Instructions to use ruyadisk/span_selection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ruyadisk/span_selection 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="ruyadisk/span_selection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ruyadisk/span_selection") model = AutoModelForQuestionAnswering.from_pretrained("ruyadisk/span_selection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ruyadisk/span_selection: direct link, hf CLI and curl.
- Browser
- Download file 1.3 GB
-
https://huggingface.co/ruyadisk/span_selection/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://ruyadisk/span_selection@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ruyadisk/span_selection/resolve/refs%2Fpr%2F1/pytorch_model.bin
1.3 GB
- Xet hash:
- 66b3b2b9a3db469428bb17868eda0cd4d7994fa078d79e877d88497734906cd5
- Size of remote file:
- 1.3 GB
- SHA256:
- 4f8f11331ca4fbd5d551000ef504b90ac66dcba6bf1150d40aec60cb06aa19a8
路
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