Token Classification
Transformers
TensorBoard
Safetensors
English
deberta-v2
named-entity-recognition
sequence-tagger-model
Instructions to use Babelscape/cner-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Babelscape/cner-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Babelscape/cner-base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Babelscape/cner-base") model = AutoModelForTokenClassification.from_pretrained("Babelscape/cner-base") - Notebooks
- Google Colab
- Kaggle
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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.
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`microsoft/deberta-v3-base` is released under the [MIT license](https://choosealicense.com/licenses/mit/).
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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.
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`microsoft/deberta-v3-base` is released under the [MIT license](https://choosealicense.com/licenses/mit/).
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