Update README.md

README.md

@@ -63,8 +63,8 @@ Note that this model is primarily aimed at being fine-tuned on tasks that use th
 from transformers import AutoModelForMaskedLM, AutoTokenizer
 import torch
 
-model = AutoModelForMaskedLM.from_pretrained("frogd51/coberta-base")
-tokenizer = AutoTokenizer.from_pretrained("frogd51/coberta-base")
+model = AutoModelForMaskedLM.from_pretrained("appleroll/coberta-base")
+tokenizer = AutoTokenizer.from_pretrained("appleroll/coberta-base")
 
 text = "The key to effective communication is to [MASK] clearly and listen actively."
 inputs = tokenizer(text, return_tensors="pt")
@@ -80,4 +80,44 @@ top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()
 
 for token in top_5_tokens:
     print(f"{tokenizer.decode([token])}")
-```
+```
+
+### Citation
+
+If you use CoBERTa in your research, please cite:
+```bibtex
+@misc{coberta,
+    title     = {CoBERTa: Training Domain-Expert Language Models on Consumer Hardware},
+    url       = {https://huggingface.co/appleroll/coberta-base},
+    author    = {Zhang, Ethan},
+    year      = {2025}
+}
+```
+
+### Additional Information
+Authors:
+- Ethan Zhang (Independent Researcher)
+
+### Contact
+
+For questions, feedback, or collaboration: ethanzhangyixuan@gmail.com
+
+### Acknowledgements
+
+- Cosmopedia dataset creators (HuggingfaceTB)
+
+- Apple MLX development team
+
+- Hugging Face for model hosting
+
+- Open-source ML community
+
+### Version
+
+Current: v0.5 (Experimental Release)
+
+Previous: None
+
+### License
+
+This model is licensed under the MIT License. See the LICENSE file for details.