Update model card for Persian

README.md

@@ -1,60 +1,74 @@
----
-pipeline_tag: sentence-similarity
-language: fas
-license: mit
-tags:
-  - trimmed
-  - sentence-transformers
-  - feature-extraction
-  - bidirectional_pplx_qwen3
-library_name: sentence-transformers
-base_model: perplexity-ai/pplx-embed-v1-0.6b
-base_model_relation: quantized
-datasets:
-  - Lumberjackk/fineweb-2-trimming
----
-
-# pplx-embed-v1-fas-32768
-
-This model is a **20.47% smaller** version of [perplexity-ai/pplx-embed-v1-0.6b](https://huggingface.co/perplexity-ai/pplx-embed-v1-0.6b) optimized for Persian 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 **32,768 tokens** and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in Persian were removed from the vocabulary.
-
-## Model Statistics
-
-| Metric | Original | Trimmed | Reduction |
-|--------|----------|---------|-----------|
-| **Vocabulary size** | 151,643 tokens | 32,768 tokens | **78.39%** |
-| **Model size** | 596,049,920 params | 474,021,888 params | **20.47%** |
-
-## 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
-
-model = SentenceTransformer("alphaedge-ai/pplx-embed-v1-fas-32768", trust_remote_code=True)
-texts = [
-    "Chunk 1",
-    "Chunk 2",
-    "Chunk 3",
-]
-embeddings = model.encode(texts)
-print(embeddings.shape)
-```
-
-## Citation
-
-#### pplx-embed
-
-```bibtex
-@article{pplxembed2025,
-  title={pplx-embed: State-of-the-Art Embedding Models for Web-Scale Retrieval},
-  author={Perplexity AI},
-  year={2025}
-}
-```
+---
+pipeline_tag: sentence-similarity
+language: fas
+license: mit
+tags:
+  - trimmed
+library_name: sentence-transformers
+base_model: perplexity-ai/pplx-embed-v1-0.6b
+base_model_relation: quantized
+datasets:
+  - lbourdois/fineweb-2-trimming
+---
+
+# pplx-embed-v1-fas-32768
+This model is a **20.47% smaller** version of [perplexity-ai/pplx-embed-v1-0.6b](https://huggingface.co/perplexity-ai/pplx-embed-v1-0.6b) optimized for **Persian** 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 32,768 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** | 151,936 tokens | 32,768 tokens | **78.43%** |
+| **Model size** | 596,049,920 params | 474,021,888 params | **20.47%** |
+
+![image](https://raw.githubusercontent.com/lbourdois/blog/refs/heads/master/assets/images/Trimming/pplx-embed-v1-32768.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/pplx-embed-v1-fas-32768")
+# Run inference with queries and documents
+query = "My query in Persian"
+documents = [
+    "Chunk in Persian",
+    "Chunk in Persian",
+    "Chunk in Persian",
+]
+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
+
+#### pplx-embed-v1
+```
+@misc{eslami2026diffusionpretraineddensecontextualembeddings,
+      title={Diffusion-Pretrained Dense and Contextual Embeddings}, 
+      author={Sedigheh Eslami and Maksim Gaiduk and Markus Krimmel and Louis Milliken and Bo Wang and Denis Bykov},
+      year={2026},
+      eprint={2602.11151},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2602.11151}, 
+}
+```
+
+#### 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}, 
+}
+```