Instructions to use sv-adv/PoA-recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sv-adv/PoA-recognition 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="sv-adv/PoA-recognition")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("sv-adv/PoA-recognition") model = AutoModelForQuestionAnswering.from_pretrained("sv-adv/PoA-recognition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sv-adv/PoA-recognition: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/sv-adv/PoA-recognition/resolve/main/training_args.bin
- Command line
-
hf download hf://sv-adv/PoA-recognition/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sv-adv/PoA-recognition/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- f40595f75495c3cff922434c022e2f7292bafbdf40f262987e6446d624789d16
- Size of remote file:
- 5.11 kB
- SHA256:
- bc7738d036bb4a33eb29dd94529c473e95b792b0c1da56f951c162bda08f2a96
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