Update README.md

README.md

@@ -7,11 +7,11 @@ base_model:
 ---
 # PatentBERT - PyTorch
 
-BERT model specialized for patent classification using the **real CPC (Cooperative Patent Classification) system**. (PyTorch version of the original [PatentBert](https://github.com/jiehsheng/PatentBERT/) model.)
+BERT model specialized for patent classification using the **CPC (Cooperative Patent Classification) system**. (PyTorch version of the original [PatentBert](https://github.com/jiehsheng/PatentBERT/) model.)
 
 ## 📊 Specifications
 
-- **Output classes**: 656 (real CPC labels)
+- **Output classes**: 656 (CPC subclass labels)
 - **Classification system**: CPC (Cooperative Patent Classification)
 - **Architecture**: BERT-base (768 hidden, 12 layers, 12 attention heads)
 - **Vocabulary**: 30,522 tokens
@@ -32,7 +32,7 @@ The model predicts classes according to the authentic CPC system used in PatentB
 - **H (51 classes)**: Electricity - Electronics, Power generation, Communication
 - **Y (9 classes)**: General Tagging of New Technological Developments
 
-### Example Real Classes
+### Example of CPC Subclasses
 
 - `A01B`: SOIL WORKING IN AGRICULTURE OR FORESTRY
 - `B25J`: MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
@@ -63,7 +63,7 @@ with torch.no_grad():
 predicted_class_id = predictions.argmax().item()
 confidence = predictions.max().item()
 
-# Use model labels (real CPC codes)
+# Use model labels (CPC codes)
 predicted_label = model.config.id2label[str(predicted_class_id)]
 
 print(f"Predicted CPC class: {predicted_label} (ID: {predicted_class_id})")
@@ -73,21 +73,20 @@ print(f"Confidence: {confidence:.2%}")
 ## 📁 Included Files
 
 - `model.safetensors`: Model weights (420 MB)
-- `config.json`: Configuration with integrated real CPC labels
+- `config.json`: Configuration with integrated CPC labels
 - `vocab.txt`: Tokenizer vocabulary
 - `tokenizer_config.json`: Tokenizer configuration
-- `labels.json`: Complete real CPC label mapping (656 authentic labels)
+- `labels.json`: Complete CPC label mapping (656 authentic labels)
 - `README.md`: This documentation
 
 ## 🔬 Performance
 
-This model was trained on a large patent corpus to automatically classify documents according to the real CPC system, using the exact same 656 CPC codes from the original PatentBERT training data.
+This model was trained on a large patent corpus to automatically classify documents according to the CPC system, using the exact same 656 CPC codes from the original PatentBERT training data.
 
 ## 📖 References
 
 - [Cooperative Patent Classification (CPC)](https://www.cooperativepatentclassification.org/)
 - [Original PatentBERT Paper](https://arxiv.org/abs/2103.02557)
-- [Hugging Face Transformers](https://huggingface.co/transformers/)
 
 ## 📝 Citation