Instructions to use avi2905/model1__docExtract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avi2905/model1__docExtract with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="avi2905/model1__docExtract")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("avi2905/model1__docExtract") model = AutoModelForTokenClassification.from_pretrained("avi2905/model1__docExtract", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f80bce548c32559224c20d4a86c64be031e6d1cf9dd671c0208ed6df17a7b3e
- Size of remote file:
- 501 MB
- SHA256:
- 0f59b32652c91d09c4190e724a0f9d471e8988c7fa58e7f986b32ef2a52727ce
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.