Instructions to use tner/deberta-large-wnut2017 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tner/deberta-large-wnut2017 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tner/deberta-large-wnut2017")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tner/deberta-large-wnut2017") model = AutoModelForTokenClassification.from_pretrained("tner/deberta-large-wnut2017", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1d4b9c9947d064bd57f1ea0b9a137028d03f114cefcb8ff023b85cea3d480f7
- Size of remote file:
- 1.62 GB
- SHA256:
- 3376bc5ea9d310a358b44822ee05622cfc05e65cae26c2e8b303a411c00e1ffa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.