Update README.md

README.md

@@ -1,439 +1,439 @@
----
-library_name: sentence-transformers
-pipeline_tag: sentence-similarity
-tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-- matryoshka
-- multilingual
-- embeddings
-- xlm-roberta
-language:
-- multilingual
-- en
-- ar
-- de
-- es
-- fr
-- zh
-- ru
-- tr
-- ko
-- ja
-- it
-- pt
-- nl
-license: cc-by-nc-4.0
-base_model: xlm-roberta-base
-metrics:
-- cosine_accuracy
-- cosine_precision
-- cosine_recall
-- cosine_f1
-- cosine_ap
-- dot_accuracy
-- dot_precision
-- dot_recall
-- dot_f1
-- dot_ap
-- manhattan_accuracy
-- manhattan_precision
-- manhattan_recall
-- manhattan_f1
-- manhattan_ap
-- euclidean_accuracy
-- euclidean_precision
-- euclidean_recall
-- euclidean_f1
-- euclidean_ap
-model-index:
-- name: Matryoshka Text Embedding v1
-  results:
-  - task:
-      type: information-retrieval
-      name: Information Retrieval
-    dataset:
-      name: SciFact
-      type: scifact
-      config: default
-      split: test
-      revision: d56462d0e63a25450459c4f213e49ffdb866f7f9
-    metrics:
-    - type: ndcg_at_10
-      value: 0.63084
-      name: NDCG@10
-    - type: ndcg_at_1
-      value: 0.51
-      name: NDCG@1
-    - type: ndcg_at_3
-      value: 0.578
-      name: NDCG@3
-    - type: ndcg_at_5
-      value: 0.60648
-      name: NDCG@5
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: STSBenchmark
-      type: stsbenchmark
-      config: default
-      split: test
-      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
-    metrics:
-    - type: spearman
-      value: 0.850616
-      name: Spearman
-    - type: pearson
-      value: 0.838067
-      name: Pearson
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: STS17
-      type: sts17-crosslingual-sts
-      config: en-en
-      split: test
-      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
-    metrics:
-    - type: spearman
-      value: 0.873981
-      name: Spearman (en-en)
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: STS17
-      type: sts17-crosslingual-sts
-      config: es-es
-      split: test
-      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
-    metrics:
-    - type: spearman
-      value: 0.88079
-      name: Spearman (es-es)
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: STS17
-      type: sts17-crosslingual-sts
-      config: ko-ko
-      split: test
-      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
-    metrics:
-    - type: spearman
-      value: 0.821019
-      name: Spearman (ko-ko)
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: STS17
-      type: sts17-crosslingual-sts
-      config: ar-ar
-      split: test
-      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
-    metrics:
-    - type: spearman
-      value: 0.805643
-      name: Spearman (ar-ar)
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: STS17
-      type: sts17-crosslingual-sts
-      config: en-de
-      split: test
-      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
-    metrics:
-    - type: spearman
-      value: 0.824516
-      name: Spearman (en-de)
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: STS17
-      type: sts17-crosslingual-sts
-      config: nl-en
-      split: test
-      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
-    metrics:
-    - type: spearman
-      value: 0.819011
-      name: Spearman (nl-en)
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: STS17
-      type: sts17-crosslingual-sts
-      config: it-en
-      split: test
-      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
-    metrics:
-    - type: spearman
-      value: 0.815176
-      name: Spearman (it-en)
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: STS17
-      type: sts17-crosslingual-sts
-      config: fr-en
-      split: test
-      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
-    metrics:
-    - type: spearman
-      value: 0.815679
-      name: Spearman (fr-en)
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: STS17
-      type: sts17-crosslingual-sts
-      config: en-tr
-      split: test
-      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
-    metrics:
-    - type: spearman
-      value: 0.748444
-      name: Spearman (en-tr)
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: STS17
-      type: sts17-crosslingual-sts
-      config: es-en
-      split: test
-      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
-    metrics:
-    - type: spearman
-      value: 0.766019
-      name: Spearman (es-en)
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: STS17
-      type: sts17-crosslingual-sts
-      config: en-ar
-      split: test
-      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
-    metrics:
-    - type: spearman
-      value: 0.71912
-      name: Spearman (en-ar)
----
-
-# Matryoshka Text Embedding v1
-
-A multilingual text embedding model with Matryoshka Representation Learning, allowing flexible embedding dimensions from 64D to 1024D.
-
-## Model Overview
-
-This model implements Matryoshka Representation Learning, enabling you to truncate embeddings to different dimensions while maintaining good performance. This allows you to balance accuracy, speed, and storage based on your specific needs.
-
-### Key Features
-
-- **Flexible Dimensions**: Choose from 7 different embedding sizes (64D, 128D, 256D, 384D, 512D, 768D, 1024D)
-- **Multilingual Support**: Trained on 100+ languages
-- **Base Architecture**: XLM-RoBERTa
-- **Max Sequence Length**: 8192 tokens
-
-## Quick Start
-
-### Installation
-
-```python
-pip install sentence-transformers
-```
-
-### Basic Usage
-
-```python
-from sentence_transformers import SentenceTransformer
-
-# Load model
-model = SentenceTransformer('matryoshka-text-embedding-v1')
-
-# Full precision (1024D)
-embeddings = model.encode(["Your text here"])
-
-# Balanced mode (512D) - Recommended for most use cases
-embeddings = model.encode(["Your text here"], truncate_dim=512)
-
-# Fast mode (256D) - For high-throughput applications
-embeddings = model.encode(["Your text here"], truncate_dim=256)
-
-# Ultra-fast mode (128D) - For real-time applications
-embeddings = model.encode(["Your text here"], truncate_dim=128)
-```
-
-## Performance Benchmarks
-
-### SciFact (Scientific Document Retrieval)
-
-| Dimension | NDCG@10 | Relative Performance |
-|-----------|---------|---------------------|
-| **1024D** | 0.6308 | 100.0% |
-| **768D** | 0.6277 | 99.5% |
-| **512D** | 0.6114 | 96.9% |
-| **384D** | 0.6035 | 95.7% |
-| **256D** | 0.5614 | 89.0% |
-| **128D** | 0.4732 | 75.0% |
-| **64D** | 0.3317 | 52.6% |
-
-### STSBenchmark (English Semantic Similarity)
-
-- **Spearman**: 0.8506 (1024D)
-- **Pearson**: 0.8381 (1024D)
-
-### STS17 (Multilingual Semantic Similarity)
-
-**Average Spearman Correlation across languages: 0.8096**
-
-Performance by language pair (1024D):
-- Spanish (es-es): 0.8808
-- English (en-en): 0.8740
-- German (en-de): 0.8245
-- Korean (ko-ko): 0.8210
-- French (fr-en): 0.8157
-- Italian (it-en): 0.8152
-- Dutch (nl-en): 0.8190
-- Arabic (ar-ar): 0.8056
-- Turkish (en-tr): 0.7484
-- Spanish-English (es-en): 0.7660
-- English-Arabic (en-ar): 0.7191
-
-## Use Cases
-
-### High Accuracy Applications (768D-1024D)
-- Scientific literature search
-- Legal document retrieval
-- Medical information systems
-
-### Balanced Production (512D) - Recommended
-- General web search
-- E-commerce product search
-- Content recommendation engines
-- Knowledge base retrieval
-
-### High-Throughput Systems (256D-384D)
-- Real-time search APIs
-- Large-scale document indexing
-- Social media search
-
-### Mobile & Edge Devices (64D-128D)
-- Mobile applications
-- IoT devices
-- Browser-based search
-- Resource-constrained environments
-
-## Advanced Usage
-
-### Semantic Search
-
-```python
-import numpy as np
-from sentence_transformers import util
-
-# Index documents with 512D (optimal balance)
-documents = [
-    "Artificial intelligence is transforming healthcare.",
-    "Machine learning models require large datasets.",
-    "Quantum computing promises exponential speedups."
-]
-
-doc_embeddings = model.encode(documents, truncate_dim=512)
-
-# Search with same dimension
-query = "How is AI used in medicine?"
-query_embedding = model.encode(query, truncate_dim=512)
-
-# Compute similarities
-similarities = util.cos_sim(query_embedding, doc_embeddings)
-top_result = np.argmax(similarities)
-
-print(f"Most relevant: {documents[top_result]}")
-```
-
-### Integration with FAISS
-
-```python
-import faiss
-import numpy as np
-
-# Create embeddings with 512D
-embeddings = model.encode(documents, truncate_dim=512)
-embeddings = embeddings.astype('float32')
-
-# Build FAISS index
-dimension = 512
-index = faiss.IndexFlatIP(dimension)
-faiss.normalize_L2(embeddings)
-index.add(embeddings)
-
-# Search
-query_embedding = model.encode(query, truncate_dim=512).astype('float32')
-faiss.normalize_L2(query_embedding.reshape(1, -1))
-distances, indices = index.search(query_embedding.reshape(1, -1), k=10)
-```
-
-## Technical Details
-
-### Architecture
-- **Base**: XLM-RoBERTa transformer encoder
-- **Embedding Dimensions**: 1024 (full) with 7 supported truncation levels
-- **Max Sequence Length**: 8192 tokens
-- **Vocabulary Size**: 250,002 tokens
-- **Parameters**: ~568M
-
-### Training
-- **Technique**: Matryoshka Representation Learning
-- **Languages**: 100+ languages
-- **Max Input Length**: 8192 tokens
-
-## Model Files
-
-- `pytorch_model.bin` - Model weights
-- `config.json` - Model configuration
-- `tokenizer.json` - Tokenizer configuration
-- `lumees_config.json` - Matryoshka-specific configuration
-
-## License
-
-This model is released under the **CC-BY-NC-4.0** (Creative Commons Attribution-NonCommercial 4.0 International) license.
-
-See the [LICENSE](LICENSE) file for full details and acknowledgments.
-
-## Acknowledgments
-
-This model builds upon important foundational work:
-
-- **XLM-RoBERTa**: Base architecture for multilingual representations
-- **BAAI**: For their contributions through RetroMAE and BGE-M3 papers
-- **Matryoshka Representation Learning**: Training methodology (Kusupati et al., 2022)
-
-## Citation
-
-If you use this model in your research or application, please cite:
-
-```bibtex
-@misc{matryoshka-text-embedding-v1,
-  title={Matryoshka Text Embedding v1},
-  author={Hasan Kurşun and Kerem Berkay Yanık},
-  year={2025},
-  url={https://huggingface.co/matryoshka-text-embedding-v1},
-  organization={Lumees},
-  contact={[email protected]},
-  website={https://lumees.io}
-}
-```
+---
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- matryoshka
+- multilingual
+- embeddings
+- xlm-roberta
+language:
+- multilingual
+- en
+- ar
+- de
+- es
+- fr
+- zh
+- ru
+- tr
+- ko
+- ja
+- it
+- pt
+- nl
+license: cc-by-nc-4.0
+base_model: xlm-roberta-base
+metrics:
+- cosine_accuracy
+- cosine_precision
+- cosine_recall
+- cosine_f1
+- cosine_ap
+- dot_accuracy
+- dot_precision
+- dot_recall
+- dot_f1
+- dot_ap
+- manhattan_accuracy
+- manhattan_precision
+- manhattan_recall
+- manhattan_f1
+- manhattan_ap
+- euclidean_accuracy
+- euclidean_precision
+- euclidean_recall
+- euclidean_f1
+- euclidean_ap
+model-index:
+- name: Matryoshka Text Embedding v1
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: SciFact
+      type: scifact
+      config: default
+      split: test
+      revision: d56462d0e63a25450459c4f213e49ffdb866f7f9
+    metrics:
+    - type: ndcg_at_10
+      value: 0.63084
+      name: NDCG@10
+    - type: ndcg_at_1
+      value: 0.51
+      name: NDCG@1
+    - type: ndcg_at_3
+      value: 0.578
+      name: NDCG@3
+    - type: ndcg_at_5
+      value: 0.60648
+      name: NDCG@5
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: STSBenchmark
+      type: stsbenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: spearman
+      value: 0.850616
+      name: Spearman
+    - type: pearson
+      value: 0.838067
+      name: Pearson
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: STS17
+      type: sts17-crosslingual-sts
+      config: en-en
+      split: test
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+    metrics:
+    - type: spearman
+      value: 0.873981
+      name: Spearman (en-en)
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: STS17
+      type: sts17-crosslingual-sts
+      config: es-es
+      split: test
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+    metrics:
+    - type: spearman
+      value: 0.88079
+      name: Spearman (es-es)
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: STS17
+      type: sts17-crosslingual-sts
+      config: ko-ko
+      split: test
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+    metrics:
+    - type: spearman
+      value: 0.821019
+      name: Spearman (ko-ko)
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: STS17
+      type: sts17-crosslingual-sts
+      config: ar-ar
+      split: test
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+    metrics:
+    - type: spearman
+      value: 0.805643
+      name: Spearman (ar-ar)
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: STS17
+      type: sts17-crosslingual-sts
+      config: en-de
+      split: test
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+    metrics:
+    - type: spearman
+      value: 0.824516
+      name: Spearman (en-de)
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: STS17
+      type: sts17-crosslingual-sts
+      config: nl-en
+      split: test
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+    metrics:
+    - type: spearman
+      value: 0.819011
+      name: Spearman (nl-en)
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: STS17
+      type: sts17-crosslingual-sts
+      config: it-en
+      split: test
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+    metrics:
+    - type: spearman
+      value: 0.815176
+      name: Spearman (it-en)
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: STS17
+      type: sts17-crosslingual-sts
+      config: fr-en
+      split: test
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+    metrics:
+    - type: spearman
+      value: 0.815679
+      name: Spearman (fr-en)
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: STS17
+      type: sts17-crosslingual-sts
+      config: en-tr
+      split: test
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+    metrics:
+    - type: spearman
+      value: 0.748444
+      name: Spearman (en-tr)
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: STS17
+      type: sts17-crosslingual-sts
+      config: es-en
+      split: test
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+    metrics:
+    - type: spearman
+      value: 0.766019
+      name: Spearman (es-en)
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: STS17
+      type: sts17-crosslingual-sts
+      config: en-ar
+      split: test
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+    metrics:
+    - type: spearman
+      value: 0.71912
+      name: Spearman (en-ar)
+---
+
+# Matryoshka Text Embedding v1
+
+A multilingual text embedding model with Matryoshka Representation Learning, allowing flexible embedding dimensions from 64D to 1024D.
+
+## Model Overview
+
+This model implements Matryoshka Representation Learning, enabling you to truncate embeddings to different dimensions while maintaining good performance. This allows you to balance accuracy, speed, and storage based on your specific needs.
+
+### Key Features
+
+- **Flexible Dimensions**: Choose from 7 different embedding sizes (64D, 128D, 256D, 384D, 512D, 768D, 1024D)
+- **Multilingual Support**: Trained on 100+ languages
+- **Base Architecture**: XLM-RoBERTa
+- **Max Sequence Length**: 8192 tokens
+
+## Quick Start
+
+### Installation
+
+```python
+pip install sentence-transformers
+```
+
+### Basic Usage
+
+```python
+from sentence_transformers import SentenceTransformer
+
+# Load model
+model = SentenceTransformer('matryoshka-text-embedding-v1')
+
+# Full precision (1024D)
+embeddings = model.encode(["Your text here"])
+
+# Balanced mode (512D) - Recommended for most use cases
+embeddings = model.encode(["Your text here"], truncate_dim=512)
+
+# Fast mode (256D) - For high-throughput applications
+embeddings = model.encode(["Your text here"], truncate_dim=256)
+
+# Ultra-fast mode (128D) - For real-time applications
+embeddings = model.encode(["Your text here"], truncate_dim=128)
+```
+
+## Performance Benchmarks
+
+### SciFact (Scientific Document Retrieval)
+
+| Dimension | NDCG@10 | Relative Performance |
+|-----------|---------|---------------------|
+| **1024D** | 0.6308 | 100.0% |
+| **768D** | 0.6277 | 99.5% |
+| **512D** | 0.6114 | 96.9% |
+| **384D** | 0.6035 | 95.7% |
+| **256D** | 0.5614 | 89.0% |
+| **128D** | 0.4732 | 75.0% |
+| **64D** | 0.3317 | 52.6% |
+
+### STSBenchmark (English Semantic Similarity)
+
+- **Spearman**: 0.8506 (1024D)
+- **Pearson**: 0.8381 (1024D)
+
+### STS17 (Multilingual Semantic Similarity)
+
+**Average Spearman Correlation across languages: 0.8096**
+
+Performance by language pair (1024D):
+- Spanish (es-es): 0.8808
+- English (en-en): 0.8740
+- German (en-de): 0.8245
+- Korean (ko-ko): 0.8210
+- French (fr-en): 0.8157
+- Italian (it-en): 0.8152
+- Dutch (nl-en): 0.8190
+- Arabic (ar-ar): 0.8056
+- Turkish (en-tr): 0.7484
+- Spanish-English (es-en): 0.7660
+- English-Arabic (en-ar): 0.7191
+
+## Use Cases
+
+### High Accuracy Applications (768D-1024D)
+- Scientific literature search
+- Legal document retrieval
+- Medical information systems
+
+### Balanced Production (512D) - Recommended
+- General web search
+- E-commerce product search
+- Content recommendation engines
+- Knowledge base retrieval
+
+### High-Throughput Systems (256D-384D)
+- Real-time search APIs
+- Large-scale document indexing
+- Social media search
+
+### Mobile & Edge Devices (64D-128D)
+- Mobile applications
+- IoT devices
+- Browser-based search
+- Resource-constrained environments
+
+## Advanced Usage
+
+### Semantic Search
+
+```python
+import numpy as np
+from sentence_transformers import util
+
+# Index documents with 512D (optimal balance)
+documents = [
+    "Artificial intelligence is transforming healthcare.",
+    "Machine learning models require large datasets.",
+    "Quantum computing promises exponential speedups."
+]
+
+doc_embeddings = model.encode(documents, truncate_dim=512)
+
+# Search with same dimension
+query = "How is AI used in medicine?"
+query_embedding = model.encode(query, truncate_dim=512)
+
+# Compute similarities
+similarities = util.cos_sim(query_embedding, doc_embeddings)
+top_result = np.argmax(similarities)
+
+print(f"Most relevant: {documents[top_result]}")
+```
+
+### Integration with FAISS
+
+```python
+import faiss
+import numpy as np
+
+# Create embeddings with 512D
+embeddings = model.encode(documents, truncate_dim=512)
+embeddings = embeddings.astype('float32')
+
+# Build FAISS index
+dimension = 512
+index = faiss.IndexFlatIP(dimension)
+faiss.normalize_L2(embeddings)
+index.add(embeddings)
+
+# Search
+query_embedding = model.encode(query, truncate_dim=512).astype('float32')
+faiss.normalize_L2(query_embedding.reshape(1, -1))
+distances, indices = index.search(query_embedding.reshape(1, -1), k=10)
+```
+
+## Technical Details
+
+### Architecture
+- **Base**: XLM-RoBERTa transformer encoder
+- **Embedding Dimensions**: 1024 (full) with 7 supported truncation levels
+- **Max Sequence Length**: 8192 tokens
+- **Vocabulary Size**: 250,002 tokens
+- **Parameters**: ~568M
+
+### Training
+- **Technique**: Matryoshka Representation Learning
+- **Languages**: 100+ languages
+- **Max Input Length**: 8192 tokens
+
+## Model Files
+
+- `pytorch_model.bin` - Model weights
+- `config.json` - Model configuration
+- `tokenizer.json` - Tokenizer configuration
+- `lumees_config.json` - Matryoshka-specific configuration
+
+## License
+
+This model is released under the **CC-BY-NC-4.0** (Creative Commons Attribution-NonCommercial 4.0 International) license.
+
+See the [LICENSE](LICENSE) file for full details and acknowledgments.
+
+## Acknowledgments
+
+This model builds upon important foundational work:
+
+- **XLM-RoBERTa**: Base architecture for multilingual representations
+- **BAAI**: For their contributions through RetroMAE and BGE-M3 papers
+- **Matryoshka Representation Learning**: Training methodology (Kusupati et al., 2022)
+
+## Citation
+
+If you use this model in your research or application, please cite:
+
+```bibtex
+@misc{matryoshka-text-embedding-v1,
+  title={Matryoshka Text Embedding v1},
+  author={Hasan Kurşun and Kerem Berkay Yanık},
+  year={2025},
+  url={https://huggingface.co/lumees/lumees-matryoshka-embedding-v1},
+  organization={Lumees},
+  contact={[email protected]},
+  website={https://lumees.io}
+}
+```