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@@ -18,10 +18,77 @@ Use https://github.com/nlmatics/nlm-utils
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  Click on each model to see details:
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- 1. [roberta.large.boolq](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.boolq)
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- 2. [roberta.large.qa](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.qa)
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- 3. [roberta.large.stsb](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.stsb)
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- 4. [roberta.large.phraseqa](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.phraseqa)
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- 5. [roberta.large.qasrl](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.qasrl)
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- 6. [roberta.large.qatype.lower.RothWithQ](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.qatype.lower.RothWithQ)
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- 7. [roberta.large.io_qa](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.io_qa)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  Click on each model to see details:
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+ ### roberta.large.boolq
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+ Location: [roberta.large.boolq](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.boolq)
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+ Trained with MNLI + Boolq
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+ Trained by: Evan Li
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+ Application: Given a passage and a question, answer the question with yes, no or unsure.
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+ Training Process: https://blogs.nlmatics.com/2020/03/12/Boolean-Question-Answering-with-Neutral-Labels.html
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+ ### roberta.large.qa
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+ See folder: [roberta.large.qa](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.qa)
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+ Trained with SQuAD 2.0 + Custom Dataset preferring shorter spans better suited for data extraction
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+ Trained by: Ambika Sukla
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+ Application: Given a passage and a question, pick the shortest span from the passage that answers the question
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+ Training Process: start, end location head on the top of Roberta Base
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+ ### roberta.large.stsb
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+ See folder: [roberta.large.stsb](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.stsb)
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+ Trained with STSB dataset
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+ Trained by: Meta/Fairseq
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+ Application: Given two passages, return a score beteen 0 and 1 to evaluate their similarity
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+ Training Process: regression head on top of Roberta Base
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+ ### roberta.large.phraseqa
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+ See folder: [roberta.large.phraseqa](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.phraseqa)
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+ Trained with Roberta 2.0 with the question words removed from the question
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+ Trained By: Batya Stein
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+ Application: Given a passage and phrase (key), extract a value from the passage
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+ Training Process: https://blogs.nlmatics.com/2020/08/25/Optimizing-Transformer-Q&A-Models-for-Naturalistic-Search.html
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+ ### roberta.large.qasrl
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+ See folder: [roberta.large.qasrl](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.qasrl)
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+ Trained with QASRL dataset
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+ Application: Given a passage, get back values for who, what, when, where etc.
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+ Trained By: Nima Sheikholeslami
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+ ### roberta.large.qatype.lower.RothWithQ
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+ See folder: [roberta.large.qatype.lower.RothWithQ](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.qatype.lower.RothWithQ)
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+ Trained with the Roth Question Type dataset.
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+ Application: Given a question, return one of the answer types e.g. number, location. See the Roth dataset for full list.
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+ Trained By: Evan Li
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+ ### roberta.large.io_qa
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+ See folder: [roberta.large.io_qa](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.io_qa)
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+ Trained with SQuAD 2.0 dataset
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+ Trained By: Nima Sheikholeslami
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+ Training Process: Use io head to support multiple spans.
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