Instructions to use ToluClassics/extractive_reader_afroxlmr_fquad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ToluClassics/extractive_reader_afroxlmr_fquad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="ToluClassics/extractive_reader_afroxlmr_fquad")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ToluClassics/extractive_reader_afroxlmr_fquad") model = AutoModelForQuestionAnswering.from_pretrained("ToluClassics/extractive_reader_afroxlmr_fquad") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d9597f03b711bc36d9c50579a75b4c7c1277865955c1ec168a8a4e87ce951395
- Size of remote file:
- 3.63 kB
- SHA256:
- 5df8a3ab0a3b6a68eca08e30f142a58a864021c923d54f30136765bd9bf68627
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.