Instructions to use Falconsai/question_answering_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Falconsai/question_answering_v2 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="Falconsai/question_answering_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Falconsai/question_answering_v2") model = AutoModelForQuestionAnswering.from_pretrained("Falconsai/question_answering_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Falconsai/question_answering_v2: direct link, hf CLI and curl.
- Browser
- Download file 4.16 kB
-
https://huggingface.co/Falconsai/question_answering_v2/resolve/refs%2Fpr%2F2/training_args.bin
- Command line
-
hf download hf://Falconsai/question_answering_v2@refs/pr/2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Falconsai/question_answering_v2/resolve/refs%2Fpr%2F2/training_args.bin
4.16 kB
- Xet hash:
- cd6332561fc47c376c32843ed559420934ec4328d42dea774a7c1d790b893db5
- Size of remote file:
- 4.16 kB
- SHA256:
- e8dd5fba4b0c8f192ad2f5be92e69c6c26e315df363baae504e67849c5fca30a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.