Instructions to use ppsingh/roberta-finetuned-qa-policy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ppsingh/roberta-finetuned-qa-policy 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="ppsingh/roberta-finetuned-qa-policy")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ppsingh/roberta-finetuned-qa-policy") model = AutoModelForQuestionAnswering.from_pretrained("ppsingh/roberta-finetuned-qa-policy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ppsingh/roberta-finetuned-qa-policy: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/ppsingh/roberta-finetuned-qa-policy/resolve/main/training_args.bin
- Command line
-
hf download hf://ppsingh/roberta-finetuned-qa-policy/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ppsingh/roberta-finetuned-qa-policy/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- 5c196eca010a03fcd35979247bf730ba89b47f80fa86b699ee150e4717669cdd
- Size of remote file:
- 3.52 kB
- SHA256:
- 10a0fda293f663fda2de51f822ba06729646c8ebafd725aad6861669aefc7df7
路
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