autogluon.text
AutoML for Image, Text, and Tabular Data
Decision gist · record as of 2026-08-14
Yes, if you need to train text models quickly without deep ML expertise and can work within Python 3.7–3.9. The low install friction, active maintenance, permissive license, and zero known vulnerabilities make it a solid choice for prototyping and production use. No, if you require the latest Python versions or need fine-grained control over model architecture—use lower-level libraries like transformers or PyTorch instead.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.7–3.9 (capped below current versions); check your environment before installing.
- Low install friction with a single runtime dependency (autogluon.multimodal).
- The package is actively maintained with recent commits and has been in active development since its first release in 2020.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing free use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
last release 2023-01-11 (1311 days) · last repo commit 2026-08-14 · 10,596 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 84,617 downloads/mo, #13,983 on PyPI
Alternatives
Verify before relying
pip install autogluon.text
from autogluon.text import TextPredictor
predictor = TextPredictor(label='class').fit(train_data, time_limit=120)
leaderboard = predictor.leaderboard(test_data)- Whether autogluon.multimodal's own dependencies introduce significant additional install friction or system requirements.
- Current state of model download sizes and memory requirements for typical text prediction tasks.
- Performance characteristics and training time for different text dataset sizes.
What it is and what it does
autogluon.text is a specialized AutoML package for text classification and prediction tasks. It wraps AutoGluon's text prediction capabilities, allowing you to train deep learning models on text data with just a few lines of code—no manual feature engineering or hyperparameter tuning required. The package handles model selection, training, and evaluation automatically, targeting use cases where you have labeled text data and want a production-ready predictor quickly.
The package depends on autogluon.multimodal, which provides the underlying neural network infrastructure. It's designed for developers and data scientists who want to avoid the complexity of building text models from scratch but need reasonable accuracy without extensive ML expertise. The API mirrors AutoGluon's other predictors (TabularPredictor, ImagePredictor), making it familiar if you've used other AutoGluon modules.
Use it for
- Train a text classifier on customer feedback or support tickets to automatically categorize incoming messages.
- Build a sentiment analysis model on labeled reviews without writing custom neural network code.
- Quickly prototype a text-based prediction model for a Kaggle competition or proof-of-concept.
- Deploy a production text classifier that automatically selects and tunes models for your specific dataset.
- Combine text predictions with other data types using AutoGluon's multimodal capabilities.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to train text models quickly without deep ML expertise and can work within Python 3.7–3.9.
The low install friction, active maintenance, permissive license, and zero known vulnerabilities make it a solid choice for prototyping and production use. No, if you require the latest Python versions or need fine-grained control over model architecture—use lower-level libraries like transformers or PyTorch instead.
Install
autogluon-text on PyPI
Before you install
Low install friction with a single runtime dependency (autogluon.multimodal). The package is actively maintained with recent commits and has been in active development since its first release in 2020.
Requires Python 3.7–3.9 (capped below current versions); check your environment before installing.
License in practice
Licensed under Apache-2.0 (permissive), allowing free use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
Quickstart
pip install autogluon.text
from autogluon.text import TextPredictor
predictor = TextPredictor(label='class').fit(train_data, time_limit=120)
leaderboard = predictor.leaderboard(test_data)Verify before relying
- Whether autogluon.multimodal's own dependencies introduce significant additional install friction or system requirements.
- Current state of model download sizes and memory requirements for typical text prediction tasks.
- Performance characteristics and training time for different text dataset sizes.
Package facts
| License | Apache-2.0 permissive |
| Python support | Capped below the current Python release >=3.7, <3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packageautogluon.multimodal |
| Maintenance | Actively maintained 1,311 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 84,617 / month, #13,983 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Customer ServiceIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Financial and Insurance IndustryIntended Audience :: Healthcare IndustryIntended Audience :: Science/ResearchIntended Audience :: Telecommunications IndustryLicense :: OSI Approved :: Apache Software LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: Python :: 3Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Image RecognitionTopic :: Scientific/Engineering :: Information AnalysisTopic :: Software Development |
Evidence: autogluon.text-0.6.2-py3-none-any.whl
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See also autogluon · autogluon.vision · autogluon.multimodal · autogluon.features · autogluon.timeseries · autogluon.common · autogluon.tabular · autogluon.core · azureml-train-automl · cleanlab