Instructions to use wietsedv/bert-base-multilingual-cased-finetuned-conll2002-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wietsedv/bert-base-multilingual-cased-finetuned-conll2002-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="wietsedv/bert-base-multilingual-cased-finetuned-conll2002-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/bert-base-multilingual-cased-finetuned-conll2002-ner") model = AutoModelForTokenClassification.from_pretrained("wietsedv/bert-base-multilingual-cased-finetuned-conll2002-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0d07bf062594117e5133f77f450456470f51221014ba059ae89608f4e77c1b1
- Size of remote file:
- 711 MB
- SHA256:
- 0e4d13c2f858a569580ecfcc341a0404e2357218f90dcbf2ee86d093fea3c40f
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