Instructions to use Hemg/Question-answering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemg/Question-answering 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="Hemg/Question-answering")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Hemg/Question-answering") model = AutoModelForQuestionAnswering.from_pretrained("Hemg/Question-answering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hemg/Question-answering: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/Hemg/Question-answering/resolve/main/training_args.bin
- Command line
-
hf download hf://Hemg/Question-answering/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hemg/Question-answering/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- db0d67dc85b9283b87c47fe746e7b497012766f635e2837dfe5459756760240c
- Size of remote file:
- 4.92 kB
- SHA256:
- 1a1272c31dabb072e7a727c38033e37b5505d422303b365354f5a626766cc575
路
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