Instructions to use canIjoin/datafun with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use canIjoin/datafun with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="canIjoin/datafun")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("canIjoin/datafun") model = AutoModelForTokenClassification.from_pretrained("canIjoin/datafun") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed40ef302663b4560cac92741971a25a3cff1319698d1505010e20f1de2cedef
- Size of remote file:
- 407 MB
- SHA256:
- 3fcc5a4fdb2a83463bf4f65ea0d60e3ecb31963cf85f4f513f3b48150b522b57
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.