Upload BertForSequenceClassification

README.md

@@ -1,36 +1,36 @@
 ---
 language:
-  - en
+- en
 license: apache-2.0
 base_model: emilyalsentzer/Bio_ClinicalBERT
 tags:
-  - medical
-  - clinical
-  - ssi
-  - classification
-  - surveillance
+- medical
+- clinical
+- ssi
+- classification
+- surveillance
 metrics:
-  - accuracy
-  - f1
-  - precision
-  - recall
+- accuracy
+- f1
+- precision
+- recall
 model-index:
-  - name: clinicalSSIBERT
-    results:
-      - task:
-          type: text-classification
-          name: SSI Detection
-        dataset:
-          name: Synthetic UK NHS Clinical Notes
-          type: synthetic
-          split: test
-        metrics:
-          - name: Accuracy
-            type: accuracy
-            value: 1.0
-          - name: F1
-            type: f1
-            value: 1.0
+- name: clinicalSSIBERT
+  results:
+  - task:
+      type: text-classification
+      name: SSI Detection
+    dataset:
+      name: Synthetic UK NHS Clinical Notes
+      type: synthetic
+      split: test
+    metrics:
+    - type: accuracy
+      value: 1.0
+      name: Accuracy
+    - type: f1
+      value: 1.0
+      name: F1
 ---
 
 # Model Card for Ch3DS/clinicalSSIBERT
@@ -99,25 +99,25 @@ print(f"Prediction: {labels[predicted_class_id]}")
 
 ### Training Data
 
-The model was trained on **5 million synthetic clinical notes** generated to mimic UK NHS postoperative records. The data covers:
+The model was trained on **1 million synthetic clinical notes** generated to mimic UK NHS postoperative records. The data covers:
 
 - **Procedures**: Total Hip/Knee Replacement, C-Section, Cholecystectomy, Hernia Repair, etc.
 - **Terminology**: UK-specific staff titles (Reg, SHO, FY1), antibiotics (Co-amoxiclav, Teicoplanin), and wound descriptions.
-- **Balance**: Approximately 5% infection rate.
+- **Balance**: Approximately 15% infection rate.
 
 ### Training Procedure
 
 #### Training Hyperparameters
 
 - **Epochs**: 3
-- **Batch Size**: 64 (per device) with Gradient Accumulation of 4
+- **Batch Size**: 8 (per device) with Gradient Accumulation of 4 (Effective Batch Size: 32)
 - **Learning Rate**: 2e-5
 - **Precision**: Mixed Precision (FP16)
 - **Optimizer**: AdamW
 
 #### Hardware
 
-- **GPU**: NVIDIA GeForce RTX 5070 Ti
+- **GPU**: NVIDIA GeForce RTX 4050 Laptop GPU (6GB VRAM)
 
 ## Evaluation
 
@@ -138,7 +138,7 @@ _Note: The perfect scores reflect the synthetic nature of the test data, which f
 
 ## Environmental Impact
 
-- **Hardware Type**: NVIDIA GeForce RTX 5070 Ti
+- **Hardware Type**: NVIDIA GeForce RTX 4050 Laptop GPU
 - **Hours used**: ~2 hours
 - **Carbon Emitted**: Negligible (local training)
 

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d614ab648ce7b43caf822add1477b67418bcf85c4c90f7796d1ca0847fbb0529
+oid sha256:660a5c1b7408477c72117546cc536b42500818ad281d91e6c6c89cf75edd7b1b
 size 433270768