after_creation = null
+after_pipeline_creation = null
+
+[corpora]
+
+[training]
+dev_corpus = "corpora.dev"
+train_corpus = "corpora.train"
+seed = ${system.seed}
+gpu_allocator = ${system.gpu_allocator}
+dropout = 0.1
+accumulate_gradient = 1
+patience = 1600
+max_epochs = 0
+max_steps = 20000
+eval_frequency = 400
+frozen_components = []
+annotating_components = []
+before_to_disk = null
+before_update = null
+
+[initialize]
+vectors = ${paths.vectors}
+init_tok2vec = ${paths.init_tok2vec}
+vocab_data = null
+lookups = null
+before_init = null
+after_init = null
+
+[components]
+
+[pretraining]
+
+[nlp.tokenizer]
+@tokenizers = "spacy.Tokenizer.v1"
+
+[nlp.vectors]
+@vectors = "spacy.Vectors.v1"
+
+[corpora.train]
+@readers = "spacy.Corpus.v1"
+path = ${paths.train}
+max_length = 0
+gold_preproc = false
+limit = 0
+augmenter = null
+
+[corpora.dev]
+@readers = "spacy.Corpus.v1"
+path = ${paths.dev}
+max_length = 0
+gold_preproc = false
+limit = 0
+augmenter = null
+
+[training.optimizer]
+@optimizers = "Adam.v1"
+beta1 = 0.9
+beta2 = 0.999
+L2_is_weight_decay = true
+L2 = 0.01
+grad_clip = 1.0
+use_averages = false
+eps = 1e-08
+learn_rate = 0.001
+
+[training.batcher]
+@batchers = "spacy.batch_by_words.v1"
+discard_oversize = false
+tolerance = 0.2
+get_length = null
+
+[training.logger]
+@loggers = "spacy.ConsoleLogger.v1"
+progress_bar = false
+
+[training.score_weights]
+tag_acc = 0.25
+pos_acc = 0.12
+tag_micro_p = null
+tag_micro_r = null
+tag_micro_f = null
+morph_acc = 0.12
+morph_per_feat = null
+lemma_acc = 0.25
+dep_uas = 0.12
+dep_las = 0.12
+dep_las_per_type = null
+sents_p = null
+sents_r = null
+sents_f = 0.0
+
+[initialize.tokenizer]
+
+[initialize.components]
+
+[components.tok2vec]
+factory = "tok2vec"
+
+[components.tagger]
+factory = "tagger"
+label_smoothing = 0.05
+overwrite = false
+neg_prefix = "!"
+
+[components.morphologizer]
+factory = "morphologizer"
+label_smoothing = 0.05
+overwrite = true
+extend = false
+
+[components.trainable_lemmatizer]
+factory = "trainable_lemmatizer"
+backoff = "orth"
+min_tree_freq = 3
+overwrite = false
+top_k = 1
+
+[components.parser]
+factory = "parser"
+moves = null
+update_with_oracle_cut_size = 100
+learn_tokens = false
+min_action_freq = 30
+
+[training.batcher.size]
+@schedules = "compounding.v1"
+start = 100
+stop = 1000
+compound = 1.001
+t = 0.0
+
+[components.tok2vec.model]
+@architectures = "spacy.Tok2Vec.v2"
+
+[components.tagger.model]
+@architectures = "spacy.Tagger.v2"
+nO = null
+normalize = false
+
+[components.tagger.scorer]
+@scorers = "spacy.tagger_scorer.v1"
+
+[components.morphologizer.model]
+@architectures = "spacy.Tagger.v2"
+nO = null
+normalize = false
+
+[components.morphologizer.scorer]
+@scorers = "spacy.morphologizer_scorer.v1"
+
+[components.trainable_lemmatizer.model]
+@architectures = "spacy.Tagger.v2"
+nO = null
+normalize = false
+
+[components.trainable_lemmatizer.scorer]
+@scorers = "spacy.lemmatizer_scorer.v1"
+
+[components.parser.model]
+@architectures = "spacy.TransitionBasedParser.v2"
+state_type = "parser"
+extra_state_tokens = false
+hidden_width = 128
+maxout_pieces = 3
+use_upper = true
+nO = null
+
+[components.parser.scorer]
+@scorers = "spacy.parser_scorer.v1"
+
+[components.tok2vec.model.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = ${components.tok2vec.model.encode.width}
+attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
+rows = [5000, 1000, 2500, 2500]
+include_static_vectors = true
+
+[components.tok2vec.model.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 96
+depth = 4
+window_size = 1
+maxout_pieces = 3
+
+[components.tagger.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode.width}
+upstream = "*"
+
+[components.morphologizer.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode.width}
+upstream = "*"
+
+[components.trainable_lemmatizer.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode.width}
+upstream = "*"
+
+[components.parser.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode.width}
+upstream = "*"

docs/MODELS.md

@@ -44,8 +44,9 @@ pipelines such as `de_core_news_sm` as `news`.
 | `fa_core_web_sm` | same as core, mixed-genre training data | hash embeddings | not built; would add ParsTwiNER to cover social media |
 | `fa_dep_news_md` | same as `fa_dep_news_sm` | floret, 50k rows / 300d | built, shipping |
 | `fa_core_news_md` | same as `fa_core_news_sm` | floret, 50k rows / 300d | built, shipping |
+| `fa_dep_news_lg` | same as `fa_dep_news_sm` | floret, 200k rows / 300d, full-wiki 5 epochs | built, shipping |
+| `fa_core_news_lg` | same as `fa_core_news_sm` | floret, 200k rows / 300d, full-wiki 5 epochs | built, shipping |
 | `fa_ent_news_lg` | ner (own internal tok2vec) | floret, 200k rows / 300d, full-wiki 5 epochs | built, optional |
-| `fa_core_news_lg` | same | floret, 200k rows | not built; bigger table, same recipe as md |
 | `fa_core_news_trf` | transformer instead of tok2vec | `HooshvareLab/roberta-fa-zwnj-base` (Apache-2.0) | not on this hardware; 2 GB VRAM cannot fine-tune a 125M-param encoder |
 
 ### Why `core` is honest here
@@ -351,26 +352,43 @@ Whether that trade is worth it depends on deployment. For a 1.19 LAS and 2.85 NE
 9x larger download and 16% slower parse is a good deal on a server and a bad one in a browser
 or a Lambda cold start. Both tiers ship; pick per target.
 
-## 7. The `lg` tier: bigger floret table, `ent` only
+## 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,
 `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). Unpacked the same way as `md`
+the rows of `md`'s 50k-row table trained on 400k documents). Raw `.floret`/`.vec` and the
-via `scripts/unpack_vectors.py`, into `assets/vectors/fa_floret_lg`.
+packaged spaCy wheel are at <https://huggingface.co/Phazel/fa-floret-wiki-vectors>. Unpacked
+the same way as `md` via `scripts/unpack_vectors.py`, into `assets/vectors/fa_floret_lg`.
 
-`configs/fa_ner_lg.cfg` is `fa_ner_md.cfg` unchanged except `--paths.vectors`. Only `ent` was
+`configs/fa_ner_lg.cfg` and `configs/fa_dep_news_lg.cfg` are `fa_ner_md.cfg`/
-trained at this tier (`fa_ent_news_lg`), not `dep`/`core`: the point of this run was to check
+`fa_dep_news_md.cfg` unchanged except `--paths.vectors`. Same seed, same corpus, same
-whether a 4x larger table is worth it before spending the CPU time on `dep`/`core` too. Same
+architecture as `sm`/`md` throughout, so the deltas below are attributable to the vector
-seed, same corpus, same architecture as `sm`/`md`. Reproduce with `spacy project run ent-lg`,
+table alone. Reproduce with `spacy project run lg`, or the tables alone with
-or the table alone with `python scripts/compare_tiers.py`.
+`python scripts/compare_tiers.py`.
 
-### PerDT NER test split, `fa_ent_news_lg`
+### UD test split, `fa_dep_news_lg` / `fa_core_news_lg`
 
-| Metric | `sm` | `md` | `lg` | Delta (lg vs sm) |
+| Metric | `sm` | `md` | `lg` | Delta (lg vs sm) | Delta (lg vs md) |
-| --- | --- | --- | --- | --- |
+| --- | --- | --- | --- | --- | --- |
-| `ENTS_P` | 77.67 | 76.56 | 81.51 | +3.84 |
+| `TAG_ACC` | 95.96 | 96.25 | 96.55 | +0.59 | +0.30 |
-| `ENTS_R` | 66.87 | 72.95 | 71.09 | +4.22 |
+| `POS_ACC` | 96.24 | 96.64 | 96.68 | +0.44 | +0.04 |
-| `ENTS_F` | 71.87 | 74.71 | 75.94 | +4.08 |
+| `MORPH_ACC` | 96.29 | 96.64 | 96.70 | +0.41 | +0.06 |
+| `LEMMA_ACC` | 97.91 | 97.96 | 98.08 | +0.17 | +0.12 |
+| `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.
+
+### PerDT NER test split, `fa_ent_news_lg` (identical `ner` component embedded in `fa_core_news_lg`)
+
+| Metric | `sm` | `md` | `lg` | Delta (lg vs sm) | Delta (lg vs md) |
+| --- | --- | --- | --- | --- | --- |
+| `ENTS_P` | 77.67 | 76.56 | 81.51 | +3.84 | +4.95 |
+| `ENTS_R` | 66.87 | 72.95 | 71.09 | +4.22 | -1.86 |
+| `ENTS_F` | 71.87 | 74.71 | 75.94 | +4.08 | +1.23 |
 
 `lg` beats both `sm` and `md` on `ENTS_F`, and unlike `md`'s recall-only gain over `sm`, `lg`
 improves precision too (+3.84 over `sm`, whereas `md` cost -1.10). Consistent with a bigger,
@@ -393,16 +411,23 @@ one-or-two-entity noise, same caveat as §6.
 
 ### Cost
 
-The bigger table dominates the artifact even more than `md`'s did: `fa_ent_news_lg` is a
+The bigger table dominates the artifact even more than `md`'s did: the 200k x 300d float32
-217 MB wheel against 5.6 MB for `sm` and 58 MB for `md` — the 200k x 300d float32 vector
+vector table is ~240 MB uncompressed, so `fa_dep_news_lg` is a 219 MB wheel (vs 7.5 MB `sm`,
-table alone is ~240 MB uncompressed. Training cost was comparable to `sm`/`md` (early stop
+60 MB `md`), `fa_core_news_lg` 225 MB (vs 13 MB `sm`, 66 MB `md`), and `fa_ent_news_lg` alone
-at step 7,200 of 20,000, best checkpoint at step 5,600). The `spacy benchmark accuracy`
+217 MB (vs 5.6 MB `sm`, 58 MB `md`). Training cost roughly doubled `md`'s: `dep_lg` ran to
-words/s figures swung in `lg`'s favor in this run (15,614 vs 8,500 `sm` / 7,149 `md`); given
+early stop at step 12,000 of 20,000 over ~2h08m CPU wall time (vs `dep_md`'s single-digit
-`lg`'s tok2vec architecture is identical to `md`'s and only the static-vector table lookup
+minutes territory implied by its architecture-identical config — `lg`'s extra time is
-differs, treat that as single-run CPU contention noise on shared hardware, not a real
+entirely the larger embedding table's per-step cost, not more steps). `ner_lg` early-stopped
-architectural speedup, and re-benchmark before citing a number.
+at step 7,200, ~13 min, in line with `sm`/`md`.
 
-For a 4x download over `md` (and 39x over `sm`) buying +4.08 ENTS_F over `sm` (+1.23 over
+`words/s` from `spacy benchmark accuracy` were noisier at this tier than `sm`-vs-`md`: dep/core
-`md`), `lg` is a server/offline-batch pipeline, not something to ship to a browser or a
+throughput dropped as expected (9,387 / 6,655 words/s vs `sm`'s 12,505 / 8,834, `md`'s
-cold-start function. `dep`/`core` at this tier are not yet built; the `ent`-only result above
+10,493 / 7,269 — the larger table costs real lookup time), but the standalone `ent_lg` run
-is the signal for whether that investment is worth making.
+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`.

project.yml

@@ -34,10 +34,11 @@ vars:
   core_md_package_name: "core_news_md"
   floret_wheel: "fa_floret-0.1.0-py3-none-any-400k-documents.whl"
   vectors_dir: "assets/vectors/fa_floret_400k"
-  # lg tier. Same architecture as sm/md, but ner only (no dep/core trained yet), with a
+  # lg tier: same architecture as sm/md, larger floret table (200k rows x 300d, trained on
-  # larger floret table: 200k rows x 300d, trained on the full Persian Wikipedia dump for
+  # the full Persian Wikipedia dump for 5 epochs, vs md's 50k rows / 400k documents).
-  # 5 epochs (vs md's 50k rows / 400k documents).
   ent_lg_package_name: "ent_news_lg"
+  dep_lg_package_name: "dep_news_lg"
+  core_lg_package_name: "core_news_lg"
   floret_lg_wheel: "fa_floret-0.1.0-py3-none-any-full-wiki-200k-5epoch.whl"
   vectors_lg_dir: "assets/vectors/fa_floret_lg"
 
@@ -99,16 +100,19 @@ workflows:
     - finalize-ent
     - evaluate-ent
     - package-ent
-  # The lg tier: ner only, same corpus as sm/md, with a bigger floret table (200k rows,
+  # The lg tier: same corpus and architecture as sm/md, with a bigger floret table (200k
-  # full Persian Wikipedia, 5 epochs) than md's (50k rows, 400k documents).
+  # rows, full Persian Wikipedia, 5 epochs) than md's (50k rows, 400k documents).
-  ent-lg:
+  lg:
     - vectors-lg
+    - train-dep-lg
     - train-ner-lg
-    - finalize-ent-lg
+    - finalize-dep-lg
-    - evaluate-ent-lg
+    - assemble-core-lg
+    - evaluate-lg
+    - finalize-meta-lg
     - compare-lg
-    - package-ent-lg
+    - package-lg
-    - smoke-ent-lg
+    - smoke-lg
   # The md tier: same corpus and architecture, plus the fa_floret static vectors.
   md:
     - vectors-md
@@ -345,6 +349,18 @@ commands:
     outputs:
       - "${vars.vectors_lg_dir}"
 
+  - name: "train-dep-lg"
+    help: "Train the dep pipeline with the lg-tier static floret vectors"
+    script:
+      - "python -m spacy train configs/fa_dep_news_lg.cfg --output training/dep-lg --paths.train corpus/merged/${vars.treebank}-ud-train.spacy --paths.dev corpus/merged/${vars.treebank}-ud-dev.spacy --paths.vectors ${vars.vectors_lg_dir} --gpu-id ${vars.gpu}"
+    deps:
+      - "corpus/merged/${vars.treebank}-ud-train.spacy"
+      - "corpus/merged/${vars.treebank}-ud-dev.spacy"
+      - "configs/fa_dep_news_lg.cfg"
+      - "${vars.vectors_lg_dir}"
+    outputs:
+      - "training/dep-lg/model-best"
+
   - name: "train-ner-lg"
     help: "Train the NER component with the lg-tier static floret vectors"
     script:
@@ -367,6 +383,27 @@ commands:
     outputs:
       - "training/fa_ent_news_lg"
 
+  - name: "finalize-dep-lg"
+    help: "Write fa_dep_news_lg metadata onto the trained lg model"
+    script:
+      - "python scripts/finalize_pipeline.py training/dep-lg/model-best training/fa_dep_news_lg --variant dep --size lg --version ${vars.package_version}"
+    deps:
+      - "training/dep-lg/model-best"
+      - "scripts/finalize_pipeline.py"
+    outputs:
+      - "training/fa_dep_news_lg"
+
+  - name: "assemble-core-lg"
+    help: "Source the lg ner into the lg dep pipeline to produce fa_core_news_lg"
+    script:
+      - "python scripts/finalize_pipeline.py training/dep-lg/model-best training/fa_core_news_lg --variant core --size lg --version ${vars.package_version} --add-ner training/perdt-ner-lg/model-best"
+    deps:
+      - "training/dep-lg/model-best"
+      - "training/perdt-ner-lg/model-best"
+      - "scripts/finalize_pipeline.py"
+    outputs:
+      - "training/fa_core_news_lg"
+
   - name: "evaluate-ent-lg"
     help: "Score fa_ent_news_lg on the held-out PerDT NER test split"
     script:
@@ -378,11 +415,38 @@ commands:
     outputs:
       - "metrics/lg-perdt-ner-test.json"
 
+  - name: "evaluate-lg"
+    help: "Score both lg packages (dep, core) on the held-out test splits"
+    script:
+      - "python -m spacy benchmark accuracy training/fa_dep_news_lg corpus/merged/${vars.treebank}-ud-test.spacy --output metrics/lg-ud-test.json --gpu-id ${vars.gpu}"
+      - "python -m spacy benchmark accuracy training/fa_core_news_lg corpus/merged/${vars.treebank}-ud-test.spacy --output metrics/lg-core-ud-test.json --gpu-id ${vars.gpu}"
+      - "python -m spacy benchmark accuracy training/fa_core_news_lg corpus/perdt-ner/test.spacy --output metrics/lg-core-perdt-ner-test.json --gpu-id ${vars.gpu}"
+    deps:
+      - "training/fa_dep_news_lg"
+      - "training/fa_core_news_lg"
+    outputs:
+      - "metrics/lg-ud-test.json"
+      - "metrics/lg-core-ud-test.json"
+      - "metrics/lg-core-perdt-ner-test.json"
+
+  - name: "finalize-meta-lg"
+    help: "Fold the lg test scores into both lg meta.json files"
+    script:
+      - "python scripts/finalize_pipeline.py training/dep-lg/model-best training/fa_dep_news_lg --variant dep --size lg --version ${vars.package_version} --ud-metrics metrics/lg-ud-test.json"
+      - "python scripts/finalize_pipeline.py training/dep-lg/model-best training/fa_core_news_lg --variant core --size lg --version ${vars.package_version} --add-ner training/perdt-ner-lg/model-best --ud-metrics metrics/lg-core-ud-test.json --ner-metrics metrics/lg-core-perdt-ner-test.json"
+    deps:
+      - "metrics/lg-ud-test.json"
+      - "metrics/lg-core-perdt-ner-test.json"
+      - "scripts/finalize_pipeline.py"
+
   - name: "compare-lg"
-    help: "Table the sm vs md vs lg ent NER deltas from the metrics/ JSON reports"
+    help: "Table the sm vs md vs lg deltas from the metrics/ JSON reports"
     script:
       - "python scripts/compare_tiers.py"
     deps:
+      - "metrics/ud-test.json"
+      - "metrics/md-ud-test.json"
+      - "metrics/lg-ud-test.json"
       - "metrics/perdt-ner-test.json"
       - "metrics/md-perdt-ner-test.json"
       - "metrics/lg-perdt-ner-test.json"
@@ -397,6 +461,18 @@ commands:
     outputs:
       - "packages/${vars.lang}_${vars.ent_lg_package_name}-${vars.package_version}"
 
+  - name: "package-lg"
+    help: "Build installable wheels + sdists for both lg packages"
+    script:
+      - "python -m spacy package training/fa_dep_news_lg packages --name ${vars.dep_lg_package_name} --version ${vars.package_version} --build sdist,wheel --force"
+      - "python -m spacy package training/fa_core_news_lg packages --name ${vars.core_lg_package_name} --version ${vars.package_version} --build sdist,wheel --force"
+    deps:
+      - "training/fa_dep_news_lg"
+      - "training/fa_core_news_lg"
+    outputs:
+      - "packages/${vars.lang}_${vars.dep_lg_package_name}-${vars.package_version}"
+      - "packages/${vars.lang}_${vars.core_lg_package_name}-${vars.package_version}"
+
   - name: "smoke-ent-lg"
     help: "Load fa_ent_news_lg and run it over real Persian text"
     script:
@@ -404,6 +480,15 @@ commands:
     deps:
       - "training/fa_ent_news_lg"
 
+  - name: "smoke-lg"
+    help: "Load both lg pipelines and run them over real Persian text"
+    script:
+      - "python scripts/smoke_test.py training/fa_dep_news_lg"
+      - "python scripts/smoke_test.py training/fa_core_news_lg"
+    deps:
+      - "training/fa_dep_news_lg"
+      - "training/fa_core_news_lg"
+
   # ---------------------------------------------------------------- md tier
 
   - name: "vectors-md"

scripts/compare_tiers.py

@@ -18,8 +18,18 @@ from pathlib import Path
 
 # (label, {tier_label: report_filename})
 GROUPS = [
-    ("dep pipeline, UD test", {"sm": "ud-test.json", "md": "md-ud-test.json"}),
+    (
-    ("core pipeline, UD test", {"sm": "core-ud-test.json", "md": "md-core-ud-test.json"}),
+        "dep pipeline, UD test",
+        {"sm": "ud-test.json", "md": "md-ud-test.json", "lg": "lg-ud-test.json"},
+    ),
+    (
+        "core pipeline, UD test",
+        {
+            "sm": "core-ud-test.json",
+            "md": "md-core-ud-test.json",
+            "lg": "lg-core-ud-test.json",
+        },
+    ),
     (
         "ent NER test",
         {

scripts/finalize_pipeline.py

@@ -69,7 +69,7 @@ FLORET = {
 FLORET_LG = {
     "name": "fa_floret static vectors (lg tier: 200k rows x 300d floret table trained on "
             "the full Persian Wikipedia dump, 5 epochs, via spacy-vectors-builder)",
-    "url": PROJECT_URL,
+    "url": "https://huggingface.co/Phazel/fa-floret-wiki-vectors",
     "author": "Kiyarash Fazeli",
     "license": "CC BY-SA 4.0",
 }