|
--- |
|
datasets: |
|
- imdb |
|
- cornell_movie_dialogue |
|
- polarity_movie_data |
|
- 25mlens_movie_data |
|
|
|
language: |
|
- English |
|
|
|
thumbnail: |
|
|
|
tags: |
|
- roberta |
|
- roberta-base |
|
- masked-language-modeling |
|
- masked-lm |
|
|
|
license: cc-by-4.0 |
|
|
|
--- |
|
# roberta-base for MLM |
|
|
|
Objective: To make a Roberta Base for the Movie Domain by using various Movie Datasets as simple text for Masked Language Modeling. |
|
This is the Movie Roberta to be used in Movie Domain applications. |
|
``` |
|
model_name = "thatdramebaazguy/movie-roberta-base" |
|
pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="Fill-Mask") |
|
``` |
|
## Overview |
|
**Language model:** roberta-base |
|
**Language:** English |
|
**Downstream-task:** Fill-Mask |
|
**Training data:** imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names |
|
**Eval data:** imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names |
|
**Infrastructure**: 4x Tesla v100 |
|
**Code:** See [example](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/scripts/shell_scripts/train_movie_roberta.sh) |
|
|
|
## Hyperparameters |
|
``` |
|
Num examples = 4767233 |
|
Num Epochs = 2 |
|
Instantaneous batch size per device = 20 |
|
Total train batch size (w. parallel, distributed & accumulation) = 80 |
|
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 |
|
|
|
perplexity = 5.0296 |
|
|
|
Some of my work: |
|
- [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/) |
|
|
|
--- |
|
|
