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--- |
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license: mit |
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language: |
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- en |
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base_model: |
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- microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext |
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tags: |
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- biomedical |
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- relation-extraction |
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- text-classification |
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--- |
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# cell-cell-BERT |
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**Configuration: R-CLS-base** |
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## Model Description |
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This is a specific configuration of the cell-cell-BERT model for extracting cell-cell interactions from biomedical text. It determines whether a sentence describes a direct biological relationship between two target cell types. |
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For full details, see our paper: **"Defining and Evaluating Cell–Cell Relation Extraction from Biomedical Literature under Realistic Annotation Constraints"** (bioRxiv, 2025). |
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* **Repository:** [https://github.com/mizuno-group/cell-cell-bert](https://github.com/mizuno-group/cell-cell-bert) |
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* **Paper:** [https://doi.org/10.64898/2025.12.01.691726](https://doi.org/10.64898/2025.12.01.691726) |
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## Model Configuration |
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This model corresponds to the following experimental setting in the paper: |
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* **Entity Indication:** [Replacement (e.g., `[CELL0]`) / Boundary Marking (e.g., `<E0>...`)] |
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* **Architecture:** [Entity-aware (R-BERT style) / CLS-only] |
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* **Pre-training:** [Continued Pre-training (CPT) / Base (Fine-tuning only)] |
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*Note: Please ensure your input data preprocessing matches the **Entity Indication** method specified above.* |
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## How to Get Started |
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**Preprocessing Requirement:** |
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Depending on the configuration above, you must insert specific special tokens into your input text before feeding it to the model. |
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* **For Replacement models:** Replace cell names with `[CELL0]` and `[CELL1]`. |
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* **For Boundary models:** Wrap cell names with `<E0>...</E0>` and `<E1>...</E1>`. |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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# 1. Load the model |
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model_name = "mizuno-group/ccbert-[INSERT-CONFIG-NAME]" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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# 2. Prepare Input |
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# CHANGE THIS LINE based on the Entity Indication method of this model: |
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# text = "The [CELL0] activate [CELL1]." # If Replacement |
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text = "The <E0> Macrophages </E0> activate <E1> T cells </E1>." # If Boundary Marking |
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# 3. Inference |
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inputs = tokenizer(text, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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predicted_class_id = logits.argmax().item() |
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# 0 = No Relation, 1 = Relation Exists |
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print(f"Predicted Class: {predicted_class_id}") |
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``` |
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## Citation |
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```bibtex |
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@article{Yoshikawa2025CCBERT, |
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title = {Defining and Evaluating Cell–Cell Relation Extraction from Biomedical Literature under Realistic Annotation Constraints}, |
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author = {Yoshikawa Mei and Mizuno Tadahaya and Ohto Yohei and Fujimoto Hiromi and Kusuhara Hiroyuki}, |
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journal = {bioRxiv}, |
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year = {2025}, |
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doi = {10.64898/2025.12.01.691726}, |
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url = {[https://doi.org/10.64898/2025.12.01.691726](https://doi.org/10.64898/2025.12.01.691726)} |
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} |
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``` |
