# Terminology#

Within Argilla we decided to differentiate our docs using main terminology classes and corresponding sub-classes.

## Features#

Specific features that are covered by internal Argilla functionalities.

Terminology

Description

Datasets

Internal Dataset classes are lightweight containers for Argilla records.

Metrics

Argilla Metrics enables you to perform fine-grained analysis of your models and training datasets.

Queries

Argilla query functionalities are based on the powerful Elasticsearch query string syntax.

Semantic Search

This built-in search uses vectors for text and enables Approximate KNN for semantic search on these vectors.

## MLOps Steps#

All steps that we directly or in-directly cover within the MLOps lifecycle.

Terminology

Description

🏷 Labelling

manual or automatic data collection and label assignment.

💪🏽 Training

training and evaluation of NLP models

👨🏽‍💻 Deploying

logging inference/prediction of your ML models during their deployment.

📊 Monitoring

Dashboarding and evaluation of model performance.

Main task categories that we cover within the NLP landscape.

Terminology

Description

📕📗 TextClassification

Assigning predefined category labels to texts. This contains sub-tasks like detecting sentiment, semantic similarity, and multi-label classification.

🈴🈯️ TokenClassification

Assigning predefined category labels to words and phrases within texts . This contains sub-tasks like Named Entity Recognition (NER) and Part-Of-Speech Tagging (POS).

👨🏽💬 Text2Text

Generating a text based on an input text. This contains sub-tasks like machine translation, and paraphrase generation.

## Techniques#

Best practices and methods that can be applied during Machine Learning within our eco-system.

Terminology

Description

🍼 Basics

Simple straightforward basics for the one’s just getting started.

👨🏽‍🏫 Active Learning

Actively evaluate prediction certainties to determine labels that need to be evaluated for training.

👮 Weak Supervision

Use rules and functions to obtain initial annotations before manually correcting them.

🔎 Explainability and bias

understand and explain how a model produced a prediction and be aware of potential systematic errors.

🔫 Few-shot classification

Model and techniques that perform reasonably well using only a few or zero training samples.

🪞 Semantic Search

This built-in search uses vectors for text and enables Approximate KNN for semantic search on these vectors.