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 training_args.bin from ToluClassics/extractive_reader_nq: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/ToluClassics/extractive_reader_nq/resolve/main/training_args.bin
- Command line
-
hf download hf://ToluClassics/extractive_reader_nq/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ToluClassics/extractive_reader_nq/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- 95385e54b8c69de0b9cd77283d53bfbc7c940e9a4b8350350974dd7e34e7a7e1
- Size of remote file:
- 3.64 kB
- SHA256:
- 92bed58ed33e338369e512b3498d2d4a491e571cb10d0ce744e47b9ac6db6fe4
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