trendyol-eticaret-2026-models

efeyol11/trendyol-eticaret-2026-models

trendyol-eticaret-2026-models is a text classification model from efeyol11. Use it when you need a label for a piece of text. The card lists the license as other.

Public model artifacts for the Trendyol E-Ticaret Yarismasi 2026 Kaggle pipeline.

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Public

Updated Jul 6, 2026

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29.1 GB

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.safetensors17.5 GB · 71%

At a glance

Task: Text Classification
License: other
Access: Public
Created: Jun 29, 2026
Updated: Jul 6, 2026
SHA: d07f251e

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Model card

From the Hugging Face model README

Trendyol E-Ticaret 2026 Shared Models

Public model artifacts for the Trendyol E-Ticaret Yarismasi 2026 Kaggle pipeline.

Last verified Hub visibility: public on 2026-06-29.

These are the default checkpoints downloaded by 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 + l3bgeattrmm43 test 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-id ile).

Each folder is a standard Transformers AutoModelForSequenceClassification checkpoint with tokenizer files and a local meta.json.

Download

From the project repository:

HF_MODEL_REPO=efeyol11/trendyol-eticaret-2026-models \
uv run python scripts/download_models.py

The repo is public, so hf auth login is not required for download. Logging in can still help with rate limits.

To fetch every experiment folder on the Hub instead of the default set:

HF_MODEL_REPO=efeyol11/trendyol-eticaret-2026-models \
uv run python scripts/download_models.py --models all

To replace existing local model folders:

HF_MODEL_REPO=efeyol11/trendyol-eticaret-2026-models \
uv run python scripts/download_models.py --force

Usage

CE_OUT=models_store/ce CE_TAG=l3 uv run python scripts/predict_l3_cross_encoder.py
CE_OUT=models_store/ce_electra CE_TAG=l3el uv run python scripts/predict_l3_cross_encoder.py
CE_OUT=models_store/ce_rich CE_TAG=l3rich uv run python scripts/predict_l3_cross_encoder.py
CE_OUT=models_store/ce_hardft_faiss10 CE_TAG=l3hard uv run python scripts/predict_l3_cross_encoder.py
CE_OUT=models_store/ce_hardft_deep CE_TAG=l3deep uv run python scripts/predict_l3_cross_encoder.py
CE_OUT=models_store/ce_eldeep CE_TAG=l3eldeep uv run python scripts/predict_l3_cross_encoder.py
CE_OUT=models_store/ce_xlmr CE_TAG=l3xlmr uv run python scripts/predict_l3_cross_encoder.py

To upload or resync the public repository with a write-capable Hugging Face token:

hf auth login
HF_MODEL_REPO=efeyol11/trendyol-eticaret-2026-models \
uv run python scripts/upload_models_to_hf.py --public

Competition data and submission caches are shared through the private GitHub repository via Git LFS. Model checkpoints are shared here on Hugging Face.

Task: Text Classification
License: other
Created: Jun 29, 2026
Updated: Jul 6, 2026