--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:786 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: Snowflake/snowflake-arctic-embed-l widget: - source_sentence: How much money was saved through systems automation and process improvement efforts? sentences: - Member","Thought Leadership","E-commerce","Entrepreneurship","Mobile Devices","Product Management","Start-ups","Strategic Partnerships","Strategy"] - '- URL":"linkedin.com/company/channel-factory","Description":"• Helped scale the video advertising startup from 0 to 8-figure revenues and 5 to 40+ employees in 2.5 years.\n• Managed the company''s day-to-day operations. Saved $100,000+ through systems automation and process improvement efforts.\n• Led sales operations for a 7-person ad sales team and managed BD partnerships with one of the three largest online travel agencies, a major online ad management platform, and rep firms in the United Kingdom, India, Brazil, and Australia.\n• Spearheaded company recruitment efforts and improved HR budget efficiency to save $350,000+ annually.\n• Evaluated, implemented, and managed third party business systems, including Salesforce and' - and start building trust and camaraderie at work - vital assets in providing psychological safety, enabling agility and unleashing growth.\n","Company Size":"11-50","Industries":["Administrative Services","Community and Lifestyle","Government and Military","HR and Recruiting","Health","Information Technology","Software"],"Title":"Co-Founder and Servant CEO","Departments":["Senior Leadership"],"Start Date":"2018-01-01","End Date":null,"Location":"Santa Monica, California, United States, United States","Is Current":true,"Job Order":18},{"Company Name":"CNCCEF","Specter - Company ID":"5e3b912d137e998b5ae832aa","Domain":"cnccef.org","LinkedIn - - source_sentence: What skills do you possess that relate to marketing and brand development? sentences: - 'I have been fortunate to have been a part of the creation and/or growth story for brands including ASYSTEM, Formula Fig, Aritzia, Mr Porter to name a few. Skills: ["E-commerce","Advertising","Social Media","Strategy","Marketing","Online Advertising","Fashion","Brand Development","Marketing Strategy","Digital Strategy","Media Relations","Retail","Business Development","Digital Marketing","Mobile Devices","Digital Media","Marketing Communications","Strategic Communications","Branding & Identity","Business Strategy","Product Development","Social media","eCommerce","Art Direction","Brand Management","Brand Strategy","Consumer Behavior","Creative Strategy","E-Commerce","Media"]' - is able to do so in near real time.","Company Size":null,"Industries":null,"Title":"ceo","Departments":["Senior Leadership"],"Start Date":"2005-03-01","End Date":"2007-12-01","Location":null,"Is Current":false,"Job Order":8},{"Company Name":"SnapNames","Specter - Company ID":"5e3bc17800c8f4c966a8bad6","Domain":"snapnames.com","LinkedIn - URL":"linkedin.com/company/snapnames-com","Description":"I served as a strategic advisor to the CEO in the capacity of a Board Director, and briefly as Chairman of the Board, prior to its acquisition by Oversee","Company Size":"11-50","Industries":["Commerce and Shopping","Internet Services"],"Title":"Director Board Of Directors","Departments":["Senior Leadership"],"Start Date":"2002-04-01","End - "Technology\",\"Software\",\"Transportation\"],\"Title\":\"Co-Founder & CTO\"\ ,\"Departments\":[\"Senior Leadership\",\"Engineering\"],\"Start Date\":\"2021-08-01\"\ ,\"End Date\":null,\"Location\":\"Los Altos, California, United States, United\ \ States\",\"Is Current\":true,\"Job Order\":6},{\"Company Name\":\"XDLINX Space\ \ Labs\",\"Specter - Company ID\":\"6712477ab8cbb513aaee920e\",\"Domain\":\"xdlinx.space\"\ ,\"LinkedIn - URL\":\"linkedin.com/company/xdlinx-labs\",\"Description\":null,\"\ Company Size\":\"51-200\",\"Industries\":[\"Hardware\",\"Transportation\"],\"\ Title\":\"Co-Founder\",\"Departments\":[\"Senior Leadership\"],\"Start Date\"\ :\"2022-07-01\",\"End Date\":null,\"Location\":\"HyderÄ\x81bÄ\x81d, Telangana,\ \ India, Asia\",\"Is Current\":true,\"Job Order\":5},{\"Company Name\":\"Diamanti\"\ ,\"Specter - Company" - source_sentence: In what ways does SignalFire support companies at the seed stage? sentences: - '- URL":"linkedin.com/school/%D0%BC%D0%BE%D1%81%D0%BA%D0%BE%D0%B2%D1%81%D0%BA%D0%B0%D1%8F-%D0%BC%D0%B5%D0%B6%D0%B4%D1%83%D0%BD%D0%B0%D1%80%D0%BE%D0%B4%D0%BD%D0%B0%D1%8F-%D0%B2%D1%8B%D1%81%D1%88%D0%B0%D1%8F-%D1%88%D0%BA%D0%BE%D0%BB%D0%B0-%D0%B1%D0%B8%D0%B7%D0%BD%D0%B5%D1%81%D0%B0-%C2%AB%D0%BC%D0%B8%D1%80%D0%B1%D0%B8%D1%81%C2%BB-%D0%B8%D0%BD%D1%81%D1%82%D0%B8%D1%82%D1%83%D1%82-","Field of Study":"","Degree Title":"Integrated year abroad","Description":null,"Start Date":"2006-01-01","End Date":"2006-01-01","Location":"Moscow, Moscow, Russian Federation, Russia"},{"Name":"Hochschule Furtwangen University","LinkedIn - URL":"linkedin.com/school/hochschule-furtwangen-university","Field of Study":"International Management","Degree Title":"Bachelor' - I specialize in driving the data algorithms that can predict venture outcomes and target the top 5% of funding rounds at each stage. I have a product mentality and a people-first, technology second, point of view. I also have an honorary doctorate from the University of Kent, where I studied British Constitution and Sociology. I have lived in Palo Alto, California since 1997, and I am passionate about anticipating and creating change in the tech industry. - 'firepower at the seed stage to solve the biggest entrepreneur pain points. Our distributed network approach provides expert advice from some of the world''s best entrepreneurs, product & engineering leaders in virtually every key discipline and industry. We have developed a first of its kind centralized infrastructure to help with recruiting exceptional talent, business development, customer acquisition as well as educational & community events. We don’t follow the crowd, and almost always lead our investment rounds as the first institutional investors in exceptional companies. You can read more about SignalFire at: https://medium.com/signalfire-fund","Company Size":"51-200","Industries":["Data and Analytics","Finance","Lending and' - source_sentence: What role did the individual hold at the company from 1998 to 2002? sentences: - Current":true,"Job Order":25},{"Company Name":"BigSpring","Specter - Company ID":"653554dfd1653b1e73051e7c","Domain":"bigspring.ai","LinkedIn - URL":"linkedin.com/company/bigspringai","Description":null,"Company Size":"11-50","Industries":["Community and Lifestyle","Data and Analytics","DeepTech","Education","HR and Recruiting","Professional Services","Software"],"Title":"Advisor","Departments":["Other"],"Start Date":"2019-01-01","End Date":null,"Location":"San Francisco, California, United States, United States","Is Current":true,"Job Order":24},{"Company Name":"Clockwise","Specter - Company ID":"5e3a8f1e040ca7b0c6f0bd98","Domain":"getclockwise.com","LinkedIn - URL":"linkedin.com/company/clockwise-inc.","Description":null,"Company - a relationship to VeriSIgn to sell Internet Keywords through its channels.\n\nAn IPO filing.\n\nOver 350 employees.","Company Size":"1-10","Industries":["Internet Services","Software","Transportation"],"Title":"CEO, President, Chairman","Departments":["Senior Leadership"],"Start Date":"1998-01-01","End Date":"2002-06-01","Location":"San Carlos, California, United States, United States","Is Current":false,"Job Order":4},{"Company Name":"NetNames","Specter - Company ID":"5e3bbde400c8f4c9669d8d4b","Domain":"netnames.com","LinkedIn - URL":"linkedin.com/company/netnames","Description":"I seed funded NetNames. We sold it to NetBenefit in 2000. I was a board member of the merged entity through 2001. NetNames was the world's first domain name - '- Company ID":"64f802e6538115f141f4063a","Domain":"trynectar.io","LinkedIn - URL":"linkedin.com/company/nectar-ai","Description":null,"Company Size":"11-50","Industries":["Advertising","Commerce and Shopping","Data and Analytics","DeepTech","Sales and Marketing","Software"],"Title":"Investor","Departments":["Senior Leadership"],"Start Date":"2023-10-01","End Date":null,"Location":"Seattle, Washington, United States, United States","Is Current":true,"Job Order":32},{"Company Name":"BinStar","Specter - Company ID":"6411d185abe7c1e313b62b4a","Domain":"bin-star.com","LinkedIn - URL":"linkedin.com/company/binstar","Description":null,"Company Size":"1-10","Industries":["Commerce and Shopping"],"Title":"Investor","Departments":["Senior' - source_sentence: What is the primary focus of Fluence as a continuing education organization? sentences: - Name":"Fluence","Specter - Company ID":"621f973f972ef7e5d69c8085","Domain":"fluencetraining.com","LinkedIn - URL":"linkedin.com/company/fluencetraining","Description":"Fluence is a leading continuing education organization in psychedelic therapy.","Company Size":"11-50","Industries":["Education","HR and Recruiting","Health","Software"],"Title":"Advisor","Departments":["Other"],"Start Date":"2023-07-01","End Date":null,"Location":"New York City, New York, United States, United States","Is Current":true,"Job Order":17},{"Company Name":"VentureKit","Specter - Company ID":null,"Domain":"venturekit.com","LinkedIn - URL":"linkedin.com/company/venturekit","Description":"VentureKit publishes free guides to help entrepreneurs get things - Order":7},{"Company Name":"Jelastic","Specter - Company ID":"5e3bbee700c8f4c966a06981","Domain":"jelastic.com","LinkedIn - URL":"linkedin.com/company/jelastic","Description":"Jelastic is a cloud platform that provides multi-cloud Platform as a Service (PaaS) based on container technology. It supports a wide range of programming languages and frameworks, and is easy to scale up or down to meet your changing needs. Acquired by Virtoozo in 2021.\n\nRole and results:\n- Managed an engineering team\n- Managed R&D projects\n- Jelastic won several international startup awards \n- Acquired by Virtozzo","Company Size":"11-50","Industries":["Information Technology","Internet Services","Software"],"Title":"Co-Founder","Departments":["Senior - 'Education Level: Bachelor''s Degree Current Position Title: CTO, Head of Research Current Position Company Name: Mursion Current Position Company Website: mursion.com Past Position Title: CEO and Co-founder Past Position Company Name: DNABLOCK Past Position Company Website: dnablock.com Current Tenure: 85.0 Average Tenure: 34.0 Languages: [{"Name":"Spanish","Proficiency Level":"Limited Working Proficiency"},{"Name":"Arabic","Proficiency Level":"Limited Working Proficiency"}] LinkedIn - Followers: 5022.0 LinkedIn - Connections: 2997.0' 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@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.7916666666666666 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9666666666666667 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.975 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9833333333333333 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7916666666666666 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.32222222222222213 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19500000000000003 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09833333333333334 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7916666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9666666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.975 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9833333333333333 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.901899634958155 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.874107142857143 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8748790726817042 name: Cosine Map@100 --- # SentenceTransformer based on Snowflake/snowflake-arctic-embed-l This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). 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:** [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) - **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: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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): Normalize() ) ``` ## 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("ngiometti/legal-ft-3") # Run inference sentences = [ 'What is the primary focus of Fluence as a continuing education organization?', 'Name":"Fluence","Specter - Company ID":"621f973f972ef7e5d69c8085","Domain":"fluencetraining.com","LinkedIn - URL":"linkedin.com/company/fluencetraining","Description":"Fluence is a leading continuing education organization in psychedelic therapy.","Company Size":"11-50","Industries":["Education","HR and Recruiting","Health","Software"],"Title":"Advisor","Departments":["Other"],"Start Date":"2023-07-01","End Date":null,"Location":"New York City, New York, United States, United States","Is Current":true,"Job Order":17},{"Company Name":"VentureKit","Specter - Company ID":null,"Domain":"venturekit.com","LinkedIn - URL":"linkedin.com/company/venturekit","Description":"VentureKit publishes free guides to help entrepreneurs get things', 'Education Level: Bachelor\'s Degree\nCurrent Position Title: CTO, Head of Research\nCurrent Position Company Name: Mursion\nCurrent Position Company Website: mursion.com\nPast Position Title: CEO and Co-founder\nPast Position Company Name: DNABLOCK\nPast Position Company Website: dnablock.com\nCurrent Tenure: 85.0\nAverage Tenure: 34.0\nLanguages: [{"Name":"Spanish","Proficiency Level":"Limited Working Proficiency"},{"Name":"Arabic","Proficiency Level":"Limited Working Proficiency"}]\nLinkedIn - Followers: 5022.0\nLinkedIn - Connections: 2997.0', ] 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 * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.7917 | | cosine_accuracy@3 | 0.9667 | | cosine_accuracy@5 | 0.975 | | cosine_accuracy@10 | 0.9833 | | cosine_precision@1 | 0.7917 | | cosine_precision@3 | 0.3222 | | cosine_precision@5 | 0.195 | | cosine_precision@10 | 0.0983 | | cosine_recall@1 | 0.7917 | | cosine_recall@3 | 0.9667 | | cosine_recall@5 | 0.975 | | cosine_recall@10 | 0.9833 | | **cosine_ndcg@10** | **0.9019** | | cosine_mrr@10 | 0.8741 | | cosine_map@100 | 0.8749 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 786 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 786 samples: | | sentence_0 | sentence_1 | |:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence_0 | sentence_1 | |:------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What types of products has the individual built experience in, according to the context? | experience in building world class hardware and software products for consumer electronics, aerospace and enterprise software solutions. Proven track record of building big-data cloud computing software and analytic software platform with AI, Computer Vision and Machine Learning. Progressive, innovative and highly valued for aligning corporate strategies with market opportunities, translating goals into actionable plans, and providing leadership to multi-discipline, cross cultural teams. | | How does the individual align corporate strategies with market opportunities? | experience in building world class hardware and software products for consumer electronics, aerospace and enterprise software solutions. Proven track record of building big-data cloud computing software and analytic software platform with AI, Computer Vision and Machine Learning. Progressive, innovative and highly valued for aligning corporate strategies with market opportunities, translating goals into actionable plans, and providing leadership to multi-discipline, cross cultural teams. | | What is the company size of Diamanti? | - Company ID":"5e3a8f19040ca7b0c6f031bf","Domain":"diamanti.com","LinkedIn - URL":"linkedin.com/company/diamanti","Description":null,"Company Size":"51-200","Industries":["Consumer Products","Hardware","Information Technology","Internet Services","Software"],"Title":"Chief Operating Officer","Departments":["Senior Leadership","Operations"],"Start Date":"2018-11-01","End Date":"2021-07-01","Location":"San Jose, California, United States, United States","Is Current":false,"Job Order":4},{"Company Name":"Planet","Specter - Company ID":"5e3bc13c00c8f4c966a7da4c","Domain":"planet.com","LinkedIn - URL":"linkedin.com/company/planet-labs","Description":"Planet operates the world's largest fleet of Earth imaging satellites to daily image the entire | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `num_train_epochs`: 10 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs | Epoch | Step | Training Loss | cosine_ndcg@10 | |:------:|:----:|:-------------:|:--------------:| | 0.6329 | 50 | - | 0.8917 | | 1.0 | 79 | - | 0.9080 | | 1.2658 | 100 | - | 0.9265 | | 1.8987 | 150 | - | 0.9091 | | 2.0 | 158 | - | 0.9100 | | 2.5316 | 200 | - | 0.9214 | | 3.0 | 237 | - | 0.9110 | | 3.1646 | 250 | - | 0.9161 | | 3.7975 | 300 | - | 0.9108 | | 4.0 | 316 | - | 0.9145 | | 4.4304 | 350 | - | 0.8955 | | 5.0 | 395 | - | 0.9019 | | 5.0633 | 400 | - | 0.9008 | | 5.6962 | 450 | - | 0.8980 | | 6.0 | 474 | - | 0.9036 | | 6.3291 | 500 | 0.7603 | 0.9021 | | 6.9620 | 550 | - | 0.8977 | | 7.0 | 553 | - | 0.8976 | | 7.5949 | 600 | - | 0.9059 | | 8.0 | 632 | - | 0.9005 | | 8.2278 | 650 | - | 0.9039 | | 8.8608 | 700 | - | 0.9050 | | 9.0 | 711 | - | 0.9052 | | 9.4937 | 750 | - | 0.9021 | | 10.0 | 790 | - | 0.9019 | ### Framework Versions - Python: 3.13.1 - Sentence Transformers: 3.4.1 - Transformers: 4.49.0 - PyTorch: 2.6.0+cu124 - Accelerate: 1.4.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## 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", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### 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} } ```