Instructions to use wietsedv/bert-base-multilingual-cased-finetuned-sonar-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-sonar-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-sonar-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/bert-base-multilingual-cased-finetuned-sonar-ner") model = AutoModelForTokenClassification.from_pretrained("wietsedv/bert-base-multilingual-cased-finetuned-sonar-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- befe49ea9cfa856743a65fb7ba8a94eb1f2fe87bd8746044aad235c013274545
- Size of remote file:
- 709 MB
- SHA256:
- 05d09d0e6f96f865a17b761396e1703cf646c16e42be52368e635464cabde303
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