Update README.md

README.md

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-Model Card coming soon!
+---
+datasets:
+- imdb (Movie corpus for Domain Adaptive Pretraining)
+- cornell_movie_dialogue 
+- MIT Movie (NER Dataset)
+
+language: 
+- English
+
+thumbnail: 
+
+tags:
+- roberta
+- roberta-base
+- token-classification
+- NER
+- named-entities
+- BIO
+- movies
+- DAPT
+
+license: cc-by-4.0
+
+---
+# Movie Roberta + Movies NER Task
+
+Objective:
+  This is Roberta Base + Movie DAPT --> trained for the NER task using MIT Movie Dataset
+  https://huggingface.co/thatdramebaazguy/movie-roberta-base was used as the MovieRoberta.
+  
+```
+model_name = "thatdramebaazguy/movie-roberta-MITmovieroberta-base-MITmovie"
+pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="ner")
+```
+
+## Overview
+**Language model:** roberta-base  
+**Language:** English  
+**Downstream-task:** NER
+**Training data:** MIT Movie
+**Eval data:** MIT Movie
+**Infrastructure**: 2x Tesla v100   
+**Code:**  See [example](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/scripts/shell_scripts/movieR_NER_squad.sh)    
+
+## Hyperparameters
+```
+Num examples = 6253  
+Num Epochs = 5 
+Instantaneous batch size per device = 64
+Total train batch size (w. parallel, distributed & accumulation) = 128  
+
+``` 
+## Performance
+
+### Eval on MIT Movie
+- epoch                     =        5.0
+- eval_accuracy             =     0.9472
+- eval_f1                   =     0.8876
+- eval_loss                 =     0.2211
+- eval_mem_cpu_alloc_delta  =        3MB
+- eval_mem_cpu_peaked_delta =        2MB
+- eval_mem_gpu_alloc_delta  =        0MB
+- eval_mem_gpu_peaked_delta =       38MB
+- eval_precision            =      0.887
+- eval_recall               =     0.8881
+- eval_runtime              = 0:00:03.73
+- eval_samples              =       1955
+- eval_samples_per_second   =    523.095
+
+Github Repo: 
+- [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/)
+
+---