GleghornLab/vec2vec_embeddings

Vec2Vec protein embeddings of PLAT

Frozen, pooled embeddings of every protein in GleghornLab/plat_data from ten encoders that read it three different ways: protein language models read the amino acid sequence, general text models read the sequence or the curator description, and an annotation encoder reads the typed annotation set. With every encoder embedding the same proteins, a translator between any two spaces can be trained and scored without running a large model. These are the embeddings behind the Gleghorn Lab's study of embedding translation between protein representation spaces.

Proteins	67,402 (PLAT train, valid and test)
Encoders	ESM2 8M, 35M, 150M, 650M and 3B; ModernBERT-base and -large; gpt-oss-20b and -120b; AT-Base
Vector	Mean then variance over tokens, concatenated: twice the encoder width, float32
Format	One gzip-compressed PyTorch dictionary per encoder, keyed by the raw input
Licence	CC BY 4.0, inheriting the UniProt data

Homology leakage

The embeddings carry PLAT's splits unchanged. PLAT was clustered at 40% identity before the split, but a later screen at 40% identity and 80% coverage of both sequences found a training neighbor for 12.57% of test proteins, so it is not homology-disjoint; the PLAT card gives the counts.

How it was built

1. Read every row of PLAT.
2. Ran each encoder frozen, in inference only, on the columns it reads, and truncated inputs instead of dropping them: sequences at 1,024 tokens, descriptions at 2,048 and annotation sets at 192.
3. Pooled each input's token embeddings into their mean and variance and concatenated the two, mean first.
4. Stored one vector per distinct input in a dictionary per encoder, so proteins with identical inputs share one entry, and saved it with torch.save under gzip.

Columns

Each file is a dictionary from an input to its vector.

Key	Encoders	Example
amino acid sequence, str	ESM2, ModernBERT, gpt-oss	"MDQPQFSGAPRFLTRPKAFV..."
description text, str	ModernBERT, gpt-oss	"The following text describes a protein: ..."
annotation ids, tuple of int	AT-Base	(2459, 2633, 6640, ...)

A description or sequence is looked up exactly as it appears in PLAT, and an annotation set as the tuple of its annotation list. For example, the ESM2-8 file holds 67,402 sequences with vectors of 640 values, ModernBERT-base holds 107,096 keys (67,402 sequences and 39,694 distinct descriptions) with 1,536, and AT-Base holds 53,371 distinct annotation sets with 1,024.

Example rows

import gzip, io, torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download

path = hf_hub_download("GleghornLab/vec2vec_embeddings",
                       "embeddings/ESM2-650_False_mean_var.pth.gz", repo_type="dataset")
with gzip.open(path, "rb") as handle:
    table = torch.load(io.BytesIO(handle.read()), weights_only=True)

plat = load_dataset("GleghornLab/plat_data", split="test")
x = torch.stack([table[seq] for seq in plat["sequence"]])  # (3420, 2560)

Load with weights_only=True: the files are pickle containers, and nothing else is needed to read them.

What a model trained on it may say

A translator trained on these vectors maps one encoder's pooled space to another's. Its scores say how much of one space can be recovered from the other on PLAT's proteins, under mean and variance pooling and these truncation lengths. They do not say how the full token-level representations relate, and an encoder's pooled vector on a long protein covers only its first 1,024 tokens.

Sources and licence

The inputs come from PLAT, built from UniProtKB under CC BY 4.0; cite UniProt when you use them. The encoders and their licences:

Encoder	Model	Reads	Licence
ESM2 8M to 3B	facebook/esm2 family	sequence	MIT
ModernBERT-base, -large	answerdotai/ModernBERT-base, -large	sequence, description	Apache 2.0
gpt-oss-20b, -120b	openai/gpt-oss-20b, -120b	sequence, description	Apache 2.0
AT-Base	Annotation Transformer of the Gleghorn Lab, not public	annotation set

Files

File	Encoder	Keys
embeddings/ESM2-8_False_mean_var.pth.gz	ESM2 8M	sequence
embeddings/ESM2-35_False_mean_var.pth.gz	ESM2 35M	sequence
embeddings/ESM2-150_False_mean_var.pth.gz	ESM2 150M	sequence
embeddings/ESM2-650_False_mean_var.pth.gz	ESM2 650M	sequence
embeddings/ESM2-3B_False_mean_var.pth.gz	ESM2 3B	sequence
embeddings/custom---answerdotai_ModernBERT-base_False_mean_var.pth.gz	ModernBERT-base	sequence, description
embeddings/custom---answerdotai_ModernBERT-large_False_mean_var.pth.gz	ModernBERT-large	sequence, description
embeddings/custom---openai_gpt-oss-20b_False_mean_var.pth.gz	gpt-oss-20b	sequence, description
embeddings/gpt-oss-120b_False_mean_var.pth.gz	gpt-oss-120b	sequence, description
embeddings/at_AT-Base_best_pad192_mean_var.pth.gz	AT-Base	annotation set