Instructions to use DunnBC22/xlnet-base-cased-finetuned-WikiNeural-PoS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/xlnet-base-cased-finetuned-WikiNeural-PoS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="DunnBC22/xlnet-base-cased-finetuned-WikiNeural-PoS")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("DunnBC22/xlnet-base-cased-finetuned-WikiNeural-PoS") model = AutoModelForTokenClassification.from_pretrained("DunnBC22/xlnet-base-cased-finetuned-WikiNeural-PoS") - Notebooks
- Google Colab
- Kaggle
xlnet-base-cased-finetuned-WikiNeural-PoS / runs /Jul10_23-27-05_Brians-Mac-mini.local /1689049630.731692 /events.out.tfevents.1689049630.Brians-Mac-mini.local.7468.1
- Xet hash:
- 91570eaaffdb54ec086e660c59f2b4cead165464f22cbfa05a28de863cfdf3d9
- Size of remote file:
- 5.95 kB
- SHA256:
- e05dcf244f6fc23e82eb6c46fa71801d06c983f8108c3bd45db9b91e0fdd9d54
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