Instructions to use MarkusDressel/cord with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MarkusDressel/cord with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="MarkusDressel/cord")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("MarkusDressel/cord") model = AutoModelForTokenClassification.from_pretrained("MarkusDressel/cord", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c1cc3677785af3d73a866c4e08acf791a34717d25e0d153e873ecafebba3110
- Size of remote file:
- 2.86 kB
- SHA256:
- e204d0248b4a24a94847dfdfb3590f69252983ca5334ee764719a1e47a64d338
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.