Instructions to use proycon/bert-pos-cased-deepfrog-nld with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use proycon/bert-pos-cased-deepfrog-nld with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="proycon/bert-pos-cased-deepfrog-nld")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("proycon/bert-pos-cased-deepfrog-nld") model = AutoModelForTokenClassification.from_pretrained("proycon/bert-pos-cased-deepfrog-nld") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fe5c018fab5cdf5a8b0cb759b5acd169c724df37a5306c98f5c071a0e6a89ce5
- Size of remote file:
- 435 MB
- SHA256:
- 31dd73fe2b1e7dd93962ee605c39abc1da04f8c4431b2c836b59998acf57dabf
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