Fine-Tuning BERT With Custom Dataset
BERT stands for “Bidirectional Encoder Representations from Transformers." It is a pre-trained language model developed by Google that has been trained on a large corpus of text data to understand the contextual relationships between words (or sub-words) in a sentence. BERT has proven to be highly effective for various natural language processing tasks such as question answering, sentiment analysis, and text classification.
The primary technological advancement of BERT is the application of Transformer’s bidirectional training, a well-liked attention model, to language modeling. In contrast, earlier research looked at text sequences from either a left-to-right or a combined left-to-right and right-to-left training perspective. The study’s findings demonstrate that bidirectionally trained language models can comprehend the context and flow of language more deeply than single-direction language models. The authors of the paper describe a unique method called Masked LM (MLM), which makes bidirectional training possible in models where it was previously not practicable.
In this article, we’ll show you how to import a pre-trained Bert model from Hugging Face’s transformers library and fine-tune it for your own NLP classification task.
