---
datasets:
- imdb
- cornell_movie_dialogue 
- MIT Movie

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/)

---