Create README.md

README.md

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+---
+datasets:
+- MIT Movie (NER Dataset)
+- SQuAD
+
+language: 
+- English
+
+thumbnail: 
+
+tags:
+- roberta
+- roberta-base
+- question-answering
+- qa
+- movies
+
+license: cc-by-4.0
+
+---
+# roberta-base + Task Transfer (NER) --> Domain-Specific QA
+
+Objective:
+  This is Roberta Base without any Domain Adaptive Pretraining --> Then trained for the NER task using MIT Movie Dataset --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the movie domain, with additional information coming from a different task (NER - Task Transfer).  
+  https://huggingface.co/thatdramebaazguy/roberta-base-MITmovie was used as the Roberta Base + NER model.
+  
+```
+model_name = "thatdramebaazguy/roberta-base-MITmovie-squad"
+pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="question-answering")
+```
+
+## Overview
+**Language model:** roberta-base  
+**Language:** English  
+**Downstream-task:** NER --> QA  
+**Training data:** MIT Movie, SQuADv1  
+**Eval data:** MoviesQA (From https://github.com/ibm-aur-nlp/domain-specific-QA)  
+**Infrastructure**: 4x Tesla v100   
+**Code:**  See [example](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/scripts/shell_scripts/movieR_NER_squad.sh)    
+
+## Hyperparameters
+```
+Num examples = 88567  
+Num Epochs = 3  
+Instantaneous batch size per device = 32  
+Total train batch size (w. parallel, distributed & accumulation) = 128  
+
+``` 
+## Performance
+
+### Eval on MoviesQA
+- eval_samples =    5032
+- exact_match  = 58.0684
+- f1           = 71.3717
+
+Github Repo: 
+- [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/)
+
+---