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scripts/download_models.py.
The public Hub repo can also keep older experiment folders; use --models all
only when those are needed.| Folder | Source model | Text mode | Max length | Notes |
|---|---|---|---|---|
ce/ | dbmdz/bert-base-turkish-cased | base | 64 | Public LB anchor around 0.80 |
ce_electra/ | dbmdz/electra-base-turkish-cased-discriminator | base | 64 | Diversity CE, public LB around 0.80 |
ce_rich/ | dbmdz/bert-base-turkish-cased | rich | 128 | Uses selected attributes/gender/age; public LB 0.79 |
ce_hardft_faiss10/ | ce/ fine-tune | base | 64 | FAISS hard-negative fine-tune; ce_l3hard_pos0.240.csv reached Public LB 0.82 |
ce_hardft_deep/ | ce/ fine-tune | base | 64 | Deeper FAISS hard-negative fine-tune with up to 24 negatives per term |
ce_eldeep/ | ce_electra/ fine-tune | base | 64 | ELECTRA version of the deeper FAISS hard-negative fine-tune |
ce_xlmr/ | FacebookAI/xlm-roberta-large (560M) | base | 64 | XLM-R large on deep negatives; ce_l3xlmr_pos0.240.csv reached Public LB 0.87 (trained bf16, grad-ckpt) |
ce_bge/ | BAAI/bge-reranker-v2-m3 (568M) | base | 64 | Reranker-pretrained on deep negatives; ce_l3bge_pos0.240.csv Public LB 0.87, proxy 0.6691 |
ce_bge_attr/ | BAAI/bge-reranker-v2-m3 (568M) | attr | 96 | bge + curated attributes (attr text mode); ce_l3bgeattr_pos0.240.csv Public LB 0.88 |
ce_bgeattrmm/ | BAAI/bge-reranker-v2-m3 (568M) | attr | 96 | + deep+attrmm negatifler (cap 32), seed 42; tekil submit LB 0.88 (düz) |
ce_bgeattrmm43/ | BAAI/bge-reranker-v2-m3 (568M) | attr | 96 | aynı reçete seed 43; 0.89 ortalamasının üyesi |
ce_bgeattrmmps/ | BAAI/bge-reranker-v2-m3 (568M) | attr | 96 | + pseudo_v2_strict.csv; 0.89 ortalamasının üyesi |
ce_bgeattrctx/ | BAAI/bge-reranker-v2-m3 (568M) | attrctx | 128 | co-candidate query context; tekil proxy düz, attr2ctx kombinasyon adayı |
Güncel en iyi public dosya (0.89):submissions/ce_l3bgeattrps43_pos0.240.csv=l3bgeattrmmps+l3bgeattrmm43test olasılıklarının 50/50 ortalaması. Yeniden üretmek:make_weighted_ce_variant.py --tags l3bgeattrmmps,l3bgeattrmm43 --weights 0.5,0.5 --target-pos 0.24. Mirror model repo:bvrtuu/trendyol-eticaret-2026-models(aynı upload script,--repo-idile).
AutoModelForSequenceClassification checkpoint
with tokenizer files and a local meta.json.1HF_MODEL_REPO=efeyol11/trendyol-eticaret-2026-models \
2uv run python scripts/download_models.pyhf auth login is not required for download. Logging in can
still help with rate limits.1HF_MODEL_REPO=efeyol11/trendyol-eticaret-2026-models \
2uv run python scripts/download_models.py --models all1HF_MODEL_REPO=efeyol11/trendyol-eticaret-2026-models \
2uv run python scripts/download_models.py --force1CE_OUT=models_store/ce CE_TAG=l3 uv run python scripts/predict_l3_cross_encoder.py
2CE_OUT=models_store/ce_electra CE_TAG=l3el uv run python scripts/predict_l3_cross_encoder.py
3CE_OUT=models_store/ce_rich CE_TAG=l3rich uv run python scripts/predict_l3_cross_encoder.py
4CE_OUT=models_store/ce_hardft_faiss10 CE_TAG=l3hard uv run python scripts/predict_l3_cross_encoder.py
5CE_OUT=models_store/ce_hardft_deep CE_TAG=l3deep uv run python scripts/predict_l3_cross_encoder.py
6CE_OUT=models_store/ce_eldeep CE_TAG=l3eldeep uv run python scripts/predict_l3_cross_encoder.py
7CE_OUT=models_store/ce_xlmr CE_TAG=l3xlmr uv run python scripts/predict_l3_cross_encoder.py1hf auth login
2HF_MODEL_REPO=efeyol11/trendyol-eticaret-2026-models \
3uv run python scripts/upload_models_to_hf.py --public