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---
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/)
---
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