Skip to main content

Fast and Lightweight Text Embedding

Project description

LightEmbed

LightEmbed is a light-weight, fast, and efficient tool for generating sentence embeddings. It does not rely on heavy dependencies like PyTorch and Transformers, making it suitable for environments with limited resources.

Benefits

1. Light-weight

  • Minimal Dependencies: LightEmbed does not depend on PyTorch and Transformers.
  • Low Resource Requirements: Operates smoothly with minimal specs: 1GB RAM, 1 CPU, and no GPU required.

2. Fast (as light)

  • ONNX Runtime: Utilizes the ONNX runtime, which is significantly faster compared to Sentence Transformers that use PyTorch.

3. Same as Original Sentence Transformers' Outputs

  • Consistency: Incorporates all modules from a Sentence Transformer model, including normalization and pooling.
  • Accuracy: Produces embedding vectors identical to those from Sentence Transformers.

Installation

pip install -U light-embed

Usage

Then you can use the model like this:

from light_embed import TextEmbedding
sentences = ["This is an example sentence", "Each sentence is converted"]

model = TextEmbedding('sentence-transformers-model-name')
embeddings = model.encode(sentences)
print(embeddings)

For example:

from light_embed import TextEmbedding
sentences = ["This is an example sentence", "Each sentence is converted"]

model = TextEmbedding('sentence-transformers/all-MiniLM-L6-v2')
embeddings = model.encode(sentences)
print(embeddings)

Citing & Authors

Binh Nguyen / binhcode25@gmail.com

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

light_embed_awslambda-0.1.0.tar.gz (12.9 kB view details)

Uploaded Source

Built Distribution

light_embed_awslambda-0.1.0-py3-none-any.whl (15.2 kB view details)

Uploaded Python 3

File details

Details for the file light_embed_awslambda-0.1.0.tar.gz.

File metadata

  • Download URL: light_embed_awslambda-0.1.0.tar.gz
  • Upload date:
  • Size: 12.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.12.3

File hashes

Hashes for light_embed_awslambda-0.1.0.tar.gz
Algorithm Hash digest
SHA256 5ed2da71a39f2a126988b05ed3bdeaf918f829338269e9f335da146ed52434e7
MD5 6bcb4b0f22f49a720562ab2050fab063
BLAKE2b-256 6f1bbb758ab1474c285efd21888b8fb4e7378b7cfe173fa6b0746ab628da7ba2

See more details on using hashes here.

File details

Details for the file light_embed_awslambda-0.1.0-py3-none-any.whl.

File metadata

File hashes

Hashes for light_embed_awslambda-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 72f2ca637f26c990ca3d47bad9a8ad52cb14498cc481843aadab81455b85f8eb
MD5 2666716ec76d0bf83211658a88fd0237
BLAKE2b-256 0ab3a30ac681dc6b03253ef6787769ef97a14d6799d303126d8a831bc86c5381

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page