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Overview
Overview
Model2Vec
Introduction
Installation
Inference
Distillation
Training
Models
Results
SemHash
Introduction
Installation
Semantic Deduplication
Outlier Filtering
Representative Sampling
Benchmarks
Vicinity
Introduction
Installation
Usage
Supported Backends
Tokenlearn
Usage
Model2Vec-rs
Usage
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Overview
Overview of the packages we are currently working on
Model2Vec
(
docs
,
repo
): Create state-of-the-art static embedding models by distilling Sentence Transformers.
SemHash
(
docs
,
repo
): Fast semantic text deduplication, outlier detection, and representative sampling.
Vicinity
(
docs
,
repo
): A lightweight library for efficient nearest neighbor search that supports various backends.
Tokenlearn
(
docs
,
repo
): Our method to pre-train static embedding models.
Model2Vec-rs
(
docs
,
repo
): Rust-native implementation of Model2Vec for high performance.
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Responses are generated using AI and may contain mistakes.