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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (d56d6916b511ce8e7dea6786c18032b66b9d220a) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>7481a7b
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upload flax model
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allow flax
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For clarity, delete deprecated modelcard.json
We now use the README.md model card instead Approved-by: Julien Chaumond <julien@huggingface.co>e963e6b
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Update config.json
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initial commit
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