shmelev/genatator-gena-rmt-large-unet-multispecies-segmentation

GENATATOR-GENA-RMT-Large-UNET (Multispecies Gene Segmentation Model)

Overview

GENATATOR-GENA-RMT-Large-UNET is a DNA language model designed for gene segmentation directly from genomic DNA sequences.

The model performs nucleotide-level multilabel classification and predicts five gene structure classes:

Class	Description
5UTR	5′ untranslated region
exon	exon
intron	intron
3UTR	3′ untranslated region
CDS	coding sequence

The order of output classes in the model is:

["5UTR", "exon", "intron", "3UTR", "CDS"]

The model outputs one logit vector per nucleotide, allowing reconstruction of complete gene structures.

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Model

Model name on Hugging Face:

genatator-gena-rmt-large-unet-multispecies-segmentation

Architecture properties:

• backbone: GENA-LM BERT Large
• layers: 24
• hidden size: 1024
• attention heads: 16
• tokenization: GENA tokenization
• segmentation head: RMT encoder + 1D UNET refinement
• output head: linear projection to 5 classes
• context handling: recurrent memory segmentation
• recommended maximum sequence length for practical inference: up to 1,000,000 nucleotides

The architecture combines three components:

1. GENA-LM backbone (BERT Large) A pretrained genomic language model that produces contextual sequence representations.

2. Recurrent Memory Transformer (RMT) Long sequences are processed in segments with memory tokens, allowing the model to operate beyond the base backbone context length.

3. 1D UNET segmentation head Token-level representations are repeated to nucleotide resolution and refined with repeated 1D convolutional UNET passes before final projection to the five output classes.

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Batch Size Limitation

The current implementation supports batch size = 1 only.

This limitation comes from the 1D convolutional UNET refinement head used after token repetition to nucleotide resolution. Inference is therefore currently performed one sequence at a time.

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Context Length

This model is not limited to a fixed short context window in the usual backbone sense because it uses RMT-based segmented processing with memory tokens.

In practice, we currently recommend using sequences of no more than 1,000,000 nucleotides per inference run.

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Training Data

This model was fine-tuned on gene sequences only, not on full genomes.

Training data includes:

• mRNA transcripts
• lncRNA transcripts

Dataset characteristics:

• one transcript per gene
• no intergenic regions
• multispecies training dataset

Each training sample corresponds to a single gene sequence.

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Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification

repo_id = "shmelev/genatator-gena-rmt-large-unet-multispecies-segmentation"

tokenizer = AutoTokenizer.from_pretrained(
    repo_id,
    trust_remote_code=True,
)

model = AutoModelForTokenClassification.from_pretrained(
    repo_id,
    trust_remote_code=True,
)

model.eval()

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Example Inference

Note: only one sequence should be passed at a time.

import torch
from transformers import AutoTokenizer, AutoModelForTokenClassification

repo_id = "shmelev/genatator-gena-rmt-large-unet-multispecies-segmentation"

tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForTokenClassification.from_pretrained(repo_id, trust_remote_code=True)

sequence = "ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGT"

enc = tokenizer([sequence])
input_ids = torch.tensor(enc["input_ids"])

with torch.no_grad():
    outputs = model(input_ids=input_ids)

logits = outputs["logits"]

print("Input shape:", input_ids.shape)
print("Logits shape:", logits.shape)

Example output:

Input shape: torch.Size([1, sequence_length])
Logits shape: torch.Size([1, sequence_length, 5])

Each nucleotide receives 5 logits corresponding to the gene structure classes.