Update README.md

README.md

@@ -9,86 +9,92 @@ These models are:
 - 100x faster than using LLMs for similar tasks
 - Easy to fine tune
 
+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 
+
 ### To run the models:
 Use https://github.com/nlmatics/nlm-model-service
+
 ### To acccess the models 
 Use https://github.com/nlmatics/nlm-utils 
 
+### To train the models
+TBD
+
 ## List of Models
 
 Click on each model to see details:
 
 ### roberta.large.boolq
 
-Location: [roberta.large.boolq](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.boolq)  
+*Location:* [roberta.large.boolq](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.boolq)  
 
 Trained with MNLI + Boolq
 
-Trained by: Evan Li
+*Trained by:* Evan Li
 
-Application: Given a passage and a question, answer the question with yes, no or unsure.
+*Application:* Given a passage and a question, answer the question with yes, no or unsure.
 
-Training Process: https://blogs.nlmatics.com/2020/03/12/Boolean-Question-Answering-with-Neutral-Labels.html
+*Training Process:* https://blogs.nlmatics.com/2020/03/12/Boolean-Question-Answering-with-Neutral-Labels.html
 
 ### roberta.large.qa
 See folder: [roberta.large.qa](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.qa)  
 
 Trained with SQuAD 2.0 + Custom Dataset preferring shorter spans better suited for data extraction
 
-Trained by: Ambika Sukla
+*Trained by:* Ambika Sukla
 
-Application: Given a passage and a question, pick the shortest span from the passage that answers the question
+*Application:* Given a passage and a question, pick the shortest span from the passage that answers the question
 
-Training Process: start, end location head on the top of Roberta Base
+*Training Process:* start, end location head on the top of Roberta Base
 
 ### roberta.large.stsb
-See folder: [roberta.large.stsb](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.stsb)
+*See folder:* [roberta.large.stsb](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.stsb)
 
 Trained with STSB dataset
 
-Trained by: Meta/Fairseq
+*Trained by:* Meta/Fairseq
 
-Application: Given two passages, return a score beteen 0 and 1 to evaluate their similarity
+*Application:* Given two passages, return a score beteen 0 and 1 to evaluate their similarity
 
-Training Process: regression head on top of Roberta Base
+*Training Process:* regression head on top of Roberta Base
 
 ### roberta.large.phraseqa
-See folder: [roberta.large.phraseqa](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.phraseqa)
+*See folder:* [roberta.large.phraseqa](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.phraseqa)
 
 Trained with Roberta 2.0 with the question words removed from the question
 
-Trained By: Batya Stein
+*Trained By:* Batya Stein
 
-Application: Given a passage and phrase (key), extract a value from the passage 
+*Application:* Given a passage and phrase (key), extract a value from the passage 
 
-Training Process: https://blogs.nlmatics.com/2020/08/25/Optimizing-Transformer-Q&A-Models-for-Naturalistic-Search.html
+*Training Process:* https://blogs.nlmatics.com/2020/08/25/Optimizing-Transformer-Q&A-Models-for-Naturalistic-Search.html
 
 ### roberta.large.qasrl
 
-See folder: [roberta.large.qasrl](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.qasrl)
+*See folder:* [roberta.large.qasrl](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.qasrl)
 
 Trained with QASRL dataset
 
-Application: Given a passage, get back values for who, what, when, where etc.
+*Application:* Given a passage, get back values for who, what, when, where etc.
 
-Trained By: Nima Sheikholeslami
+*Trained By:* Nima Sheikholeslami
 
 ### roberta.large.qatype.lower.RothWithQ
 
-See folder: [roberta.large.qatype.lower.RothWithQ](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.qatype.lower.RothWithQ)
+*See folder:* [roberta.large.qatype.lower.RothWithQ](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.qatype.lower.RothWithQ)
 
 Trained with the Roth Question Type dataset.
 
-Application: Given a question, return one of the answer types e.g. number, location. See the Roth dataset for full list. 
+*Application:* Given a question, return one of the answer types e.g. number, location. See the Roth dataset for full list. 
 
-Trained By: Evan Li
+*Trained By:* Evan Li
 
 ### roberta.large.io_qa
 
 See folder: [roberta.large.io_qa](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.io_qa)
 Trained with SQuAD 2.0 dataset
 
-Trained By: Nima Sheikholeslami
+*Trained By:* Nima Sheikholeslami
 
-Training Process: Use io head to support multiple spans.
+*Training Process:* Use io head to support multiple spans.