Update README.md

README.md

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-Model Card coming soon!
+---
+datasets:
+- imdb
+- cornell_movie_dialogue 
+- SQuAD
+
+language: 
+- English
+
+thumbnail: 
+
+tags:
+- roberta
+- roberta-base
+- question-answering
+- qa
+- movies
+
+license: cc-by-4.0
+
+---
+# roberta-base + DAPT + Domain-Specific QA
+
+Objective:
+  This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the movie domain.  
+  https://huggingface.co/thatdramebaazguy/movie-roberta-base was used as the MovieRoberta.
+  
+```
+model_name = "thatdramebaazguy/movie-roberta-squad"
+pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="question-answering")
+```
+
+## Overview
+**Language model:** roberta-base  
+**Language:** English  
+**Downstream-task:** QA  
+**Training data:** imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names, SQuADv1  
+**Eval data:** MoviesQA (From https://github.com/ibm-aur-nlp/domain-specific-QA)  
+**Infrastructure**: 1x Tesla v100   
+**Code:**  See [example](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/scripts/shell_scripts/train_movieR_just_squadv1.sh)    
+
+## Hyperparameters
+```
+Num examples = 88567  
+Num Epochs = 10
+Instantaneous batch size per device = 32  
+Total train batch size (w. parallel, distributed & accumulation) = 32  
+
+``` 
+## Performance
+
+### Eval on MoviesQA
+- eval_samples =    5032
+- exact_match  = 50.1391
+- f1           = 64.8813  
+
+Github Repo: 
+- [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/)
+
+---