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:
- b99353faf2776830f96771d77fd418f3a29a72722798cf60cf436d78bdf7b847
- Size of remote file:
- 736 MB
- SHA256:
- 649d7a92df0a7dc94707c8aa450bb9fc18f73665b04f9d696c6efcc7667c65f7
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