Embetter

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embetter provides scikit-learn compatible embeddings for computer vision and text, enabling rapid proof-of-concept building. It integrates seamlessly into scikit-learn pipelines for quick development.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is embetter?

embetter is an open-source Python library designed to provide useful embeddings for scikit-learn pipelines, making it easy to quickly build proof of concepts for machine learning tasks. It offers scikit-learn compatible embeddings for both computer vision and text data, simplifying the integration of advanced embedding techniques into existing workflows. The library is particularly helpful for bulk labeling efforts and plays well with tools like scikit-partial for handling out-of-core datasets. It includes components for grabbing data from pandas DataFrames, various encoders for images (TimmEncoder, ColorHistogramEncoder) and text (SentenceEncoder, MatryoshkaEncoder), and multi-modal models like ClipEncoder. Additionally, it supports finetuning components and external embedding providers requiring API keys, such as Cohere and OpenAI.

Best used for

Ideal for data scientists and developers who need to rapidly build machine learning proof of concepts, integrate advanced embeddings into scikit-learn pipelines, and handle bulk data labeling. Especially valuable for those working with both computer vision and text data, seeking pre-trained models for quick deployment.

Common actions

integrate embeddings

build ML pipelines

process vision data

process text data

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Capabilities

Key features

Scikit-learn compatible embeddings
Computer vision encoders
Text encoders
Multi-modal encoders
Finetuning components
External API integrations

Target Audience

data scientistdeveloper

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of embeddings does embetter support?

embetter supports a variety of embeddings for both computer vision and text. This includes image encoders like TimmEncoder and ColorHistogramEncoder, text encoders such as SentenceEncoder and MatryoshkaEncoder, and multi-modal encoders like ClipEncoder. It also allows integration with external providers like Cohere and OpenAI.

Is embetter compatible with scikit-learn pipelines?

Yes, all components within embetter are designed to be fully compatible with scikit-learn pipelines. This means you can easily integrate its embedding tools into your existing scikit-learn workflows, treating them as stateless, pre-trained transformers within your pipeline.

Can embetter handle large datasets or out-of-core learning?

Yes, the encoding tools in embetter are compatible with scikit-learn's `partial_fit` mechanic. This allows you to leverage libraries like scikit-partial to build pipelines that can effectively handle out-of-core datasets, making it suitable for larger-scale data processing.

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Data & Analytics › Data Labeling & Annotation

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