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  ---
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  annotations_creators:
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- - machine-generated
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  language_creators:
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- - machine-generated
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  widget:
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- - text: George Washington went to Washington.
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- - text: What is the seventh tallest mountain in North America?
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  tags:
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- - named-entity-recognition
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- - sequence-tagger-model
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  datasets:
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- - Babelscape/cner
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  language:
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- - en
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- license:
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- - cc-by-nc-sa-4.0
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  pretty_name: cner-model
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  source_datasets:
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- - original
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  task_categories:
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- - structure-prediction
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  task_ids:
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- - named-entity-recognition
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  ---
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  # CNER: Concept and Named Entity Recognition
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  This is the model card for the NAACL 2024 paper [CNER: Concept and Named Entity Recognition](https://aclanthology.org/2024.naacl-long.461/).
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  We fine-tuned a language model (DeBERTa-v3-base) for 1 epoch on our [CNER dataset](https://huggingface.co/datasets/Babelscape/cner)
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- The resulting CNER model is able to jointly identifing and classifying concepts and named entities with fine-grained tags.
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  **If you use the model, please reference this work in your paper**:
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@@ -77,4 +75,4 @@ print(ner_results)
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  ## Licensing Information
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- Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents and models belongs to the original copyright holders.
 
1
  ---
2
  annotations_creators:
3
+ - machine-generated
4
  language_creators:
5
+ - machine-generated
6
  widget:
7
+ - text: George Washington went to Washington.
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+ - text: What is the seventh tallest mountain in North America?
9
  tags:
10
+ - named-entity-recognition
11
+ - sequence-tagger-model
12
  datasets:
13
+ - Babelscape/cner
14
  language:
15
+ - en
 
 
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  pretty_name: cner-model
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  source_datasets:
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+ - original
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  task_categories:
20
+ - structure-prediction
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  task_ids:
22
+ - named-entity-recognition
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  ---
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  # CNER: Concept and Named Entity Recognition
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  This is the model card for the NAACL 2024 paper [CNER: Concept and Named Entity Recognition](https://aclanthology.org/2024.naacl-long.461/).
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  We fine-tuned a language model (DeBERTa-v3-base) for 1 epoch on our [CNER dataset](https://huggingface.co/datasets/Babelscape/cner)
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+ The resulting CNER model is able to jointly identifying and classifying concepts and named entities with fine-grained tags.
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  **If you use the model, please reference this work in your paper**:
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  ## Licensing Information
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+ Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents and models belongs to the original copyright holders.