⚠️ Input format: this model uses a Geneformer-style WordLevel tokenizer whose vocabulary is ENSEMBL gene IDs only (e.g. ENSG00000000003). Plain nucleotide strings (AUGCAUGC...) or free text will NOT tokenize correctly — they collapse to one [UNK] and the MLM head will return an ENSG-id at that mask position by design. See the Example Output below for the correct list-of-tokens calling convention.

molcrawl-rna-bert-small

Model Description

GPT-2 small (124M parameters) foundation model pre-trained on RNA gene expression sequences from the MolCrawl dataset.

• Model Type: bert
• Data Type: RNA
• Training Date: 2026-04-24

Usage

from transformers import AutoModelForMaskedLM, AutoTokenizer
import torch

model = AutoModelForMaskedLM.from_pretrained("kojima-lab/molcrawl-rna-bert-small")
tokenizer = AutoTokenizer.from_pretrained("kojima-lab/molcrawl-rna-bert-small")

# Predict masked RNA token
# Use tokenizer.mask_token instead of hardcoded "[MASK]":
# BERT-style tokenizers vary ("[MASK]", "<mask>", etc.)
if tokenizer.mask_token is None:
    raise ValueError("This tokenizer has no mask_token; masked LM inference is not supported.")
prompt = "AUGCAUGC{MASK}AUGCAUGC".replace("{MASK}", tokenizer.mask_token)
inputs = tokenizer(prompt, return_tensors="pt")
mask_index = (inputs["input_ids"] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]

with torch.no_grad():
    outputs = model(**inputs)
logits = outputs.logits

predicted_token_id = logits[0, mask_index].argmax(dim=-1)
predicted_token = tokenizer.decode(predicted_token_id)
result = prompt.replace(tokenizer.mask_token, predicted_token)
print(f"Predicted: {result}")

Source Code

Training pipeline, configuration files, and data preparation scripts are available in the MolCrawl GitHub repository: https://github.com/mmai-framework-lab/MolCrawl

License

This model is released under the APACHE-2.0 license.

Citation

If you use this model, please cite:

@misc{molcrawl_rna_bert_small,
  title={molcrawl-rna-bert-small},
  author={{RIKEN}},
  year={2026},
  publisher={{Hugging Face}},
  url={{https://huggingface.co/kojima-lab/molcrawl-rna-bert-small}}
}

Example Output

Inference test (CPU). Important: this tokenizer uses a WordLevel gene-ID vocabulary without a text-mode pre_tokenizer, so feed input as a list of ENSEMBL IDs (and the mask token) directly into convert_tokens_to_ids, not as a single text string.

import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM

REPO_ID = "kojima-lab/molcrawl-rna-bert-small"
tokenizer = AutoTokenizer.from_pretrained(REPO_ID)
model = AutoModelForMaskedLM.from_pretrained(REPO_ID)
model.eval()

# Gene-expression masked prediction (ENSEMBL gene IDs in rank order)
genes = [
    "ENSG00000000003",
    "ENSG00000000005",
    tokenizer.mask_token,   # mask one position to predict
    "ENSG00000001167",
    "ENSG00000002586",
]
ids = tokenizer.convert_tokens_to_ids(genes)
input_ids = torch.tensor([ids])
mask_index = (input_ids == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]

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

predicted_id = outputs.logits[0, mask_index].argmax(dim=-1)
predicted_gene = tokenizer.convert_ids_to_tokens(predicted_id.tolist())[0]
print(f"Predicted gene at mask: {predicted_gene}")
# => Predicted gene at mask: ENSG00000164972