Instructions to use rafmacalaba/gliner2-datause-large-v1-deval-synth-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use rafmacalaba/gliner2-datause-large-v1-deval-synth-v2 with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("rafmacalaba/gliner2-datause-large-v1-deval-synth-v2") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
Add v1-deval-synth-v2 adapter (count_pred fix) — val_loss=439.4476
Browse files- adapter_config.json +15 -0
- adapter_weights.safetensors +3 -0
adapter_config.json
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{
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"adapter_type": "lora",
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"adapter_version": "1.0",
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"lora_r": 16,
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"lora_alpha": 32.0,
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"lora_dropout": 0.1,
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"target_modules": [
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"classifier",
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"count_embed",
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"count_pred",
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"encoder",
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"span_rep"
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],
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"created_at": "2026-04-06T13:46:19.060075Z"
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}
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adapter_weights.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:3f789f443becc3ec63f509d050e5a9e79072f25c25172b52e0d13e86cb496372
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size 31758920
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