Question Answering
Transformers
PyTorch
TensorBoard
Habana
roberta
Generated from Trainer
Eval Results (legacy)
Instructions to use nbroad/rob-base-superqa1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nbroad/rob-base-superqa1 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-superqa1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("nbroad/rob-base-superqa1") model = AutoModelForQuestionAnswering.from_pretrained("nbroad/rob-base-superqa1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add evaluation results on the adversarialQA config of adversarial_qa
Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋!\ Your model has been evaluated on the adversarialQA config of the [adversarial_qa](https://huggingface.co/datasets/adversarial_qa) dataset by @nbroad, using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-eval-project-adversarial_qa-b21f20c3-1305649900).\ Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=adversarial_qa).\ Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/model-evaluator?dataset=adversarial_qa).6e546e4 -
add datasets to frontmatter
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Merge branch 'main' of https://huggingface.co/nbroad/rob-base-superqa into main
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results
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add task
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update model card README.md
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Model save
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Training in progress, epoch 3
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Training in progress, epoch 2
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Training in progress, epoch 1
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initial commit
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