Instructions to use wietsedv/bert-base-multilingual-cased-finetuned-udlassy-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-udlassy-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-udlassy-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/bert-base-multilingual-cased-finetuned-udlassy-ner") model = AutoModelForTokenClassification.from_pretrained("wietsedv/bert-base-multilingual-cased-finetuned-udlassy-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b471e0bce90a9a600404a5f7735926ffdc11ed5b70a1079114248ea1d514a6f4
- Size of remote file:
- 709 MB
- SHA256:
- a37380aa63bde60a63f26a3c468dea9f9267535dd0fd4969014a12be891e1d91
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