--- tags: - sentence-transformers - sentence-similarity - feature-extraction base_model: NovaSearch/stella_en_1.5B_v5 widget: - source_sentence: $230 pool for outdoors not plastic sentences: - >- Wireless Outdoor Security Camera, WiFi Solar Rechargeable Battery Power IP Surveillance Home Cameras, 1080P, Human Motion Detection, Night Vision, 2-Way Audio, 4dbi Antenna, IP65 Waterproof, Cloud/SD - Bestway SaluSpa Miami Inflatable Hot Tub, 4-Person AirJet Spa - Mens Plush Robe - Fleece Robe, Mens Bathrobe - Fig -Small/Medium - source_sentence: (hearing aid not amplifer) sentences: - Hearing Aid Cleaning Wire for Sound Tubes (2 Packs of 5) - >- Hearing Aids, Enjoyee Hearing Aids for Seniors Rechargeable Hearing Amplifier with Noise Cancelling for Adults Hearing Loss, Digital Ear Hearing Assist Devices with Volume Control - >- 24 Pieces Checking Erasable Pencils Red Pencils Pre-Sharpened #2 HB with Erasable Tops for Checking Map Coloring Tests Grading - source_sentence: (can not use in the usa) european 220voltage hair tools sentences: - Umarex 2252109 Brodax Air Pistol .177 BB - >- One-Step Hair Dryer & Volumizer Hot Air Brush, 3-in-1 Hair Dryer Brush Styler for Straightening, Curling, Salon Negative Ion Ceramic Lightweight Blow Dryers Straightener Curl Hair Brush - >- Mini Portable Flat Iron Tourmaline Ceramic Dual Voltage Travel Iron for Worldwide Use LED Indicator LOVANI Hair Straightener (Ceramic Mini) - source_sentence: '''not my circus not my monkeys my monkeys flyshirt''' sentences: - >- Dresswel Women This is My Circus These are My Monkeys T-Shirt Mom Life Graphic Tee Pocket Shirt Casual Tops - >- Goyunwell Nylon Black Zippers by The Yard #5 10 Yards Nylon Black Long Zipper Tape for Sewing 20Pcs Gunmetal Pulls Slider Zipper by The Yard Black Zipper Roll for Craft Bag Purse Sewing Black Tape - Not My Circus Not My Monkeys Party T-Shirt - source_sentence: '#1 small corded treadmill without remote control' sentences: - >- Goplus Under Desk Treadmill, with Touchable LED Display and Wireless Remote Control, Built-in 3 Workout Modes and 12 Programs, Walking Jogging Machine, Superfit Electric Treadmill for Home Office - Pencil Guy Untipped white round pencil, no eraser 144 to a box - >- SUNNY HEALTH & FITNESS ASUNA Space Saving Treadmill, Motorized with Speakers for AUX Audio Connection - 8730G pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@5 - cosine_ndcg@10 - cosine_mrr@1 - cosine_mrr@5 - cosine_mrr@10 - cosine_map@10 - cosine_map@100 model-index: - name: SentenceTransformer based on NovaSearch/stella_en_1.5B_v5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: ir evaluation type: ir_evaluation metrics: - type: cosine_accuracy@1 value: 0.5243984708792444 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7189116258151563 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.782325163031257 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8452889588486621 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5243984708792444 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.44764260550183643 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.4002248706993479 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.3198335956824826 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.09367303103726933 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.21358059074273028 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.2980042886250134 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.43181596262310956 name: Cosine Recall@10 - type: cosine_ndcg@5 value: 0.46762394517912087 name: Cosine Ndcg@5 - type: cosine_ndcg@10 value: 0.46811760590873697 name: Cosine Ndcg@10 - type: cosine_mrr@1 value: 0.5243984708792444 name: Cosine Mrr@1 - type: cosine_mrr@5 value: 0.625680233865528 name: Cosine Mrr@5 - type: cosine_mrr@10 value: 0.6341315350816145 name: Cosine Mrr@10 - type: cosine_map@10 value: 0.34926714503106293 name: Cosine Map@10 - type: cosine_map@100 value: 0.4065326888005573 name: Cosine Map@100 - task: type: graded-ir name: Graded IR dataset: name: gr evaluation type: gr_evaluation metrics: - type: cosine_accuracy@1 value: 0.708792444344502 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9037553406791096 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9462559028558579 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9840341803463009 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.708792444344502 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.6468780451240538 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.6044974139869574 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.5283786822577018 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.12721101888273206 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.30930165915538804 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.4489712213025254 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7098551817763595 name: Cosine Recall@10 - type: cosine_ndcg@5 value: 0.7127144186187505 name: Cosine Ndcg@5 - type: cosine_ndcg@10 value: 0.7543447490248549 name: Cosine Ndcg@10 - type: cosine_mrr@1 value: 0.708792444344502 name: Cosine Mrr@1 - type: cosine_mrr@5 value: 0.8065999550258622 name: Cosine Mrr@5 - type: cosine_mrr@10 value: 0.8118267710352285 name: Cosine Mrr@10 - type: cosine_map@10 value: 0.6051494969120079 name: Cosine Map@10 - type: cosine_map@100 value: 0.6944358205631005 name: Cosine Map@100 --- # SentenceTransformer based on NovaSearch/stella_en_1.5B_v5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NovaSearch/stella_en_1.5B_v5](https://huggingface.co/NovaSearch/stella_en_1.5B_v5). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [NovaSearch/stella_en_1.5B_v5](https://huggingface.co/NovaSearch/stella_en_1.5B_v5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: Qwen2Model (1): Pooling({'word_embedding_dimension': 1536, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Dense({'in_features': 1536, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("bod9/fulldshardneg") # Run inference sentences = [ '#1 small corded treadmill without remote control', 'SUNNY HEALTH & FITNESS ASUNA Space Saving Treadmill, Motorized with Speakers for AUX Audio Connection - 8730G', 'Goplus Under Desk Treadmill, with Touchable LED Display and Wireless Remote Control, Built-in 3 Workout Modes and 12 Programs, Walking Jogging Machine, Superfit Electric Treadmill for Home Office', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `ir_evaluation` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.5244 | | cosine_accuracy@3 | 0.7189 | | cosine_accuracy@5 | 0.7823 | | cosine_accuracy@10 | 0.8453 | | cosine_precision@1 | 0.5244 | | cosine_precision@3 | 0.4476 | | cosine_precision@5 | 0.4002 | | cosine_precision@10 | 0.3198 | | cosine_recall@1 | 0.0937 | | cosine_recall@3 | 0.2136 | | cosine_recall@5 | 0.298 | | cosine_recall@10 | 0.4318 | | cosine_ndcg@5 | 0.4676 | | **cosine_ndcg@10** | **0.4681** | | cosine_mrr@1 | 0.5244 | | cosine_mrr@5 | 0.6257 | | cosine_mrr@10 | 0.6341 | | cosine_map@10 | 0.3493 | | cosine_map@100 | 0.4065 | #### Graded IR * Dataset: `gr_evaluation` * Evaluated with GradedIREvaluator.GradedIREvaluator | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.7088 | | cosine_accuracy@3 | 0.9038 | | cosine_accuracy@5 | 0.9463 | | cosine_accuracy@10 | 0.984 | | cosine_precision@1 | 0.7088 | | cosine_precision@3 | 0.6469 | | cosine_precision@5 | 0.6045 | | cosine_precision@10 | 0.5284 | | cosine_recall@1 | 0.1272 | | cosine_recall@3 | 0.3093 | | cosine_recall@5 | 0.449 | | cosine_recall@10 | 0.7099 | | cosine_ndcg@5 | 0.7127 | | **cosine_ndcg@10** | **0.7543** | | cosine_mrr@1 | 0.7088 | | cosine_mrr@5 | 0.8066 | | cosine_mrr@10 | 0.8118 | | cosine_map@10 | 0.6051 | | cosine_map@100 | 0.6944 | ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```