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 training_args.bin from nbroad/rob-base-gc1: direct link, hf CLI and curl.
- Browser
- Download file 2.67 kB
-
https://huggingface.co/nbroad/rob-base-gc1/resolve/main/training_args.bin
- Command line
-
hf download hf://nbroad/rob-base-gc1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nbroad/rob-base-gc1/resolve/main/training_args.bin
2.67 kB
- Xet hash:
- 01801e1439f16611c7940c1f272e9a75968b96f77a6b34e56c8319828def6c7b
- Size of remote file:
- 2.67 kB
- SHA256:
- 8c78bd2ef90e6c20a22b8e72f7c76c49b8f249b6537aff6f9a9f9ff2ab0745f7
路
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