Instructions to use dewdev/question_and_answer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dewdev/question_and_answer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="dewdev/question_and_answer")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("dewdev/question_and_answer") model = AutoModelForQuestionAnswering.from_pretrained("dewdev/question_and_answer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fd384b7bf0eee5be0ecfa5d081003934e678e4e5bbce6950fb49810786af0746
- Size of remote file:
- 261 MB
- SHA256:
- 0e28afe23658cb018f3507106a07f29025ace312df02a44059a4162eed29847f
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.