--- 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('hasankursun/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 * `matryoshka_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](https://www.google.com/search?q=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}, year={2025}, url={[https://huggingface.co/hasankursun/matryoshka-text-embedding-v1](https://huggingface.co/hasankursun/matryoshka-text-embedding-v1)} } ``` ``` ```