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
Commit History
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Add GLiNER2 flat NER data mention LoRA adapter (v20-encore-flat-ner)
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Add GLiNER2 flat NER data mention LoRA adapter (v19-encore-flat-ner)
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Add GLiNER2 flat NER data mention LoRA adapter (v19-encore-flat-ner)
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Add GLiNER2 flat NER data mention LoRA adapter (v16-encore-flat-ner)
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Add GLiNER2 flat NER data mention LoRA adapter (v15b-encore-flat-ner)
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Add GLiNER2 flat NER data mention LoRA adapter (v15-encore-flat-ner)
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Add GLiNER2 flat NER data mention LoRA adapter (v15-encore-flat-ner)
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Add GLiNER2 flat NER data mention LoRA adapter (v12-encore-flat-ner)
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Add GLiNER2 flat NER data mention LoRA adapter (v12-encore-flat-ner)
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Add GLiNER2 flat NER data mention LoRA adapter (v12-encore-flat-ner)
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