Update model card for Chinese

README.md

@@ -1,52 +1,62 @@
----
-pipeline_tag: fill-mask
-language: zho
-license: mit
-tags:
-  - trimmed
-library_name: transformers
-base_model: jhu-clsp/mmBERT-small
-base_model_relation: quantized
-datasets:
-  - Lumberjackk/fineweb-2-trimming
----
-
-# mmBERT-small-zho-16384
-This model is a 65.49% smaller version of [jhu-clsp/mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small) optimized for Chinese language via vocabulary size reduction using the [trimming](https://huggingface.co/blog/introduction-to-trimming) method.  
-This trimmed model should perform similarly to the original model with only 16,384 tokens and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in the selected languages were removed from the vocabulary.
-
-## Model Statistics
-| Metric | Original | Trimmed | Reduction |
-|--------|----------|---------|-----------|
-| **Vocabulary size** | 256,000 tokens | 16,384 tokens | **93.60%** |
-| **Model size** | 140,493,696 params | 48,481,152 params | **65.49%** |
-
-![image](https://cdn-uploads.huggingface.co/production/uploads/613b0a62a14099d5afed7830/9ZxDwbIrunwGgGULtPJ8K.png)
-
-## Mining Dataset Statistics
-- **Number of texts used for mining**: 200,000 texts  
-- **Dataset**: [Lumberjackk/fineweb-2-trimming](https://huggingface.co/datasets/Lumberjackk/fineweb-2-trimming)
-
-## Usage
-```python
-from transformers import AutoModel, AutoTokenizer
-
-model_name = "Lumberjackk/mmBERT-small-zho-16384"
-model = AutoModel.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-```
-
-## Citation
-
-#### mmBERT
-```
-@misc{marone2025mmbertmodernmultilingualencoder,
-      title={mmBERT: A Modern Multilingual Encoder with Annealed Language Learning}, 
-      author={Marc Marone and Orion Weller and William Fleshman and Eugene Yang and Dawn Lawrie and Benjamin Van Durme},
-      year={2025},
-      eprint={2509.06888},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2509.06888}, 
-}
-```
+---
+pipeline_tag: fill-mask
+language: zho
+license: mit
+tags:
+  - trimmed
+library_name: transformers
+base_model: jhu-clsp/mmBERT-small
+base_model_relation: quantized
+datasets:
+  - lbourdois/fineweb-2-trimming
+---
+
+# mmBERT-small-zho-16384
+This model is a **65.49% smaller** version of [jhu-clsp/mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small) optimized for **Chinese** language via vocabulary size reduction using the [trimming](https://huggingface.co/blog/lbourdois/introduction-to-trimming) method.  
+This trimmed model should perform similarly to the original model with only 16,384 tokens and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in the selected languages were removed from the vocabulary.
+
+## Model Statistics
+| Metric | Original | Trimmed | Reduction |
+|--------|----------|---------|-----------|
+| **Vocabulary size** | 256,000 tokens | 16,384 tokens | **93.60%** |
+| **Model size** | 140,493,696 params | 48,481,152 params | **65.49%** |
+
+![image](https://raw.githubusercontent.com/lbourdois/blog/refs/heads/master/assets/images/Trimming/mmBERT-small-16384.png)
+
+## Mining Dataset Statistics
+- **Number of texts used for mining**: 200,000 texts  
+- **Dataset**: [lbourdois/fineweb-2-trimming](https://huggingface.co/datasets/lbourdois/fineweb-2-trimming)
+
+## Usage
+```python
+from transformers import AutoModel, AutoTokenizer
+
+model_name = "alphaedge-ai/mmBERT-small-zho-16384"
+model = AutoModel.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+```
+
+## Citations
+
+#### mmBERT
+```
+@misc{marone2025mmbertmodernmultilingualencoder,
+      title={mmBERT: A Modern Multilingual Encoder with Annealed Language Learning}, 
+      author={Marc Marone and Orion Weller and William Fleshman and Eugene Yang and Dawn Lawrie and Benjamin Van Durme},
+      year={2025},
+      eprint={2509.06888},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2509.06888}, 
+}
+```
+
+#### Trimming blog post
+```
+@misc{hf_blogpost_trimming,
+      title={Introduction to Trimming}, 
+      author={Loïck BOURDOIS and Tom AARSEN and Bram VANROY and Christopher AKIKI and Woojun JUNG and Manuel ROMERO and Prithiv SAKTHI},
+      year={2026},
+      url={https://huggingface.co/blog/lbourdois/introduction-to-trimming}, 
+}
+```