These tutorials show you can use your favorite packages with Argilla.


FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints.


BentoML is an open platform that simplifies ML model deployment and enables you to serve your models at production scale in minutes.


DVC is built to make ML models shareable and reproducible. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.

Google Colab

Colab notebooks allow you to combine executable code and rich text in a single document, along with images, HTML, LaTeX and more.


spaCy is a free open-source library for Natural Language Processing in Python.


Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages.

Hugging Face Transformers

Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models.

Hugging Face Disaggregators

Addressing fairness and bias in machine learning models is more important than ever!

Sentence Transformers

SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings.


SetFit is an efficient and prompt-free framework for few-shot fine-tuning of Sentence Transformers.


A very simple framework for state-of-the-art NLP. Developed by Humboldt University of Berlin and friends.


Small-Text provides state-of-the-art Active Learning for Text Classification.


modAL is an active learning framework for Python3, designed with modularity, flexibility and extensibility in mind.

Classy Classification

An intuitive approach to few-shot classification using sentence-transformers or spaCy models, or zero-shot classification with Huggingface.


Skweak: A software toolkit for weak supervision applied to NLP tasks, machine learning models, or even annotations from crowd-workers.


A system for quickly generating training data with weak supervision.

Transformers Interpret

Transformers Interpret is a model explainability tool designed to work exclusively with the transformers package.


The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.


SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model.


OpenAI is a suite of APIs powered by large language and vision models which perform a variety of natural language and visual tasks.


The LangChain framework is a wrapper around LLM models that allows for easier data-aware and agent-based LLM models.


Unstructured provides open-source components for pre-processing text documents such as PDFs, HTML and Word Documents.