Question Answering
Transformers
PyTorch
Graphcore
roberta
Generated from Trainer
Eval Results (legacy)
Instructions to use nbroad/rob-base-gc1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nbroad/rob-base-gc1 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="nbroad/rob-base-gc1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("nbroad/rob-base-gc1") model = AutoModelForQuestionAnswering.from_pretrained("nbroad/rob-base-gc1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nbroad/rob-base-gc1: direct link, hf CLI and curl.
- Browser
- Download file 248 MB
-
https://huggingface.co/nbroad/rob-base-gc1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nbroad/rob-base-gc1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nbroad/rob-base-gc1/resolve/main/pytorch_model.bin
248 MB
- Xet hash:
- 24c8cac992fdc1f78ed0d6e04c32baacfd913d6951d78e2d77e5eb6a87417889
- Size of remote file:
- 248 MB
- SHA256:
- 716be906cfe626d112737c0c651aa6b7475bc785da55c0a4f35db2b520154c2e
路
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