Generally, we use pre-trained language models trained on the large corpus to get embeddings and then mostly add a layer or two of neural networks on top to fit our task in hand. If we need to get accurate classification, we can use pre-trained models trained on the large corpus to get decent results.
We often have a large quantity of unlabelled dataset with only a small amount of labeled dataset. Implementation of entire code and explanations can be found on this repo. All the code can be found on the shared Github repository below.
#FINETUNE BERT MOVIE#
We will do all this on Google Colab using PyTorch-transformers with movie review’s data. We will also compare the results with using directly pre-trained Bert’s model.
#FINETUNE BERT HOW TO#
In this tutorial, I will show how one can finetune Bert’s language model and then how to use finetuned language model for sequence classification. We can also finetune Bert’s pre-trained language model to fit our task and then use that model to gain some improvements. Through Pytorch-transformers we can use Bert’s pre-trained language model for sequence classification. This has been made very easy by HuggingFace’s Pytorch-transformers. We will see how we can use Bert’s language model for the text classification task. Note: We won’t go into technical details but if one is interested in reading about transformers, this blog can be highly helpful. We can use language representations learned by BERT for our tasks such as text classification and so on to get state-of-art results on our problem. It has achieved state-of-the-art results on various NLP tasks. It is a bidirectional transformer pre-trained model developed using a combination of two tasks namely: masked language modeling objective and next sentence prediction on a large corpus.
Before we go ahead, let me give a brief introduction to BERT.īERT (Bidirectional Encoder Representations from Transformers) is a transformer-based method of learning language representations. If you are here, you have probably heard about BERT. Fine-tuning Bert language model to get better results on text classification
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