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
- Xet hash:
- f0b7e47d85b7c11cc264f985040353f80739ae91a88fbc5584578e2b6adcb826
- Size of remote file:
- 467 MB
- SHA256:
- 6d5a0730b4aec77082711712482189de174cb577a6cd93787ab79c660dfc9dbb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.