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README.md

@@ -294,37 +294,17 @@ John Smith works at Google in New York and uses Python programming language.
 - ✅ **GPU acceleration** provides significant speed improvements
 - ✅ **Apple Silicon** users get Metal acceleration automatically
 
-## 📚 Limitations & Biases
-
-### Current Limitations
-
-| Limitation | Description | Impact |
-|------------|-------------|--------|
-| **Variable Output Quality** | Sometimes produces garbled or incomplete responses | May miss entities in certain contexts |
-| **No Entity Type Labels** | Outputs entity names but not their types | Requires post-processing for type classification |
-| **Context Window** | Limited to 2,048 token context window | Cannot process very long documents |
-| **Language Scope** | Primarily trained on English text | Limited performance on other languages |
-| **Inconsistent Extraction** | Performance varies by input complexity | May miss entities in complex sentences |
-
-### Potential Biases
-
-| Bias Type | Description | Mitigation |
-|-----------|-------------|------------|
-| **Output Format Inconsistency** | Sometimes outputs structured lists, sometimes garbled text | Improved prompt engineering and training |
-| **Entity Recognition Patterns** | May favor certain entity patterns over others | Diverse training data and evaluation |
-| **Domain Specificity** | Performance varies across different text types | Multi-domain training and fine-tuning |
-
 ## 📜 Citation
 
-If you use NER-Small in your research, please cite:
+If you use Named Entity Recognition - Standard in your research, please cite:
 
 ```bibtex
-@misc{ner-small-2025,
-  title={NER-Small: A Compact Named Entity Recognition Model},
+@misc{ner-standard-2025,
+  title={Named Entity Recognition - Standard: High-Performance Named Entity Recognition Model},
   author={Minibase AI Team},
   year={2025},
   publisher={Hugging Face},
-  url={https://huggingface.co/Minibase/NER-Small}
+  url={https://huggingface.co/Minibase/NER-Standard}
 }
 ```