Instructions to use rafmacalaba/datause-extraction-v8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use rafmacalaba/datause-extraction-v8 with PEFT:
Task type is invalid.
- Transformers
How to use rafmacalaba/datause-extraction-v8 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rafmacalaba/datause-extraction-v8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add GLiNER2 flat NER data mention LoRA adapter (v12-encore-flat-ner)
Browse files- adapter_config.json +6 -6
- adapter_model.safetensors +1 -1
adapter_config.json
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"rank_pattern": {},
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"revision": null,
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"target_parameters": null,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"dense",
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"project_start.3",
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"project_end.3",
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"project_start.0",
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"out_project.3",
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"out_project.0",
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"key_proj",
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"value_proj",
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"query_proj"
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],
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"target_parameters": null,
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"task_type": null,
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 30387408
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version https://git-lfs.github.com/spec/v1
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oid sha256:ff7df826c133bf623814483aebccbe5d9adf20c2dae15ee341477e7e677f3316
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size 30387408
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