|
--- |
|
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/) |
|
|
|
--- |
|
|