This folder contains examples and best practices, written in Jupyter notebooks, for building text classification models. We use the
utility scripts in the utils_nlp folder to speed up data preprocessing and model building for text classification.
The models can be used in a wide variety of applications, such as
sentiment analysis, document indexing in digital libraries, hate speech detection, and general-purpose categorization in medical, academic, legal, and many other domains.
Currently, we focus on fine-tuning pre-trained BERT
model. We plan to continue adding state-of-the-art models as they come up and welcome community
contributions.
Text classification is a supervised learning method of learning and predicting the category or the class of a document given its text content. The state-of-the-art methods are based on neural networks of different architectures as well as pre-trained language models or word embeddings.
The following summarizes each notebook for Text Classification. Each notebook provides more details and guiding in principles on building state of the art models.
| Notebook | Environment | Description | Dataset |
|---|---|---|---|
| BERT for text classification with MNLI | Local | A notebook which walks through fine-tuning and evaluating a pre-trained BERT model on a subset of the MultiNLI dataset | MultiNLI |
| BERT for text classification on AzureML | Azure ML | A notebook which walks through fine-tuning and evaluating pre-trained BERT model on a distributed setup with AzureML. | MultiNLI |