AutoNLP: Auto training and fast deployment for state-of-the-art NLP models
AutoNLP is an automatic way to train, evaluate and deploy state-of-the-art NLP models for different tasks. Using AutoNLP, you can leave all the worries of selecting the best model, fine-tuning the model or even deploying the models and focus on the broader picture for your project/business.
Automatic selection of best models given your data
Automatic hyperparameter optimization
Model comparison after training
Immediate deployment after training
CLI and Python API available
Currently, AutoNLP supports the following tasks:
Binary classification: one sentence has one target associated with it and there are two unique targets in the dataset
Multi-class classification: one sentence has one target associated with it and there are more than two unique targets in the dataset
Entity extraction: also known as named entity recognition or token classification. This task consists of one sentence and in the sentence, each token is associated to a particular label
Currently, AutoNLP supports the following languages:
If the language you want to use is not listed, please create an issue here: https://github.com/huggingface/autonlp/issues and we will try our best to add the languages you need.