Instructions to use davidpc99/multilanguageNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davidpc99/multilanguageNER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="davidpc99/multilanguageNER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("davidpc99/multilanguageNER") model = AutoModelForTokenClassification.from_pretrained("davidpc99/multilanguageNER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6fd2bc55dffdbe7ef413a93ac891c7263b6327a62afd38853bf87f10c7847093
- Size of remote file:
- 1.11 GB
- SHA256:
- ab6a64816336a4c1d8e84f113b83c701936de9d4e694e9803b8fbf2f5686a44e
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