Deep Lake

Deep Lake

activeloop
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About

Generative AI may be new, but we've been building for this day for the past 5 years. Deep Lake thus combines the power of both data lakes and vector databases to build and fine-tune enterprise-grade, LLM-based solutions, and iteratively improve them over time. Vector search does not resolve retrieval. To solve it, you need a serverless query for multi-modal data, including embeddings or metadata. Filter, search, & more from the cloud or your laptop. Visualize and understand your data, as well as the embeddings. Track & compare versions over time to improve your data & your model. Competitive businesses are not built on OpenAI APIs. Fine-tune your LLMs on your data. Efficiently stream data from remote storage to the GPUs as models are trained. Deep Lake datasets are visualized right in your browser or Jupyter Notebook. Instantly retrieve different versions of your data, materialize new datasets via queries on the fly, and stream them to PyTorch or TensorFlow.

About

E5 Text Embeddings, developed by Microsoft, are advanced models designed to convert textual data into meaningful vector representations, enhancing tasks like semantic search and information retrieval. These models are trained using weakly-supervised contrastive learning on a vast dataset of over one billion text pairs, enabling them to capture intricate semantic relationships across multiple languages. The E5 family includes models of varying sizes—small, base, and large—offering a balance between computational efficiency and embedding quality. Additionally, multilingual versions of these models have been fine-tuned to support diverse languages, ensuring broad applicability in global contexts. Comprehensive evaluations demonstrate that E5 models achieve performance on par with state-of-the-art, English-only models of similar sizes.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Developers in search of a tool to store, deploy, and fine-tune their own LLM models

Audience

E5 Text Embeddings are designed for AI researchers, machine learning engineers, and developers seeking high-quality text representations for applications like semantic search, information retrieval, and multilingual NLP tasks

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

No images available

Pricing

$995 per month
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

activeloop
United States
www.activeloop.ai/

Company Information

Microsoft
Founded: 1975
United States
github.com/microsoft/unilm/tree/master/e5

Alternatives

Alternatives

Nomic Atlas

Nomic Atlas

Nomic AI
word2vec

word2vec

Google
txtai

txtai

NeuML

Categories

Categories

Integrations

Amazon SageMaker
Amazon Web Services (AWS)
ChatGPT
Google Cloud Platform
Jupyter Notebook
LangChain
OpenAI
PyTorch
TensorFlow

Integrations

Amazon SageMaker
Amazon Web Services (AWS)
ChatGPT
Google Cloud Platform
Jupyter Notebook
LangChain
OpenAI
PyTorch
TensorFlow
Claim Deep Lake and update features and information
Claim Deep Lake and update features and information
Claim E5 Text Embeddings and update features and information
Claim E5 Text Embeddings and update features and information