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:
- ddb4b4af3b041f9f7acf277cadcf3fe6c3edb9ef5a106e5b8c73afb2b3a119c4
- Size of remote file:
- 1.11 GB
- SHA256:
- d9640fa3e65c767e4162ff7a86522123af5894957ffabde105f5a5a07423f01c
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