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
- Xet hash:
- ef34e17b0f97596fd451f50fbec9264b4f912ffa66ddf4cb3088dce00a1711cc
- Size of remote file:
- 4.86 kB
- SHA256:
- 456a5e0d98e3d0d5cfa9aa87485c32794fc42b06518dbabb7147cbdb8b4990df
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