Javanese DistilBERT Small IMDB Classifier is a movie-classification model based on the DistilBERT model. It was trained on Javanese IMDB movie reviews.
The model was originally
w11wo/javanese-distilbert-small-imdb which is then fine-tuned on the
w11wo/imdb-javanese dataset consisting of Javanese IMDB movie reviews. It achieved an accuracy of 76.04% on the validation dataset. Many of the techniques used are based on a Hugging Face tutorial notebook written by Sylvain Gugger.
Trainer class from the Transformers library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with TensorFlow nonetheless.
|Model||#params||Arch.||Training/Validation data (text)|
||66M||DistilBERT Small||Javanese IMDB (47.5 MB of text)|
The model was trained for 5 epochs and the following is the final result once the training ended.
|train loss||valid loss||accuracy||total time|
from transformers import pipeline pretrained_name = "w11wo/javanese-distilbert-small-imdb-classifier" nlp = pipeline( "sentiment-analysis", model=pretrained_name, tokenizer=pretrained_name ) nlp("Film sing apik banget!")
Do consider the biases which came from the IMDB review that may be carried over into the results of this model.
Javanese DistilBERT Small IMDB Classifier was trained and evaluated by Wilson Wongso. All computation and development are done on Google Colaboratory using their free GPU access.
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