Instructions to use SalmonAI123/Question_answering_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SalmonAI123/Question_answering_test 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="SalmonAI123/Question_answering_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("SalmonAI123/Question_answering_test") model = AutoModelForQuestionAnswering.from_pretrained("SalmonAI123/Question_answering_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from SalmonAI123/Question_answering_test: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/SalmonAI123/Question_answering_test/resolve/main/training_args.bin
- Command line
-
hf download hf://SalmonAI123/Question_answering_test/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/SalmonAI123/Question_answering_test/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- d6f0b0fc981e3bb480b46bc8bacd837bbd21dbf975a245f5cb258e68cee8901c
- Size of remote file:
- 4.03 kB
- SHA256:
- 33ef9b290413729b0a56aa544071db2f15e40b024cbb4e7d54ac4eb635b6cf64
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.