Instructions to use spyysalo/example-turku-ner-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spyysalo/example-turku-ner-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="spyysalo/example-turku-ner-model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("spyysalo/example-turku-ner-model") model = AutoModelForTokenClassification.from_pretrained("spyysalo/example-turku-ner-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e6d15d99bfc0b0823702bbe1dc0f27e28798cd57f7e94fd466f7ec65458c9b61
- Size of remote file:
- 496 MB
- SHA256:
- 98ac2a7ad9867f2fc611e1af1dac8885ece20a71c6af96ed636f4ecb121117ad
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