Instructions to use ToluClassics/extractive_reader_nq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ToluClassics/extractive_reader_nq with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="ToluClassics/extractive_reader_nq")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ToluClassics/extractive_reader_nq") model = AutoModelForQuestionAnswering.from_pretrained("ToluClassics/extractive_reader_nq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ToluClassics/extractive_reader_nq: direct link, hf CLI and curl.
- Browser
- Download file 667 MB
-
https://huggingface.co/ToluClassics/extractive_reader_nq/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ToluClassics/extractive_reader_nq/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ToluClassics/extractive_reader_nq/resolve/main/pytorch_model.bin
667 MB
- Xet hash:
- ae3bdd39d18392b6554d4c22cb6626c4bffbfadb140c42bfd6a5e887d525baf6
- Size of remote file:
- 667 MB
- SHA256:
- bfacfdec7a269d6807bf690be3a6977dfe05c5554e3d1d7ddf63768e004d35cf
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.