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 from transformers import pipeline pipe = pipeline("question-answering", model="nbroad/rob-base-gc1")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("nbroad/rob-base-gc1") model = AutoModelForQuestionAnswering.from_pretrained("nbroad/rob-base-gc1") - Notebooks
- Google Colab
- Kaggle
Add evaluation results on the default config of quoref
#3
by autoevaluator HF Staff - opened
README.md
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- duorc
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model-index:
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- name: rob-base-gc1
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results:
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- duorc
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model-index:
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- name: rob-base-gc1
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results:
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- task:
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type: question-answering
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name: Question Answering
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dataset:
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name: quoref
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type: quoref
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config: default
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split: validation
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metrics:
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- name: Exact Match
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type: exact_match
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value: 78.403
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verified: true
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- name: F1
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type: f1
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value: 82.1408
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verified: true
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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