update model card

README.md

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+This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> 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/movie-roberta-base was used as the MovieRoberta.
+
+---
+datasets:
+- imdb (Movie corpus for Domain Adaptive Pretraining)
+- cornell_movie_dialogue 
+- MIT Movie (NER Dataset)
+
+language: 
+- English
+
+thumbnail: 
+
+tags:
+- roberta
+- roberta-base
+- question-answering
+- qa
+- movies
+
+license: cc-by-4.0
+
+---
+# roberta-base for MLM 
+
+```
+model_name = "thatdramebaazguy/movie-roberta-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:** imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names, 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
+Gradient Accumulation steps = 1
+Total optimization steps = 119182
+eval_loss  = 1.6153
+eval_samples = 20573
+perplexity = 5.0296
+learning_rate=5e-05
+n_gpu = 4
+
+``` 
+## Performance
+
+### Eval on MoviesQA
+eval_samples =   10790
+2021-05-07 21:48:01,204 >>   exact_match  = 83.0274
+2021-05-07 21:48:01,204 >>   f1           = 90.1615
+
+Github Repo: 
+- [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/)
+
+---