fastembed-bio
Fast, lightweight biological sequence embeddings using ONNX. Built on FastEmbed.
Why fastembed-bio?
-
Light: No GPU required. No PyTorch. Just ONNX Runtime. Perfect for serverless and resource-constrained environments.
-
Fast: ONNX Runtime is faster than PyTorch inference. Batch processing and parallelism built-in.
-
Simple: Same interface patterns as FastEmbed. If you've used FastEmbed for text, you already know how to use this.
Installation
pip install fastembed-bio
Quickstart
from fastembed.bio import ProteinEmbedding
sequences = [
"MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG",
"GKGDPKKPRGKMSSYAFFVQTSREEHKKKHPDASVNFSEFSKKCSERWKTMSAKEKGKFEDMAK",
]
model = ProteinEmbedding("facebook/esm2_t12_35M_UR50D")
embeddings = list(model.embed(sequences))
# [
# array([-0.0055, -0.0144, 0.0355, -0.0049, ...], dtype=float32),
# array([ 0.0114, 0.0020, -0.0247, 0.0060, ...], dtype=float32)
# ]
Supported Models
Protein Embeddings
| Model | Parameters | Dimensions | Description |
|---|---|---|---|
facebook/esm2_t12_35M_UR50D |
35M | 480 | ESM-2 protein language model |
from fastembed.bio import ProteinEmbedding
model = ProteinEmbedding("facebook/esm2_t12_35M_UR50D")
embeddings = list(model.embed(["MKTVRQERLKS", "GKGDPKKPRGK"]))
DNA Embeddings (Coming Soon)
DNABert and similar models for DNA sequence embeddings.
RNA Embeddings (Coming Soon)
RNA foundation models for RNA sequence embeddings.
GPU Support
from fastembed.bio import ProteinEmbedding
model = ProteinEmbedding(
"facebook/esm2_t12_35M_UR50D",
providers=["CUDAExecutionProvider"]
)
Requires onnxruntime-gpu instead of onnxruntime.
Relationship to FastEmbed
This project is a community-driven fork of FastEmbed focused on biological sequence embeddings. It uses the same core infrastructure (ONNX models, model management, etc.) but is specialized for proteins, DNA, and RNA.
The goal is to make biological embeddings as accessible and efficient as text embeddings.
Contributing
Contributions welcome! Areas of interest:
- Additional ESM-2 model sizes
- DNABert and other DNA models
- RNA foundation models
- Performance optimizations
License
Apache 2.0
Metadata
Release files for fastembed-bio 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| fastembed_bio-0.1.2.tar.gz | 21.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| fastembed_bio-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 46.6 kB
Release files / fastembed_bio-0.1.2.tar.gz
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