Update model card for Kannada

README.md

@@ -1,69 +1,74 @@
----
-pipeline_tag: sentence-similarity
-language: kan
-license: mit
-tags:
-  - trimmed
-library_name: sentence-transformers
-base_model: BAAI/bge-m3
-base_model_relation: quantized
-datasets:
-  - Lumberjackk/fineweb-2-trimming
----
-
-# bge-m3-kan-16384
-
-This model is a **42.14% smaller** version of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) optimized for Kannada 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 Kannada were removed from the vocabulary.
-
-## Model Statistics
-
-| Metric | Original | Trimmed | Reduction |
-|--------|----------|---------|-----------|
-| **Vocabulary size** | 250,002 tokens | 16,384 tokens | **93.45%** |
-| **Model size** | 567,754,752 params | 328,529,920 params | **42.14%** |
-
-
-## 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)
-
-![image](https://cdn-uploads.huggingface.co/production/uploads/613b0a62a14099d5afed7830/7UlOxvIMVUm--Wexm9yyz.png)
-
-## Usage
-
-```python
-from sentence_transformers import SentenceTransformer
-# Download from the 🤗 Hub
-model = SentenceTransformer("lbourdois/bge-m3-kan-16384")
-# Run inference with queries and documents
-query = "My query"
-documents = [
-    "Chunk 1",
-    "Chunk 2",
-    "Chunk 3",
-]
-query_embeddings = model.encode_query(query)
-document_embeddings = model.encode_document(documents)
-print(query_embeddings.shape, document_embeddings.shape)
-# Compute similarities to determine a ranking
-similarities = model.similarity(query_embeddings, document_embeddings)
-print(similarities)
-```
-
-## Citation
-
-#### BGE M3-Embedding
-
-```bibtex
-@misc{bge-m3,
-      title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, 
-      author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},
-      year={2024},
-      eprint={2402.03216},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-```
+---
+pipeline_tag: sentence-similarity
+language: kan
+license: mit
+tags:
+  - trimmed
+library_name: sentence-transformers
+base_model: BAAI/bge-m3
+base_model_relation: quantized
+datasets:
+  - lbourdois/fineweb-2-trimming
+---
+
+# bge-m3-kan-16384
+This model is a **42.14% smaller** version of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) optimized for Kannada 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** | 250,002 tokens | 16,384 tokens | **93.45%** |
+| **Model size** | 567,754,752 params | 328,529,920 params | **42.14%** |
+
+![image](https://raw.githubusercontent.com/lbourdois/blog/refs/heads/master/assets/images/Trimming/bge-m3-16384.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 sentence_transformers import SentenceTransformer
+# Download from the 🤗 Hub
+model = SentenceTransformer("alphaedge-ai/bge-m3-kan-16384")
+# Run inference with queries and documents
+query = "My query in Kannada"
+documents = [
+    "Chunk in Kannada",
+    "Chunk in Kannada",
+    "Chunk in Kannada",
+]
+query_embeddings = model.encode_query(query)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings.shape)
+# Compute similarities to determine a ranking
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+```
+
+## Citations
+
+#### BGE-M3
+```
+@misc{bge-m3,
+      title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, 
+      author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},
+      year={2024},
+      eprint={2402.03216},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+#### 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}, 
+}
+```