Token Classification
GLiNER2
Safetensors
multilingual
English
extractor
Text classification
Named Entity Recognition
Relation Extraction
Intent classification
Sentiment Analysis
Topic classification
Structured extraction
Json extraction
information-extraction
boundary-extraction
Instructions to use fastino/gliner2.5-multi-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use fastino/gliner2.5-multi-v1 with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("fastino/gliner2.5-multi-v1") # 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
Upload folder using huggingface_hub
Browse files- config.json +1 -1
- encoder_config/config.json +1 -1
- model.safetensors +2 -2
config.json
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"model_name": "microsoft/mdeberta-v3-base",
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"model_type": "extractor",
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"token_pooling": "first",
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"transformers_version": "5.
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}
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"model_name": "microsoft/mdeberta-v3-base",
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"model_type": "extractor",
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"token_pooling": "first",
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"transformers_version": "5.8.0"
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}
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encoder_config/config.json
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"relative_attention": true,
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"share_att_key": true,
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"tie_word_embeddings": true,
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"transformers_version": "5.
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"type_vocab_size": 0,
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"vocab_size": 250112
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}
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"relative_attention": true,
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"share_att_key": true,
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"tie_word_embeddings": true,
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"transformers_version": "5.8.0",
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"type_vocab_size": 0,
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"vocab_size": 250112
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}
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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
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version https://git-lfs.github.com/spec/v1
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oid sha256:c1ff4ec0bc00031c15530b8f3c33d3677f27949e6a0cb52e1247a6224b6c5395
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size 1149461028
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