Instructions to use Hansollll/Question_Answering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hansollll/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="Hansollll/Question_Answering")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Hansollll/Question_Answering") model = AutoModelForQuestionAnswering.from_pretrained("Hansollll/Question_Answering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hansollll/Question_Answering: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/Hansollll/Question_Answering/resolve/main/training_args.bin
- Command line
-
hf download hf://Hansollll/Question_Answering/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hansollll/Question_Answering/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 43ca93b270cd6b0b82547f9ec0f27a192542910e9e8982888f7282abf69feb34
- Size of remote file:
- 3.58 kB
- SHA256:
- 5127af807c67808f60e3477342a933cfbcd148ab3d40cab3a007389ae5c2b694
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.