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license: apache-2.0 |
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This repo offers a set of fairseq roberta based models fine tuned to specific NLP tasks. |
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These models are: |
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- Small |
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- Easy to host on T4 or V100 |
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- 100x faster than using LLMs for similar tasks |
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- Easy to fine tune |
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All the models below were trained at Nlmatics Corp. from 2019-2023 with base model from: https://github.com/facebookresearch/fairseq/blob/main/examples/roberta/README.md |
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### To run the models: |
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Use https://github.com/nlmatics/nlm-model-service |
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### To acccess the models |
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Use https://github.com/nlmatics/nlm-utils |
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### To train the models |
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TBD |
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## List of Models |
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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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