Instructions to use descentis/distilbert-cuad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use descentis/distilbert-cuad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="descentis/distilbert-cuad")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("descentis/distilbert-cuad") model = AutoModelForQuestionAnswering.from_pretrained("descentis/distilbert-cuad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1db8c86bd1a39c5334a05f8f5d595b5e45506112621435f159a9fcbda6df3225
- Size of remote file:
- 265 MB
- SHA256:
- 81f6681e09a5fd74a095ffbebabfcf0fb41667ae5887ae862568832eceb7a073
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.