Active Learning with AutoNLP and Prodigy
📰 Hugging Face Blog
Use AutoNLP and Prodigy to build an active learning pipeline for machine learning models
Action Steps
- Train a model using AutoNLP
- Use Prodigy to annotate and label data
- Retrain the model with the new labeled data
- Repeat the process to continuously improve the model
Who Needs to Know This
Data scientists and machine learning engineers can benefit from using AutoNLP and Prodigy to streamline their workflow and improve model accuracy
Key Insight
💡 Active learning with AutoNLP and Prodigy can improve model accuracy by iteratively adding labeled data and retraining the model
Share This
🚀 Boost your ML model's accuracy with active learning using AutoNLP and Prodigy!
Key Takeaways
Use AutoNLP and Prodigy to build an active learning pipeline for machine learning models
Full Article
Published Time: 2021-12-23T00:00:00.047Z
# Active Learning with AutoNLP and Prodigy
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# [](https://huggingface.co/blog/autonlp-prodigy#active-learning-with-autonlp-and-prodigy) Active Learning with AutoNLP and Prodigy
Published December 23, 2021
[Update on GitHub](https://github.com/huggingface/blog/blob/main/autonlp-prodigy.md)
[- [x] Upvote 2](https://huggingface.co/login?next=%2Fblog%2Fautonlp-prodigy)
* [](https://huggingface.co/omerfguzel "omerfguzel")
* [](https://huggingface.co/Kannav02 "Kannav02")
[](https://huggingface.co/abhishek)
[Abhishek Thakur abhishek Follow](https://huggingface.co/abhishek)
* [AutoNLP](https://huggingface.co/blog/autonlp-prodigy#autonlp "AutoNLP")
* [Prodigy](https://huggingface.co/blog/autonlp-prodigy#prodigy "Prodigy")
* [Dataset](https://huggingface.co/blog/autonlp-prodigy#dataset "Dataset")
* [Active Learning](https://huggingface.co/blog/autonlp-prodigy#active-learning "Active Learning")
Active learning in the context of Machine Learning is a process in which you iteratively add labeled data, retrain a model and serve it to the end user. It is an endless process and requires human interaction for labeling/creating the data. In this article, we will discuss how to use [AutoNLP](https://huggingface.co/autonlp) and [Prodigy](https://prodi.gy/) to build an active learning pipeline.
## [](https://huggingface.co/blog/autonlp-prodigy#autonlp) AutoNLP
[AutoNLP](https://huggingface.co/autonlp) is a framework created by Hugging Face that helps you to build your own state-of-the-art deep learning models on your own dataset with almost no coding at all. AutoNLP is built on the giant shoulders of Hugging Face's [transformers](https://github.com/huggingface/transformers), [datasets](https://github.com/huggingface/datasets), [inference-api](https://huggingface.co/inference-api) and many other tools.
With AutoNLP, you can train SOTA transformer models on your own custom dataset, fine-tune them (automatically) and serve them to the end-user. All models trained with AutoNLP are state-of-the-art and production-ready.
At the time of writing this article, AutoNLP supports tasks like binary classification, regression, multi class classification, token classification (such as named entity recognition or part of speech), question answering, summarization and more. You can find a list of all the supported tasks [here](https://huggingface.co/autonlp/). AutoNLP supports languages like English, French, German, Spanish, Hindi, Dutch, Swedish and many more. There is also support for custom models with custom tokenizers (in case your language is not supported by AutoNLP).
## [](https://huggingface.co/blog/autonlp-prodigy#prodigy) Prodigy
[Prodigy](https://prodi.gy/) is an annotation tool developed by Explosion (the makers of [spaCy](https://spacy.io/)). It is a web-based tool that allows you to annotate your data in real time. Prodigy supports NLP tasks such as named entity recognition (NER) and text classification, but it's not limited to NLP! It supports Computer Vision tasks and even crea
# Active Learning with AutoNLP and Prodigy
[Hugging Face](https://huggingface.co/)
* [Models](https://huggingface.co/models)
* [Datasets](https://huggingface.co/datasets)
* [Spaces](https://huggingface.co/spaces)
* [Buckets new](https://huggingface.co/storage)
* [Docs](https://huggingface.co/docs)
* [Enterprise](https://huggingface.co/enterprise)
* [Pricing](https://huggingface.co/pricing)
*
*
* * *
* [Log In](https://huggingface.co/login)
* [Sign Up](https://huggingface.co/join)
[Back to Articles](https://huggingface.co/blog)
# [](https://huggingface.co/blog/autonlp-prodigy#active-learning-with-autonlp-and-prodigy) Active Learning with AutoNLP and Prodigy
Published December 23, 2021
[Update on GitHub](https://github.com/huggingface/blog/blob/main/autonlp-prodigy.md)
[- [x] Upvote 2](https://huggingface.co/login?next=%2Fblog%2Fautonlp-prodigy)
* [](https://huggingface.co/omerfguzel "omerfguzel")
* [](https://huggingface.co/Kannav02 "Kannav02")
[](https://huggingface.co/abhishek)
[Abhishek Thakur abhishek Follow](https://huggingface.co/abhishek)
* [AutoNLP](https://huggingface.co/blog/autonlp-prodigy#autonlp "AutoNLP")
* [Prodigy](https://huggingface.co/blog/autonlp-prodigy#prodigy "Prodigy")
* [Dataset](https://huggingface.co/blog/autonlp-prodigy#dataset "Dataset")
* [Active Learning](https://huggingface.co/blog/autonlp-prodigy#active-learning "Active Learning")
Active learning in the context of Machine Learning is a process in which you iteratively add labeled data, retrain a model and serve it to the end user. It is an endless process and requires human interaction for labeling/creating the data. In this article, we will discuss how to use [AutoNLP](https://huggingface.co/autonlp) and [Prodigy](https://prodi.gy/) to build an active learning pipeline.
## [](https://huggingface.co/blog/autonlp-prodigy#autonlp) AutoNLP
[AutoNLP](https://huggingface.co/autonlp) is a framework created by Hugging Face that helps you to build your own state-of-the-art deep learning models on your own dataset with almost no coding at all. AutoNLP is built on the giant shoulders of Hugging Face's [transformers](https://github.com/huggingface/transformers), [datasets](https://github.com/huggingface/datasets), [inference-api](https://huggingface.co/inference-api) and many other tools.
With AutoNLP, you can train SOTA transformer models on your own custom dataset, fine-tune them (automatically) and serve them to the end-user. All models trained with AutoNLP are state-of-the-art and production-ready.
At the time of writing this article, AutoNLP supports tasks like binary classification, regression, multi class classification, token classification (such as named entity recognition or part of speech), question answering, summarization and more. You can find a list of all the supported tasks [here](https://huggingface.co/autonlp/). AutoNLP supports languages like English, French, German, Spanish, Hindi, Dutch, Swedish and many more. There is also support for custom models with custom tokenizers (in case your language is not supported by AutoNLP).
## [](https://huggingface.co/blog/autonlp-prodigy#prodigy) Prodigy
[Prodigy](https://prodi.gy/) is an annotation tool developed by Explosion (the makers of [spaCy](https://spacy.io/)). It is a web-based tool that allows you to annotate your data in real time. Prodigy supports NLP tasks such as named entity recognition (NER) and text classification, but it's not limited to NLP! It supports Computer Vision tasks and even crea
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