Vocabulary Pruning Engine for Multilingual GLiNER Models#366
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ALI-AL-MARJANI wants to merge 12 commits into
Open
Vocabulary Pruning Engine for Multilingual GLiNER Models#366ALI-AL-MARJANI wants to merge 12 commits into
ALI-AL-MARJANI wants to merge 12 commits into
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added 11 commits
May 18, 2026 15:47
- scripts/prune_gliner_vocab.py: prune multilingual GLiNER vocab to target language
- scripts/validate_pruned_model.py: 3-tier validator (PASS/SCORE_DRIFT/ENTITY_FAIL),
--score_tol flag
- docs/vocab_pruning.md: full documentation page with benchmark results
- docs/index.md: add vocab_pruning to toctree
- gliner/modeling/encoder.py: fix token_lengths kwarg leak in BiEncoder.encode_labels
- gliner/model.py: wrap onnxruntime import in try/except for ARM compatibility
Benchmarked on urchade/gliner_multi-v2.1 (mDeBERTa-v3, 250k vocab):
English Wikipedia corpus -> 250,105 -> 90,840 tokens (63.7% reduction)
Model size: 1155.8 -> 666.5 MB (42.3% smaller), ALL PASS entity correctness
Collaborator
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@ALI-AL-MARJANI , thanks for your contributions. Please, fix ruff errors first so we can move forward. |
Fix 25 ruff errors blocking the lint job requested by maintainer: - Sort import blocks (I001) and __all__ (RUF022) across 9 files - Merge duplicate get_train_dataloader (F811) — curriculum path integrated into the single authoritative implementation - Add missing docstring args (D417) in config.py, long_doc.py, model.py - Remove unused imports (F401): math, torch, Dict, numpy - Fix for-loop variable overwrite (PLW2901) in descriptions.py - Remove unused neg_mask variable (F841) in loss_functions.py - Fix EN dash in string literal (RUF001) in trainer.py - Fix docstring summary blank line (D205) in hard_negatives.py - Break line > 120 chars (E501) in model.py Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Author
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CI should pass now i guess |
Collaborator
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@ALI-AL-MARJANI , please merge from main as there are some conflicts right now that need to be resolved |
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Problem
Multilingual GLiNER models (mDeBERTa-v3) carry a 250k-token embedding matrix.
For single-language deployments, >60% of these embeddings are never accessed.
This creates an unnecessary memory and cold-start bottleneck for edge/CPU deployments.
Solution
scripts/prune_gliner_vocab.pytokenises a target-language corpus, identifies theactive token intersection, slices
word_embeddings.weight, rebuildstokenizer.json,and saves a fully self-contained pruned model loadable via
GLiNER.from_pretrained().Benchmark —
urchade/gliner_multi-v2.1(English Wikipedia, 100k articles)Files changed
scripts/prune_gliner_vocab.py— pruning engine (new)scripts/validate_pruned_model.py— 3-tier correctness validator (new)docs/vocab_pruning.md— documentation with benchmarks (new)docs/index.md— added to toctreegliner/modeling/encoder.py— bugfix:token_lengthskwarg leaked into BiEncoderlabels encoder forward pass
gliner/model.py— bugfix: bareimport onnxruntimereplaced with try/exceptUsage
python scripts/prune_gliner_vocab.py \ --model_id urchade/gliner_multi-v2.1 \ --dataset_for_vocab wikipedia \ --output_dir ./pruned_en \ --lang en