Instructions to use mbartolo/roberta-large-synqa-ext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbartolo/roberta-large-synqa-ext with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="mbartolo/roberta-large-synqa-ext")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mbartolo/roberta-large-synqa-ext") model = AutoModelForQuestionAnswering.from_pretrained("mbartolo/roberta-large-synqa-ext") - Notebooks
- Google Colab
- Kaggle
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# Model Overview
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This is a RoBERTa-Large QA Model trained from https://huggingface.co/roberta-large in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator on Wikipedia passages from SQuAD as well as Wikipedia passages external to SQuAD, and then it is trained on SQuAD and AdversarialQA in a second stage of fine-tuning.
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# Data
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Training data: SQuAD + AdversarialQA
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# Model Overview
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This is a RoBERTa-Large QA Model trained from https://huggingface.co/roberta-large in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator on Wikipedia passages from SQuAD as well as Wikipedia passages external to SQuAD, and then it is trained on SQuAD and AdversarialQA (https://arxiv.org/abs/2002.00293) in a second stage of fine-tuning.
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# Data
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Training data: SQuAD + AdversarialQA
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