Instructions to use piEsposito/braquad-bert-qna with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use piEsposito/braquad-bert-qna with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="piEsposito/braquad-bert-qna")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("piEsposito/braquad-bert-qna") model = AutoModelForQuestionAnswering.from_pretrained("piEsposito/braquad-bert-qna", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7899b79990480bea12aa9c98033056d35576d545e73c63ba00a65fc0ac406f77
- Size of remote file:
- 433 MB
- SHA256:
- a186d178e4200aea383b3f6c35b6f625c2d1b0f9366a0214feef2c1ea071a4b7
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.