Initial upload: Best performing cardiology embedding model (separation: 0.510)

README.md

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+---
+library_name: peft
+base_model: michiyasunaga/BioLinkBERT-large
+tags:
+- medical
+- cardiology
+- embeddings
+- domain-adaptation
+- lora
+- sentence-transformers
+- sentence-similarity
+language:
+- en
+license: apache-2.0
+---
+
+# CardioEmbed-BioLinkBERT
+
+**Domain-specialized cardiology text embeddings using LoRA-adapted BioLinkBERT-large**
+
+This is the **best performing model** from our comparative study of 10 embedding architectures for clinical cardiology.
+
+## Performance
+
+| Metric | Score |
+|--------|-------|
+| Separation Score | **0.510** |
+| Similar Pair Avg | 0.811 |
+| Different Pair Avg | 0.301 |
+| Throughput | 143.5 emb/sec |
+| Memory | 1.51 GB |
+
+## Usage
+
+```python
+from transformers import AutoModel, AutoTokenizer
+from peft import PeftModel
+
+# Load base model
+base_model = AutoModel.from_pretrained("michiyasunaga/BioLinkBERT-large")
+tokenizer = AutoTokenizer.from_pretrained("michiyasunaga/BioLinkBERT-large")
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, "richardyoung/CardioEmbed-BioLinkBERT")
+
+# Generate embeddings
+text = "Atrial fibrillation with rapid ventricular response"
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+outputs = model(**inputs)
+embeddings = outputs.last_hidden_state.mean(dim=1)
+```
+
+## Training
+
+- **Training Data**: 106,535 cardiology text pairs from medical textbooks
+- **Method**: LoRA fine-tuning (r=16, alpha=32)
+- **Loss**: Multiple Negatives Ranking Loss (InfoNCE)
+
+## Citation
+
+```bibtex
+@article{young2024comparative,
+  title={Comparative Analysis of LoRA-Adapted Embedding Models for Clinical Cardiology Text Representation},
+  author={Young, Richard J and Matthews, Alice M},
+  journal={arXiv preprint},
+  year={2024}
+}
+```
+
+## Related Models
+
+This is part of the CardioEmbed model family. See [richardyoung/CardioEmbed](https://huggingface.co/richardyoung/CardioEmbed) for more models.

adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "michiyasunaga/BioLinkBERT-large",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.0",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "value",
+    "key",
+    "query"
+  ],
+  "target_parameters": null,
+  "task_type": "FEATURE_EXTRACTION",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+size 9456904

special_tokens_map.json

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+{
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
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+      "special": true
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+  "do_lower_case": true,
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+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_config.json

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+{
+  "name": "BioLinkBERT-large",
+  "model_id": "michiyasunaga/BioLinkBERT-large",
+  "batch_size": 64,
+  "gradient_accumulation_steps": 1,
+  "learning_rate": 2e-05,
+  "epochs": 3,
+  "max_seq_length": 512,
+  "lora_r": 16,
+  "lora_alpha": 32,
+  "use_8bit": false,
+  "target_modules": [
+    "query",
+    "key",
+    "value"
+  ],
+  "output_dir": "biolinkbert_cardiology"
+}