Add Key Highlights, Model List and Experimental Results sections to all Octen model READMEs

README.md

@@ -20,6 +20,76 @@ base_model: Qwen/Qwen3-Embedding-0.6B
 
 Octen-Embedding-0.6B is a text embedding model designed for semantic search and retrieval tasks. This model is fine-tuned from [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) and supports multiple languages, providing high-quality embeddings for various applications.
 
+## Key Highlights
+
+### 🥇 RTEB Leaderboard Champion (as of January 12, 2026)
+- **Octen-Embedding-8B ranks #1 on the [RTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard)** with Mean (Task) score of **0.8045**
+- Excellent performance on both Public (0.7953) and Private (0.8157) datasets
+- Demonstrates true generalization capability without overfitting to public benchmarks
+
+### Industry-Oriented Vertical Domain Expertise
+- **Legal**: Legal document retrieval
+- **Finance**: Financial reports, Q&A, and personal finance content
+- **Healthcare**: Medical Q&A, clinical dialogues, and health consultations
+- **Code**: Programming problems, code search, and SQL queries
+
+### Ultra-Long Context Support
+- Supports up to **32,768 tokens** context length
+- Suitable for processing long documents in legal, healthcare, and other domains
+- High-dimensional embedding space for rich semantic representation
+
+### Multilingual Capability
+- Supports **100+ languages**
+- Includes various programming languages
+- Strong multilingual, cross-lingual, and code retrieval capabilities
+
+---
+
+## Open Source Model List
+
+| Model Type | Model | Size | Max Tokens | Embedding Dimensions | HuggingFace Link |
+|------------|-------|------|------------|---------------------|------------------|
+| Text Embedding | [Octen-Embedding-0.6B](https://huggingface.co/Octen/Octen-Embedding-0.6B) | 0.6B | 32,768 | 1024 | ✅ Available |
+| Text Embedding | [Octen-Embedding-4B](https://huggingface.co/Octen/Octen-Embedding-4B) | 4.0B | 32,768 | 2560 | ✅ Available |
+| Text Embedding | [Octen-Embedding-8B](https://huggingface.co/Octen/Octen-Embedding-8B) | 7.6B | 32,768 | 4096 | ✅ Available |
+
+**Model Family Design**:
+- **Octen-Embedding-8B**: Best performance, RTEB #1, for high-precision retrieval
+- **Octen-Embedding-4B**: Best in 4B category, balanced performance and efficiency
+- **Octen-Embedding-0.6B**: Lightweight deployment, suitable for edge devices and resource-constrained environments
+
+---
+
+## Experimental Results
+
+### RTEB Leaderboard (Overall Performance)
+
+| Model | Embedding Dim | Max Tokens | Mean (Public) | Mean (Private) | Mean (Task) |
+|-------|---------------|------------|---------------|----------------|-------------|
+| **Octen-Embedding-8B** | **4096** | **32768** | **0.7953** | **0.8157** | **0.8045** |
+| voyage-3-large | 1024 | 32000 | 0.7434 | 0.8277 | 0.7812 |
+| gemini-embedding-001 | 3072 | 2048 | 0.7218 | 0.8075 | 0.7602 |
+| **Octen-Embedding-4B** | **2560** | **32768** | **0.7747** | **0.7942** | **0.7834** |
+| MoD-Embedding | 2560 | 32768 | 0.7642 | 0.7900 | 0.7758 |
+| Qwen3-Embedding-8B | 4096 | 32768 | 0.7310 | 0.7838 | 0.7547 |
+| **Octen-Embedding-0.6B** | **1024** | **32768** | **0.7241** | **-** | **-** |
+| voyage-3.5 | 1024 | 32000 | 0.7139 | 0.8102 | 0.7571 |
+| Cohere-embed-v4.0 | 1536 | 128000 | 0.6534 | 0.7943 | 0.7166 |
+| jina-embeddings-v4 | 2048 | 32768 | 0.6652 | 0.7664 | 0.7105 |
+| GritLM-7B | 4096 | 32768 | 0.6187 | 0.7385 | 0.6724 |
+| text-embedding-3-large | 3072 | 8191 | 0.6110 | 0.7130 | 0.6567 |
+| e5-mistral-7b-instruct | 4096 | 32768 | 0.5090 | 0.7091 | 0.5987 |
+| NV-Embed-v2 | 4096 | 32768 | 0.5805 | 0.6691 | 0.6203 |
+| snowflake-arctic-embed-l-v2.0 | 1024 | 8192 | 0.5395 | 0.7079 | 0.6150 |
+| multilingual-e5-large-instruct | 1024 | 514 | 0.5478 | 0.6859 | 0.6097 |
+| gte-multilingual-base | 768 | 8192 | 0.5291 | 0.6697 | 0.5921 |
+| text-embedding-3-small | 1536 | 8191 | 0.5260 | 0.6630 | 0.5874 |
+| bge-m3 | 1024 | 8194 | 0.5216 | 0.6726 | 0.5893 |
+| Qwen3-Embedding-4B | 2560 | 32768 | - | 0.7711 | - |
+| Qwen3-Embedding-0.6B | 1024 | 32768 | - | 0.7117 | - |
+
+---
+
 ## Model Details
 
 - **Base Model**: [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B)

README.md.backup

@@ -0,0 +1,123 @@
+---
+language:
+- en
+- zh
+- multilingual
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- embedding
+- text-embedding
+- retrieval
+pipeline_tag: sentence-similarity
+base_model: Qwen/Qwen3-Embedding-0.6B
+---
+
+# Octen-Embedding-0.6B
+
+Octen-Embedding-0.6B is a text embedding model designed for semantic search and retrieval tasks. This model is fine-tuned from [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) and supports multiple languages, providing high-quality embeddings for various applications.
+
+## Model Details
+
+- **Base Model**: [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B)
+- **Model Size**: 0.6B parameters
+- **Max Sequence Length**: 32,768 tokens
+- **Embedding Dimension**: 1024
+- **Languages**: English, Chinese, and multilingual support
+- **Training Method**: LoRA fine-tuning
+
+## Usage
+
+### Using Sentence Transformers
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("Octen/Octen-Embedding-0.6B")
+
+# Encode sentences
+sentences = [
+    "This is an example sentence",
+    "Each sentence is converted to a vector"
+]
+
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# Output: (2, 1024)
+
+# Compute similarity
+from sentence_transformers.util import cos_sim
+similarity = cos_sim(embeddings[0], embeddings[1])
+print(f"Similarity: {similarity.item():.4f}")
+```
+
+### Using Transformers
+
+```python
+from transformers import AutoModel, AutoTokenizer
+import torch
+import torch.nn.functional as F
+
+tokenizer = AutoTokenizer.from_pretrained("Octen/Octen-Embedding-0.6B", padding_side="left")
+model = AutoModel.from_pretrained("Octen/Octen-Embedding-0.6B")
+model.eval()
+
+def encode(texts):
+    inputs = tokenizer(texts, padding=True, truncation=True,
+                      max_length=8192, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        # Use last token embedding
+        embeddings = outputs.last_hidden_state[:, -1, :]
+        # Normalize embeddings
+        embeddings = F.normalize(embeddings, p=2, dim=1)
+
+    return embeddings
+
+# Example usage
+texts = ["Hello world", "你好世界"]
+embeddings = encode(texts)
+similarity = torch.matmul(embeddings[0], embeddings[1])
+print(f"Similarity: {similarity.item():.4f}")
+```
+
+## Recommended Use Cases
+
+- Semantic search and information retrieval
+- Document similarity and clustering
+- Question answering
+- Cross-lingual retrieval
+- Text classification with embeddings
+
+## Limitations
+
+- Performance may vary across different domains and languages
+- Very long documents (>32K tokens) require truncation
+- Optimized for retrieval tasks, not for text generation
+
+## License
+
+This model is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
+
+This model is derived from [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B), which is also licensed under Apache License 2.0.
+
+## Paper
+
+For more details, please refer to our blog post: [Octen-Embedding: Reproducible 1st Place on RTEB](https://octen-team.github.io/octen_blog/posts/octen-rteb-first-place/)
+
+## Citation
+
+If you find our work helpful, please consider citing:
+
+```bibtex
+@misc{octen2025rteb,
+  title={Octen-Embedding: Reproducible 1st Place on RTEB},
+  author={Octen Team},
+  year={2025},
+  url={https://octen-team.github.io/octen_blog/posts/octen-rteb-first-place/}
+}
+```