
# Model Overview
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, and then it is trained on SQuAD and AdversarialQA (https://arxiv.org/abs/2002.00293) in a second stage of fine-tuning.

# Data
Training data: SQuAD + AdversarialQA
Evaluation data: SQuAD + AdversarialQA

# Training Process
Approx. 1 training epoch on the synthetic data and 2 training epochs on the manually-curated data.

# Additional Information
Please refer to https://arxiv.org/abs/2104.08678 for full details.
{"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["adversarial_qa", "mbartolo/synQA", "squad"], "metrics": ["exact_match", "f1"], "model-index": [{"name": "mbartolo/roberta-large-synqa", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 89.6529, "name": "Exact Match", "verified": true}, {"type": "f1", "value": 94.8172, "name": "F1", "verified": true}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "adversarial_qa", "type": "adversarial_qa", "config": "adversarialQA", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 55.3333, "name": "Exact Match", "verified": true}, {"type": "f1", "value": 66.7464, "name": "F1", "verified": true}]}]}]}
transformers pytorch roberta question-answering en dataset:adversarial_qa dataset:mbartolo/synQA dataset:squad arxiv:2002.00293 arxiv:2104.08678 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #question-answering #en #dataset-adversarial_qa #dataset-mbartolo/synQA #dataset-squad #arxiv-2002.00293 #arxiv-2104.08678 #license-apache-2.0 #model-index #endpoints_compatible #region-us 


# Model Overview
This is a RoBERTa-Large QA Model trained from URL in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator on Wikipedia passages from SQuAD, and then it is trained on SQuAD and AdversarialQA (URL in a second stage of fine-tuning.

# Data
Training data: SQuAD + AdversarialQA
Evaluation data: SQuAD + AdversarialQA

# Training Process
Approx. 1 training epoch on the synthetic data and 2 training epochs on the manually-curated data.

# Additional Information
Please refer to URL for full details.
# Model Overview
This is a RoBERTa-Large QA Model trained from URL in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator on Wikipedia passages from SQuAD, and then it is trained on SQuAD and AdversarialQA (URL in a second stage of fine-tuning. # Data
Training data: SQuAD + AdversarialQA
Evaluation data: SQuAD + AdversarialQA # Training Process
Approx. 1 training epoch on the synthetic data and 2 training epochs on the manually-curated data. # Additional Information
Please refer to URL for full details.
TAGS
#transformers #pytorch #roberta #question-answering #en #dataset-adversarial_qa #dataset-mbartolo/synQA #dataset-squad #arxiv-2002.00293 #arxiv-2104.08678 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 # Model Overview
This is a RoBERTa-Large QA Model trained from URL in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator on Wikipedia passages from SQuAD, and then it is trained on SQuAD and AdversarialQA (URL in a second stage of fine-tuning. # Data
Training data: SQuAD + AdversarialQA
Evaluation data: SQuAD + AdversarialQA # Training Process
Approx. 1 training epoch on the synthetic data and 2 training epochs on the manually-curated data. # Additional Information
Please refer to URL for full details.
passage: TAGS
#transformers #pytorch #roberta #question-answering #en #dataset-adversarial_qa #dataset-mbartolo/synQA #dataset-squad #arxiv-2002.00293 #arxiv-2104.08678 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
# Model Overview
This is a RoBERTa-Large QA Model trained from URL in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator on Wikipedia passages from SQuAD, and then it is trained on SQuAD and AdversarialQA (URL in a second stage of fine-tuning.# Data
Training data: SQuAD + AdversarialQA
Evaluation data: SQuAD + AdversarialQA# Training Process
Approx. 1 training epoch on the synthetic data and 2 training epochs on the manually-curated data.# Additional Information
Please refer to URL for full details.