readme: ---
  datasets:
  - MIT Movie
  
  language: 
  - English
  
  thumbnail: 
  
  tags:
  - roberta
  - roberta-base
  - token-classification
  - NER
  - named-entities
  - BIO
  - movies
  
  license: cc-by-4.0
  
  ---
  # roberta-base + Movies NER Task
  
  Objective:
    This is Roberta Base trained for the NER task using MIT Movie Dataset
    
  ```
  model_name = "thatdramebaazguy/roberta-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             =     0.9476
  - eval_f1                   =     0.8853
  - eval_loss                 =     0.2208
  - eval_mem_cpu_alloc_delta  =       17MB
  - eval_mem_cpu_peaked_delta =        2MB
  - eval_mem_gpu_alloc_delta  =        0MB
  - eval_mem_gpu_peaked_delta =       38MB
  - eval_precision            =     0.8833
  - eval_recall               =     0.8874
  - eval_runtime              = 0:00:03.62
  - eval_samples              =       1955
  
  Github Repo: 
  - [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/)
  
  ---
  