Update model card for Swedish

README.md

@@ -1,66 +1,71 @@
----
-pipeline_tag: sentence-similarity
-language: swe
-license: apache-2.0
-tags:
-  - trimmed
-library_name: sentence-transformers
-base_model: Qwen/Qwen3-Embedding-0.6B
-base_model_relation: quantized
-datasets:
-  - Lumberjackk/fineweb-2-trimming
----
-
-# Qwen3-Embedding-swe-16384
-
-This model is a **23.25% smaller** version of [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) optimized for Swedish 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 Swedish were removed from the vocabulary.
-
-## Model Statistics
-
-| Metric | Original | Trimmed | Reduction |
-|--------|----------|---------|-----------|
-| **Vocabulary size** | 151,643 tokens | 16,384 tokens | **89.20%** |
-| **Model size** | 595,776,512 params | 457,244,672 params | **23.25%** |
-
-![image](https://cdn-uploads.huggingface.co/production/uploads/613b0a62a14099d5afed7830/xyTFk3gLOL-4MQZSfKCgt.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 sentence_transformers import SentenceTransformer
-# Download from the 🤗 Hub
-model = SentenceTransformer("lbourdois/Qwen3-Embedding-swe-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
-
-#### Qwen3 Embedding 
-
-```bibtex
-@article{qwen3embedding,
-  title={Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models},
-  author={Zhang, Yanzhao and Li, Mingxin and Long, Dingkun and Zhang, Xin and Lin, Huan and Yang, Baosong and Xie, Pengjun and Yang, An and Liu, Dayiheng and Lin, Junyang and Huang, Fei and Zhou, Jingren},
-  journal={arXiv preprint arXiv:2506.05176},
-  year={2025}
-}
-```
+---
+pipeline_tag: sentence-similarity
+language: swe
+license: apache-2.0
+tags:
+  - trimmed
+library_name: sentence-transformers
+base_model: Qwen/Qwen3-Embedding-0.6B
+base_model_relation: quantized
+datasets:
+  - lbourdois/fineweb-2-trimming
+---
+
+# Qwen3-Embedding-swe-16384
+This model is a **23.25% smaller** version of [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) optimized for **Swedish** 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** | 151,669 tokens | 16,384 tokens | **89.20%** |
+| **Model size** | 595,776,512 params | 457,244,672 params | **23.25%** |
+
+![image](https://raw.githubusercontent.com/lbourdois/blog/refs/heads/master/assets/images/Trimming/Qwen3-Embedding-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/Qwen3-Embedding-swe-16384")
+# Run inference with queries and documents
+query = "My query in Swedish"
+documents = [
+    "Chunk in Swedish",
+    "Chunk in Swedish",
+    "Chunk in Swedish",
+]
+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
+
+#### Qwen3 Embedding 
+```
+@article{qwen3embedding,
+  title={Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models},
+  author={Zhang, Yanzhao and Li, Mingxin and Long, Dingkun and Zhang, Xin and Lin, Huan and Yang, Baosong and Xie, Pengjun and Yang, An and Liu, Dayiheng and Lin, Junyang and Huang, Fei and Zhou, Jingren},
+  journal={arXiv preprint arXiv:2506.05176},
+  year={2025}
+}
+```
+
+#### 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}, 
+}
+```