MolCrawl/molecule_nat_lang
Collection
9 items • Updated
GPT-2 medium (345M parameters) foundation model pre-trained on molecule-related natural language text using a standard GPT-2 BPE tokenizer (vocab_size=50257).
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained("kojima-lab/molcrawl-molecule-nat-lang-gpt2-medium")
tokenizer = AutoTokenizer.from_pretrained("kojima-lab/molcrawl-molecule-nat-lang-gpt2-medium")
# Generate molecule-related text
prompt = "The compound with SMILES CC(=O)Oc1ccccc1C(=O)O represents aspirin, which"
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
output_ids = model.generate(
**inputs,
max_new_tokens=100,
do_sample=True,
temperature=0.8,
eos_token_id=None, # HF config.json has legacy eos_token_id=0; disable early stop
pad_token_id=0,
)
print(tokenizer.decode(output_ids[0], skip_special_tokens=True))
Training pipeline, configuration files, and data preparation scripts are available in the MolCrawl GitHub repository: https://github.com/mmai-framework-lab/MolCrawl
This model is released under the APACHE-2.0 license.
If you use this model, please cite:
@misc{molcrawl_molecule_nat_lang_gpt2_medium,
title={molcrawl-molecule-nat-lang-gpt2-medium},
author={{RIKEN}},
year={2026},
publisher={{Hugging Face}},
url={{https://huggingface.co/kojima-lab/molcrawl-molecule-nat-lang-gpt2-medium}}
}