Instructions to use jdavit/roberta-large-QA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jdavit/roberta-large-QA 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="jdavit/roberta-large-QA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("jdavit/roberta-large-QA") model = AutoModelForQuestionAnswering.from_pretrained("jdavit/roberta-large-QA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jdavit/roberta-large-QA: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/jdavit/roberta-large-QA/resolve/main/training_args.bin
- Command line
-
hf download hf://jdavit/roberta-large-QA/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jdavit/roberta-large-QA/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- db3555e10027a54bef9e5fad333152df57aa6be9d7fb14c17260ef4884871fa4
- Size of remote file:
- 5.24 kB
- SHA256:
- 0bf4260039df362c35336a48d3b513ff1e7b02cccf6b2d398cf1e5b2801500a5
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