Instructions to use AndyChiang/Pre-CoFactv3-Question-Answering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndyChiang/Pre-CoFactv3-Question-Answering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="AndyChiang/Pre-CoFactv3-Question-Answering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("AndyChiang/Pre-CoFactv3-Question-Answering") model = AutoModelForQuestionAnswering.from_pretrained("AndyChiang/Pre-CoFactv3-Question-Answering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d3cb635cbf4d442f6312eae3eb19f2123d767d1ef963e8af8f3cf97d173a858f
- Size of remote file:
- 1.74 GB
- SHA256:
- af9234c383bf2ec65ccc897e2f94897764d8f5129a2edad105817a9ba03779ec
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.