MM-YY-WW
/

GoBERT

@@ -12,190 +12,63 @@ tags:
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 This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
 ## Model Details
+GoBERT: Gene Ontology Graph Informed BERT for Universal Gene Function Prediction.
 ### Model Description
+First encoder to capture relations among GO functions. Could generate GO function embedding for various biological applications that related to gene or gene products.
+### Model Sources
 <!-- Provide the basic links for the model. -->
+- **Repository:** https://github.com/MM-YY-WW/GoBERT
+- **Paper [optional]:** GoBERT: Gene Ontology Graph Informed BERT for Universal Gene Function Prediction.
+- **Demo [optional]:** https://gobert.nasy.moe/
 ## How to Get Started with the Model
 Use the code below to get started with the model.
+```python
+from transformers import AutoTokenizer, BertForPreTraining
+import torch
+repo_name = "MM-YY-WW/GoBERT"
+tokenizer = AutoTokenizer.from_pretrained(repo_name, use_fast=False, trust_remote_code=True)
+model = BertForPreTraining.from_pretrained(repo_name)
+# Obtain function-level GoBERT Embedding:
+input_sequences = 'GO:0005739 GO:0005783 GO:0005829 GO:0006914 GO:0006915 GO:0006979 GO:0031966 GO:0051560'
+tokenized_input = tokenizer(input_sequences)
+input_tensor = torch.tensor(tokenized_input['input_ids']).unsqueeze(0)
+attention_mask = torch.tensor(tokenized_input['attention_mask']).unsqueeze(0)
+model.eval()
+with torch.no_grad():
+    outputs = model(input_ids=input_tensor, attention_mask=attention_mask, output_hidden_states=True)
+    embedding = outputs.hidden_states[-1].squeeze(0).cpu().numpy()
+```
+## Citation
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 **BibTeX:**
+```bibtex
+@inproceedings{miao2025gobert,
+  title={GoBERT: Gene Ontology Graph Informed BERT for Universal Gene Function Prediction},
+  author={Miao, Yuwei and Guo, Yuzhi and Ma, Hehuan and Yan, Jingquan and Jiang, Feng and Liao, Rui and Huang, Junzhou},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={39},
+  number={1},
+  pages={622--630},
+  year={2025},
+  doi={10.1609/aaai.v39i1.32043}
+}
+```