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kojima-lab/
molcrawl-protein-sequence-proteingym-bert-medium

Name: molcrawl-protein-sequence-proteingym-bert-medium
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molcrawl-protein-sequence-proteingym-bert-medium

Model Description

GPT-2 medium (345M parameters) fine-tuned on ProteinGym protein sequence data, starting from the molcrawl-protein-sequence-gpt2-medium pre-trained model.

Datasets

ProteinGym: https://proteingym.org/ (Fine-tuning dataset)
Model Type: bert
Data Type: Protein
Training Date: 2026-05-15

Usage

python

from transformers import AutoModelForMaskedLM, AutoTokenizer
import torch

model = AutoModelForMaskedLM.from_pretrained("kojima-lab/molcrawl-protein-sequence-proteingym-bert-medium")
tokenizer = AutoTokenizer.from_pretrained("kojima-lab/molcrawl-protein-sequence-proteingym-bert-medium")

# Predict masked amino acid
# 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 = "MKTAYIAK{MASK}RQISFVKSHFSRQ".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:

bibtex

@misc{molcrawl_protein_sequence_proteingym_bert_medium,
  title={molcrawl-protein-sequence-proteingym-bert-medium},
  author={{RIKEN}},
  year={2026},
  publisher={{Hugging Face}},
  url={{https://huggingface.co/kojima-lab/molcrawl-protein-sequence-proteingym-bert-medium}}
}

Example Output

End-to-end inference test (downloaded the model from this repo on CPU).

python

import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM

REPO_ID = "kojima-lab/molcrawl-protein-sequence-proteingym-bert-medium"
tokenizer = AutoTokenizer.from_pretrained(REPO_ID)
model = AutoModelForMaskedLM.from_pretrained(REPO_ID)
model.eval()

# A protein sequence; mask one residue to predict
sequence = "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSR<mask>VIVQDIAYLRSLGYNIVATPRGYVLAGG"
inputs = tokenizer(sequence, return_tensors="pt")
mask_index = (inputs["input_ids"][0] == tokenizer.mask_token_id).nonzero(as_tuple=True)[0]

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

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