weijiang99
/

clinvarbert

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 ---
 library_name: transformers
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 ---
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 ---
 library_name: transformers
+tags:
+- biomedical
+- clinical
+- variant-classification
+- genetics
+- bert
+- fine-tuned
+language:
+- en
+license: apache-2.0
+base_model: dmis-lab/biobert-large-cased-v1.1
+datasets:
+- clinvar
+pipeline_tag: text-classification
 ---
+# ClinVarBERT-Large
+A BERT model fine-tuned for clinical variant interpretation and classification tasks, based on BioBERT-Large.
 ## Model Details
 ### Model Description
+ClinVarBERT-Large is a domain-specific language model fine-tuned from BioBERT-Large for understanding and classifying genetic variant descriptions and clinical interpretations. The model has been trained to understand the nuanced language used in clinical genetics, particularly for variant pathogenicity assessment and clinical significance classification.
+- **Developed by:** [Your Name/Organization]
+- **Model type:** BERT-based transformer for sequence classification
+- **Language(s):** English (biomedical/clinical domain)
+- **License:** Apache 2.0
+- **Finetuned from model:** dmis-lab/biobert-large-cased-v1.1
+### Model Sources
+- **Repository:** [Your GitHub Repository]
+- **Base Model:** [BioBERT-Large](https://huggingface.co/dmis-lab/biobert-large-cased-v1.1)
+- **Training Data:** ClinVar database submissions text
 ## Uses
 ### Direct Use
+This model is designed for:
+- **Variant pathogenicity classification:** Classifying genetic variants as P/LP, B/LB, or VUS
+- **Clinical interpretation analysis:** Understanding and categorizing clinical variant descriptions
+- **Biomedical text classification:** General classification tasks in the clinical genetics domain
 ## How to Get Started with the Model
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("your-username/clinvarbert-large")
+model = AutoModelForSequenceClassification.from_pretrained("your-username/clinvarbert-large")
+# Example usage
+text = "This missense variant in exon 5 of the BRCA1 gene has been observed in multiple families with breast cancer."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+# Get predicted class
+predicted_class = torch.argmax(predictions, dim=-1)