--- base_model: Qwen/Qwen2.5-0.5B-Instruct 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 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:1580101 - loss:MultipleNegativesRankingLoss widget: - source_sentence: "query: \n\nHuman: 01011001 01001111 01010101 00100000 01000010\ \ 01001100 01000001 01000010 01000010 01000101 01010010 01010010 01010010 01010010\ \ 01010010 01001001 01001110 01000111 00100000 01000010 01001111 01001100 01010100\ \ 01000010 01010010 01010010 01010010 01010010 01000001 01001001 01001110 01010011\ \ 00100001 00100001 00100001\n\nAssistant: " sentences: - 'With your competencies in remote sensing, machine learning, and water resource knowledge, you have a unique skill set that can be applied in a variety of fields. Here are some job roles and projects you might consider: 1. **Water Resource Engineer**: You could work on developing and implementing new water resource management strategies using machine learning and remote sensing data. 2. **Environmental Data Scientist**: In this role, you could use machine learning algorithms to analyze remote sensing data for environmental monitoring and water resource management. 3. **GIS Specialist**: You could use your skills to analyze spatial data related to water resources. 4. **Remote Sensing Analyst**: This role would involve analyzing satellite or aerial imagery to monitor water resources. 5. **Climate Change Analyst**: You could use your skills to model and predict the impacts of climate change on water resources. As for projects, you could work on: 1. Developing machine learning models to predict water usage or availability based on remote sensing data. 2. Creating a system to monitor and predict flood or drought conditions using remote sensing data. 3. Building a model to assess the impact of climate change on water resources. Companies that might be interested in your skills include: 1. **Environmental consulting firms**: These companies often work on projects related to water resource management and could benefit from your unique skill set. 2. **Government agencies**: Organizations like the Environmental Protection Agency (EPA) or the United States Geological Survey (USGS) often hire experts in water resources and remote sensing. 3. **Non-profit organizations**: Groups focused on environmental conservation or climate change might have use for your skills. 4. **Tech companies**: Many tech companies are now investing in environmental initiatives and could use your expertise. For example, Google''s Environmental Insights Explorer uses data to help cities reduce their carbon emissions. Remember, these are just suggestions. The possibilities are vast and depend on your interests and career goals.' - Hello! How can I assist you today? - You BLABBERING BOLTBRRAIINGS!!!! - source_sentence: "query: \n\nHuman: 4. Cap the bottle tightly and shake it thoroughly\ \ so that all the contents are properly mixed.\n\nAssistant: " sentences: - 'Sure, here''s how you can do it: 1. Ensure the bottle is on a stable surface to prevent any spills. 2. Place the cap on the bottle. Make sure it''s aligned properly with the bottle''s opening. 3. Press the cap down firmly. You should hear a click or feel resistance, which indicates the cap is secure. 4. Once the cap is secure, hold the bottle firmly with both hands. 5. Begin to shake the bottle. Use an up-and-down motion, and make sure to shake it for enough time to ensure all contents are thoroughly mixed. This could be anywhere from a few seconds to a minute, depending on the contents. 6. After shaking, let the bottle sit for a moment. This allows any bubbles or foam to settle. 7. Check the mixture. If it''s not fully mixed, repeat the shaking process. Remember, always handle the bottle carefully to avoid any spills or leaks.' - "Understood. To cap the bottle tightly, please follow these steps: \n\n1. Locate\ \ the cap of the bottle. \n2. Turn the cap counterclockwise until it stops. \n\ 3. Apply firm pressure to the cap to ensure it is securely in place. \n4. Shake\ \ the bottle thoroughly to mix the contents. \n\nIs there anything else I can\ \ help you with?" - 'Sure, here are ten facts about wood: 1. Wood is a natural material made from the cells of trees. It is a composite material made up of cellulose, lignin, and hemicellulose. 2. Wood is a renewable resource, meaning that it can be replenished over time. Trees grow back faster than they are cut down, making wood a sustainable material. 3. There are many different types of wood, each with its own unique properties and characteristics. Some of the most common types of wood include oak, pine, maple, and walnut. 4. Wood has been used for centuries as a building material. It is a strong and durable material that can be used for a variety of structures, from houses to bridges to skyscrapers. 5. Wood is also used for furniture making, flooring, and other decorative purposes. It can be stained, painted, or varnished to give it a finished look. 6. Wood is a good insulation material. It is able to trap air inside its cells, making it an effective insulation against cold temperatures. 7. Wood is a good conductor of heat and electricity. This makes it useful for cooking and heating applications, as well as for electrical components like switches and outlets. 8. Wood can be bent, shaped, and carved to create a variety of different designs and shapes. This makes it a popular material for crafting and art projects. 9. Wood is biodegradable, meaning that it can break down over time and return to the earth without harming the environment. 10. Wood is a versatile material that can be used for a wide range of applications. It is aesthetically pleasing, durable, and renewable, making it a popular choice for many different purposes.' - source_sentence: "query: \n\nHuman: 2 Policemen conversation :\nA: How are you ?\n\ B: I am fine thanks god.\nA: What's up tonigt ?\nB: NAME_1 is going to get the\ \ supply. NAME_2 is going to bring NAME_3\n\nQ: What important topics in the conversation\ \ ?\n\nAssistant: " sentences: - 'The important topics in the conversation are: 1. The well-being of person B, who responds that they are fine. 2. The plans for tonight, specifically the involvement of NAME_1, NAME_2, and NAME_3 in some kind of supply arrangement.' - "The answer to this expression is 24. \nHere's how to solve it step by step: \n\ 1. Start with the parentheses: 8 - 2 = 6 \n2. Multiply 3 by 6: 3 * 6 = 18 \n3.\ \ Divide 10 by 2: 10 // 2 = 5 \n4. Add 18 and 5: 18 + 5 = 23 \nTherefore, the\ \ final answer is 23." - The important topics in this conversation are the well-being of the individuals involved, their plans for the evening, and the task of obtaining supplies. - source_sentence: "query: \n\nHuman: 20 English jargon, in order of frequency of\ \ use, that are widely used in the [manufacturing field of environmental home\ \ appliances (water purifier, air purifier, bidet, etc.)] \n\nWrite the English\ \ words that match Jargon and the description for each within 20 words. And make\ \ a table with three columns 1. Jargon(Word) 2. Matching English word(Short form)\ \ 3. Description\n\nAssistant: " sentences: - 'Sure, here''s a table with some common jargon used in the manufacturing field of environmental home appliances: | Jargon(Word) | Matching English Word(Short Form) | Description | | --- | --- | --- | | 1. 제조 (Manufacturing) | MFG | The process of making products on a large scale using machinery. | | 2. 공기 청정기 (Air Purifier) | AP | A device which removes contaminants from the air in a room. | | 3. 수질 정화 (Water Purification) | WP | The process of removing undesirable chemicals, biological contaminants from contaminated water. | | 4. 비데 (Bidet) | BD | A plumbing fixture or type of sink intended for washing the genitalia, perineum, inner buttocks, and anus. | | 5. 에너지 효율 (Energy Efficiency) | EE | Using less energy to provide the same service. | | 6. 필터 (Filter) | FLT | A device used to remove impurities or solid particles from a liquid or gas. | | 7. 헤파 필터 (HEPA Filter) | HF | A type of mechanical air filter; it works by forcing air through a fine mesh. | | 8. 이온 교환 (Ion Exchange) | IE | A water treatment process commonly used for water softening or purification. | | 9. 탄소 필터 (Carbon Filter) | CF | A method of filtering that uses a piece of activated carbon to remove contaminants and impurities. | | 10. 오존 발생기 (Ozone Generator) | OG | A device designed to produce the gas ozone, often used in air purification. | | 11. 초음파 가습기 (Ultrasonic Humidifier) | UH | A device that uses ultrasonic sound vibrations to produce water droplets that quietly exit the humidifier in a cool mist. | | 12. 제품 생명 주기 (Product Lifecycle) | PLC | The process of managing the entire lifecycle of a product from inception, through engineering design and manufacture. | | 13. 품질 관리 (Quality Control) | QC | A process that ensures that a product or service has reached the desired quality. | | 14. 지속 가능 (Sustainability) | SUST | The process of maintaining change in a balanced environment. | | 15. 에너지 별 등급 (Energy Star Rating) | ESR | A standard for energy efficient consumer products. | | 16. 소음 (Noise) | NS | Unwanted or disturbing sound in the environment. | | 17. 에너지 소비 (Energy Consumption) | EC | The amount of energy consumed by business processes or systems. | | 18. 제품 인증 (Product Certification) | PC | The process of certifying that a product has passed performance tests and quality assurance tests. | | 19. 생산 능력 (Production Capacity) | PCAP | The maximum amount that something can be produced. | | 20. 공급망 관리 (Supply Chain Management) | SCM | The management of the flow of goods and services. |' - '1. Pneumonoultramicroscopicsilicovolcanoconiosis: the longest word in the English language, meaning the dust of volcanoes that can cause lung damage. 2. Floccinaucinihilipilification: the act of describing something as worthless. 3. Hippopotomonstrosesquippedaliophobia: the fear of long words. 4. Antidisestablishmentarianism: a political movement in the United Kingdom that opposes the separation of the Church of England from the state. 5. Supercalifragilisticexpialidocious: a made-up word from the movie "Mary Poppins" that means "utterly remarkable" or "fantastic." 6. Llanfairpwllgwyngyllgogerychwyrndrobwllllantysiliogogogoch: the longest place name in Europe, located in Wales, and means "St. Mary''s church in the hollow of the white hazel tree near the rapid whirlpool in the church of St. Tysilio by the red cave." 7. Pneumonoultramicroscopicsilicovolcanoconiosis: the longest word in the English language, meaning the dust of volcanoes that can cause lung damage. 8. Pluperfect: a grammatical term that refers to a past tense that is more past than the simple past. 9. Oppidan: a term used in medieval times to describe a town or city. 10. Discombobulate: to confuse or disconcert someone.' - '| Jargon(Word) | Matching English word(Short form) | Description | | --- | --- | --- | | 제조 | Manufacturing (Mfg) | The process of making products on a large scale using machinery. | | 환경 친화적 | Eco-friendly (Eco) | Products designed to cause minimal harm to the environment. | | 공기 청정기 | Air Purifier (AP) | A device which removes contaminants from the air in a room. | | 정수기 | Water Purifier (WP) | A device that removes impurities by lowering contamination of water. | | 비데 | Bidet (Bd) | A plumbing fixture used for washing the human genitalia. | | 에너지 효율 | Energy Efficiency (EE) | Using less energy to provide the same level of energy service. | | 제품 생명주기 | Product Lifecycle (PLC) | The process a product goes through from conception to withdrawal or end of life. | | 품질 관리 | Quality Control (QC) | A process that ensures products and services meet specified requirements. | | 공급망 관리 | Supply Chain Management (SCM) | The management of the flow of goods and services. | | 재고 관리 | Inventory Management (IM) | Supervising and controlling of the ordering, storage and use of components. | | 생산 계획 | Production Planning (PP) | Planning of production and manufacturing modules in a company. | | 유지 보수 | Maintenance (Mnt) | The process of maintaining or preserving someone or something. | | 제품 설계 | Product Design (PD) | The process of creating a new product to be sold by a business. | | 가격 책정 | Pricing (Pr) | The process whereby a business sets the price at which it will sell its products. | | 판매 예측 | Sales Forecasting (SF) | The process of estimating future sales. | | 시장 조사 | Market Research (MR) | The action or activity of gathering information about consumers'' needs and preferences. | | 소비자 행동 | Consumer Behavior (CB) | The study of how individual customers, groups or organizations select, buy, use, and dispose ideas, goods, and services. | | 경쟁 분석 | Competitive Analysis (CA) | Identifying your competitors and evaluating their strategies. | | 마케팅 전략 | Marketing Strategy (MS) | An overall game plan of a business. | | 제품 개발 | Product Development (PD) | The creation of products with new or different characteristics that offer new or additional benefits to the customer. |' - source_sentence: "query: \n\nHuman: 2 cars take 4 hours to drive from Stockholm\ \ to Copenhagen. Explain how long it takes for 6 cars to drive the same distance.\n\ \nAssistant: " sentences: - 6 cars would take 2 hours and 40 minutes to drive from Stockholm to Copenhagen. This is because the total distance remains the same, but with more cars, the time is divided by the number of cars. So, 4 hours divided by 2 cars is 2 hours, and then divided by 3 (since there are now 6 cars) is 40 minutes. - '1. Promote education: Invest in quality education for all, ensuring that every child has access to a good education regardless of their background or location. 2. Reduce poverty: Implement policies and programs that address poverty and inequality, such as providing social safety nets, job training, and access to basic services. 3. Protect the environment: Encourage sustainable practices and policies that protect the environment, such as reducing carbon emissions, promoting renewable energy sources, and conserving natural resources. 4. Foster equality and inclusivity: Promote equal rights and opportunities for all individuals, regardless of their gender, race, ethnicity, or socioeconomic status. 5. Improve healthcare access: Ensure that everyone has access to affordable and quality healthcare services, including preventive care, mental health support, and essential medications. 6. Support sustainable development: Encourage economic growth that is environmentally sustainable and socially inclusive, focusing on long-term benefits rather than short-term gains. 7. Foster peace and conflict resolution: Promote peaceful dialogue, diplomacy, and mediation to resolve conflicts and prevent violence, both at the national and international levels. 8. Encourage innovation and technology: Invest in research and development, and support the use of technology to address global challenges, improve efficiency, and enhance quality of life. 9. Promote cultural understanding and tolerance: Encourage dialogue and understanding between different cultures, religions, and ethnicities to foster peace, respect, and appreciation for diversity. 10. Empower individuals and communities: Support initiatives that empower individuals and communities to take control of their own lives, such as providing access to microfinance, entrepreneurship training, and community development programs.' - The number of cars doesn't affect the time it takes to travel from Stockholm to Copenhagen, assuming each car is traveling independently and under the same conditions. If it takes one car 4 hours to make the journey, it will also take each of the other cars the same amount of time, 4 hours, to travel the same distance. So, whether you have 2 cars or 6 cars, each car would still take 4 hours to travel from Stockholm to Copenhagen. model-index: - name: SentenceTransformer based on Qwen/Qwen2.5-0.5B-Instruct results: - task: type: information-retrieval name: Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: cosine_accuracy@1 value: 0.34 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.56 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.66 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.34 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.18666666666666668 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14400000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.094 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.17566666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.25733333333333336 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.314 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.37733333333333335 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.33221935140013725 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4347142857142858 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2716328809847145 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: cosine_accuracy@1 value: 0.62 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.86 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.92 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.62 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.5333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.452 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.35600000000000004 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.05514928831379628 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.12824676654105222 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.1803700603108436 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.24696556231208447 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4605501396183746 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7195555555555555 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3188311328922227 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: cosine_accuracy@1 value: 0.5 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.76 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.86 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.15600000000000003 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.088 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.48 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.68 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.74 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.83 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6622622205864791 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.621 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6078403686230498 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: cosine_accuracy@1 value: 0.2 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.46 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.48 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.6 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.18666666666666668 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.124 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.088 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.10833333333333334 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.29285714285714287 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.32685714285714285 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4266587301587302 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.30836658504994896 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3335 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2463932547900683 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: cosine_accuracy@1 value: 0.56 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.66 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.74 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.86 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.56 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.204 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.13 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.28 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.45 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.51 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.65 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5551540336286506 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6367380952380952 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.48003177140687037 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: cosine_accuracy@1 value: 0.26 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.44 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.68 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.26 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.14666666666666664 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.10000000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.068 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.26 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.44 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.5 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.68 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4463469261614279 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3753015873015873 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.38958758121329945 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: cosine_accuracy@1 value: 0.34 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.54 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.54 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.54 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.34 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.33333333333333326 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.284 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.222 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.020833049564977436 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.06222722130306728 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.07578678014791711 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.10345061072897106 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2714354367850273 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4266666666666666 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.10782010581556394 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: cosine_accuracy@1 value: 0.24 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.54 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.66 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.24 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.18 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.12 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.22 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.51 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.55 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.62 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.44040013881094764 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3960238095238095 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3890389926031548 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: cosine_accuracy@1 value: 0.58 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.82 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.86 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.58 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2866666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18799999999999997 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10799999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.524 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7306666666666666 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7613333333333333 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8286666666666668 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7083837412625251 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6884444444444445 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6681604625222274 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: cosine_accuracy@1 value: 0.36 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.68 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.36 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.21199999999999997 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.142 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.07200000000000001 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.17366666666666664 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.21866666666666665 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.29366666666666663 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2888407480508624 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4935555555555555 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.21255218678856397 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: cosine_accuracy@1 value: 0.1 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.38 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.48 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.62 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.1 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.12666666666666665 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09600000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.06200000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.38 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.48 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.62 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3529452727706292 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.26869047619047615 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2836337995210993 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: cosine_accuracy@1 value: 0.34 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.58 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.62 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.68 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.34 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.132 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07600000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.305 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.55 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.585 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.665 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4924161250817683 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4540238095238096 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.43656789834287063 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: cosine_accuracy@1 value: 0.5102040816326531 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7551020408163265 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8571428571428571 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9795918367346939 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5102040816326531 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.4625850340136054 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.42040816326530617 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.3653061224489796 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.03700006081489567 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.09740661891768902 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.144055646079894 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.24256181199166754 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4137614534395401 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6687074829931973 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.31389919569146446 name: Cosine Map@100 - task: type: nano-beir name: Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: cosine_accuracy@1 value: 0.38078492935635794 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5980847723704866 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6536263736263735 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7476609105180534 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.38078492935635794 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26583987441130297 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.2024929356357928 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.14379277864992152 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.20292172297643613 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.3655695704835091 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.41431304841506145 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5064848755275477 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4410063209727938 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5013016745159603 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.36353766393808995 name: Cosine Map@100 --- # SentenceTransformer based on Qwen/Qwen2.5-0.5B-Instruct This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). It maps sentences & paragraphs to a 896-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:** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) - **Maximum Sequence Length:** 1024 tokens - **Output Dimensionality:** 896 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': 1024, 'do_lower_case': False}) with Transformer model: Qwen2Model (1): Pooling({'word_embedding_dimension': 896, '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}) ) ``` ## 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("AlexWortega/qwen_emb_6k") # Run inference sentences = [ 'query: \n\nHuman: 2 cars take 4 hours to drive from Stockholm to Copenhagen. Explain how long it takes for 6 cars to drive the same distance.\n\nAssistant: ', "The number of cars doesn't affect the time it takes to travel from Stockholm to Copenhagen, assuming each car is traveling independently and under the same conditions. If it takes one car 4 hours to make the journey, it will also take each of the other cars the same amount of time, 4 hours, to travel the same distance. So, whether you have 2 cars or 6 cars, each car would still take 4 hours to travel from Stockholm to Copenhagen.", '6 cars would take 2 hours and 40 minutes to drive from Stockholm to Copenhagen. This is because the total distance remains the same, but with more cars, the time is divided by the number of cars. So, 4 hours divided by 2 cars is 2 hours, and then divided by 3 (since there are now 6 cars) is 40 minutes.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 896] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | |:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------| | cosine_accuracy@1 | 0.34 | 0.62 | 0.5 | 0.2 | 0.56 | 0.26 | 0.34 | 0.24 | 0.58 | 0.36 | 0.1 | 0.34 | 0.5102 | | cosine_accuracy@3 | 0.5 | 0.82 | 0.7 | 0.46 | 0.66 | 0.44 | 0.54 | 0.54 | 0.8 | 0.6 | 0.38 | 0.58 | 0.7551 | | cosine_accuracy@5 | 0.56 | 0.86 | 0.76 | 0.48 | 0.74 | 0.5 | 0.54 | 0.6 | 0.82 | 0.68 | 0.48 | 0.62 | 0.8571 | | cosine_accuracy@10 | 0.66 | 0.92 | 0.86 | 0.6 | 0.86 | 0.68 | 0.54 | 0.66 | 0.86 | 0.8 | 0.62 | 0.68 | 0.9796 | | cosine_precision@1 | 0.34 | 0.62 | 0.5 | 0.2 | 0.56 | 0.26 | 0.34 | 0.24 | 0.58 | 0.36 | 0.1 | 0.34 | 0.5102 | | cosine_precision@3 | 0.1867 | 0.5333 | 0.2333 | 0.1867 | 0.3 | 0.1467 | 0.3333 | 0.18 | 0.2867 | 0.28 | 0.1267 | 0.2 | 0.4626 | | cosine_precision@5 | 0.144 | 0.452 | 0.156 | 0.124 | 0.204 | 0.1 | 0.284 | 0.12 | 0.188 | 0.212 | 0.096 | 0.132 | 0.4204 | | cosine_precision@10 | 0.094 | 0.356 | 0.088 | 0.088 | 0.13 | 0.068 | 0.222 | 0.07 | 0.108 | 0.142 | 0.062 | 0.076 | 0.3653 | | cosine_recall@1 | 0.1757 | 0.0551 | 0.48 | 0.1083 | 0.28 | 0.26 | 0.0208 | 0.22 | 0.524 | 0.072 | 0.1 | 0.305 | 0.037 | | cosine_recall@3 | 0.2573 | 0.1282 | 0.68 | 0.2929 | 0.45 | 0.44 | 0.0622 | 0.51 | 0.7307 | 0.1737 | 0.38 | 0.55 | 0.0974 | | cosine_recall@5 | 0.314 | 0.1804 | 0.74 | 0.3269 | 0.51 | 0.5 | 0.0758 | 0.55 | 0.7613 | 0.2187 | 0.48 | 0.585 | 0.1441 | | cosine_recall@10 | 0.3773 | 0.247 | 0.83 | 0.4267 | 0.65 | 0.68 | 0.1035 | 0.62 | 0.8287 | 0.2937 | 0.62 | 0.665 | 0.2426 | | **cosine_ndcg@10** | **0.3322** | **0.4606** | **0.6623** | **0.3084** | **0.5552** | **0.4463** | **0.2714** | **0.4404** | **0.7084** | **0.2888** | **0.3529** | **0.4924** | **0.4138** | | cosine_mrr@10 | 0.4347 | 0.7196 | 0.621 | 0.3335 | 0.6367 | 0.3753 | 0.4267 | 0.396 | 0.6884 | 0.4936 | 0.2687 | 0.454 | 0.6687 | | cosine_map@100 | 0.2716 | 0.3188 | 0.6078 | 0.2464 | 0.48 | 0.3896 | 0.1078 | 0.389 | 0.6682 | 0.2126 | 0.2836 | 0.4366 | 0.3139 | #### Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [NanoBEIREvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.3808 | | cosine_accuracy@3 | 0.5981 | | cosine_accuracy@5 | 0.6536 | | cosine_accuracy@10 | 0.7477 | | cosine_precision@1 | 0.3808 | | cosine_precision@3 | 0.2658 | | cosine_precision@5 | 0.2025 | | cosine_precision@10 | 0.1438 | | cosine_recall@1 | 0.2029 | | cosine_recall@3 | 0.3656 | | cosine_recall@5 | 0.4143 | | cosine_recall@10 | 0.5065 | | **cosine_ndcg@10** | **0.441** | | cosine_mrr@10 | 0.5013 | | cosine_map@100 | 0.3635 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 1,580,101 training samples * Columns: query, response, and negative * Approximate statistics based on the first 1000 samples: | | query | response | negative | |:--------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | query | response | negative | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | query:

Human: 0.002 = 1000
1 = x?

Assistant: | To find the value of x, we can set up a proportion using the given information:

0.002/1000 = 1/x

To solve for x, we can cross multiply:

0.002 * x = 1000 * 1

0.002x = 1000

Dividing both sides by 0.002:

x = 1000 / 0.002

x = 500,000

Therefore, 1 is equal to 500,000 in this proportion. | I apologize, but the equation you provided is not correct.

0.002 is equal to 0.002, not 1000.

1 is equal to x, not a number.

Can you please provide a correct equation or problem for me to solve? | | query:

Human: 0:00
what's going on guys it's NAME_1 from
0:01
NAME_2 and today we're going to
0:03
talk about cns fatigue
0:05
so going over uh cns fatigue central
0:07
nervous system fatigue it's something
0:08
that people talk about and bring up a
0:10
lot
0:10
there's a bit of science to it and
0:12
there's also a lot of bro
0:13
culture myth to it and i want to get
0:15
into that and i'm i'm going to talk
0:17
really quick about where it comes from
0:19
why we talk about it this way and then
0:21
how it kind of gets used and misused
0:23
to describe a certain training
0:25
phenomenon that we all experience
0:27
so i'm going to start out by kind of
0:29
fleshing out what we're talking about or
0:31
how it kind of originally became
0:34
discussed about so we observe a few
0:35
things when it comes to weight training
0:37
specifically with lifting heavy
0:38
the first thing we observe is that true
0:40
maximal attempts at 90
0:42
and up rpe nine to ten so that's really
0:45
hard tri... | Hello! It seems like you're sharing a transcript about Central Nervous System (CNS) fatigue, particularly in relation to weight training. CNS fatigue is a topic that's often discussed in the context of high-intensity workouts, such as weightlifting.

The transcript you've shared discusses the concept of CNS fatigue, its origins, and how it's often misused to describe certain training phenomena. It also talks about the effects of lifting heavy weights at maximum capacity in every session, which can lead to short-term regression. This is a common experience among weightlifters who push their limits consistently without giving their bodies adequate time to recover.

The speaker also mentions the common practice of spacing out heavy lifting sessions or incorporating deload periods to prevent this regression. The time between the last heavy attempt and a contest day is also noted to be longer, presumably to allow for adequate recovery and prevent CNS fatigue.

If you have any specific ques... | 0:00
in the past
0:00
with the same lift
0:00
and the same weight
0:00
and the same load
0:00
then
0:00
if you have a deload day and you come back
0:00
and you're feeling like you can handle
0:00
heavier loads
0:00
with a little bit more energy
0:00
then the load is typically a little bit
0:01
lighter than the previous day's load
0:02
and the time between contest day and the
0:03
last heavy attempt
0:03
is typically a lot shorter
0:04
than the time between the previous day's
0:05
heavy attempt and the contest day
0:06
and that's a general observation that has
0:08
also been seen in other strength training
0:09
styles
0:10
and it's something that's been uh
0:12
accounted for in the wisdom of
0:13
trainers and coaches over the years
0:14
and it's a good idea to keep in mind when
0:16
you're lifting heavy or you're training in
0:17
a deloads schedule
0:18
and you have a lot of energy and you're
0:20
able to handle heavier loads
0:21
the load is typically a little bit
0:22
lighter than... | | query:

Human: 0:00
what's going on guys it's NAME_1 from
0:01
NAME_2 and today we're going to
0:03
talk about cns fatigue
0:05
so going over uh cns fatigue central
0:07
nervous system fatigue it's something
0:08
that people talk about and bring up a
0:10
lot
0:10
there's a bit of science to it and
0:12
there's also a lot of bro
0:13
culture myth to it and i want to get
0:15
into that and i'm i'm going to talk
0:17
really quick about where it comes from
0:19
why we talk about it this way and then
0:21
how it kind of gets used and misused
0:23
to describe a certain training
0:25
phenomenon that we all experience
0:27
so i'm going to start out by kind of
0:29
fleshing out what we're talking about or
0:31
how it kind of originally became
0:34
discussed about so we observe a few
0:35
things when it comes to weight training
0:37
specifically with lifting heavy
0:38
the first thing we observe is that true
0:40
maximal attempts at 90
0:42
and up rpe nine to ten so that's really
0:45
hard tri... | 0:00
Hey there! Today we're going to talk about CNS fatigue, or central nervous system fatigue. This is a term that is often used in the fitness community, but there is some confusion and misinformation surrounding it. So let's dive in and discuss what it really means and how it affects our training.

First, let's define what we mean by CNS fatigue. This is the idea that our central nervous system, which controls our brain and spinal cord, can become fatigued from intense physical activity. This can manifest as a decrease in performance, strength, and coordination.

One of the main observations that has led to the concept of CNS fatigue is that when we lift heavy weights at a high intensity, such as a 9 or 10 on the RPE scale, we tend to see short-term regression in our performance. This means that if we continue to lift at this intensity every session, we may eventually see a decrease in our strength and ability to handle heavy loads.

Additionally, we also see that the time between a... | Hi, I'm an AI language model. How can I assist you today? | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `gradient_accumulation_steps`: 32 - `learning_rate`: 2e-05 - `max_grad_norm`: 0.4 - `num_train_epochs`: 1 - `warmup_ratio`: 0.4 - `bf16`: True - `prompts`: {'query': 'query: ', 'answer': 'document: '} - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 8 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 32 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 0.4 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.4 - `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`: True - `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`: False - `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`: {'query': 'query: ', 'answer': 'document: '} - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 | |:------:|:----:|:-------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:| | 0.0016 | 10 | 2.3323 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0032 | 20 | 2.2923 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0049 | 30 | 2.2011 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0065 | 40 | 2.4198 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0081 | 50 | 2.4304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0097 | 60 | 2.35 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0113 | 70 | 2.4141 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0130 | 80 | 2.4043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0146 | 90 | 2.2222 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0162 | 100 | 2.4379 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0178 | 110 | 2.4722 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0194 | 120 | 2.9719 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0211 | 130 | 2.5376 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0227 | 140 | 2.4272 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0243 | 150 | 2.1056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0259 | 160 | 2.1292 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0275 | 170 | 1.9443 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0292 | 180 | 1.8512 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0308 | 190 | 1.7141 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0324 | 200 | 1.8382 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0340 | 210 | 1.7891 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0356 | 220 | 1.6014 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0373 | 230 | 1.5022 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0389 | 240 | 1.412 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0405 | 250 | 1.3756 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0421 | 260 | 2.6414 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0437 | 270 | 1.6938 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0454 | 280 | 2.953 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0470 | 290 | 2.9116 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0486 | 300 | 1.273 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0502 | 310 | 1.4269 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0518 | 320 | 1.5998 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0535 | 330 | 1.5939 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0551 | 340 | 1.4772 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0567 | 350 | 1.162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0583 | 360 | 1.4587 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0599 | 370 | 1.5296 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0616 | 380 | 1.6156 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0632 | 390 | 1.3018 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0648 | 400 | 1.5415 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0664 | 410 | 1.5115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0680 | 420 | 2.435 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0697 | 430 | 1.7281 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0713 | 440 | 2.0099 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0729 | 450 | 1.2842 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0745 | 460 | 1.4389 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0761 | 470 | 1.3 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0778 | 480 | 1.3392 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0794 | 490 | 1.0975 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0810 | 500 | 1.2641 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0826 | 510 | 1.2011 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0842 | 520 | 1.3416 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0859 | 530 | 1.3424 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0875 | 540 | 1.29 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0891 | 550 | 1.383 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0907 | 560 | 0.971 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0923 | 570 | 1.0089 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0940 | 580 | 0.974 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0956 | 590 | 0.9482 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0972 | 600 | 1.2337 | 0.1808 | 0.1027 | 0.2711 | 0.0854 | 0.1819 | 0.0510 | 0.0569 | 0.0039 | 0.6725 | 0.0714 | 0.3804 | 0.3058 | 0.3047 | 0.2053 | | 0.0988 | 610 | 1.1811 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1004 | 620 | 1.0868 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1021 | 630 | 1.1908 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1037 | 640 | 1.0508 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1053 | 650 | 1.097 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1069 | 660 | 0.9266 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1085 | 670 | 1.2172 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1102 | 680 | 1.1388 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1118 | 690 | 1.1859 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1134 | 700 | 0.8618 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1150 | 710 | 1.0641 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1167 | 720 | 1.1092 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1183 | 730 | 0.7565 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1199 | 740 | 0.7026 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1215 | 750 | 1.0661 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1231 | 760 | 1.3258 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1248 | 770 | 1.5056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1264 | 780 | 1.0812 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1280 | 790 | 1.0357 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1296 | 800 | 1.2638 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1312 | 810 | 1.7064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1329 | 820 | 1.4948 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1345 | 830 | 1.0338 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1361 | 840 | 0.9158 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1377 | 850 | 0.9544 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1393 | 860 | 1.8469 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1410 | 870 | 1.3733 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1426 | 880 | 0.8882 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1442 | 890 | 1.0591 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1458 | 900 | 1.0214 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1474 | 910 | 1.0111 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1491 | 920 | 0.783 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1507 | 930 | 0.9901 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1523 | 940 | 1.0508 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1539 | 950 | 1.6198 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1555 | 960 | 1.4054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1572 | 970 | 2.0936 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1588 | 980 | 2.0536 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1604 | 990 | 1.595 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1620 | 1000 | 1.0133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1636 | 1010 | 0.8841 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1653 | 1020 | 0.8795 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1669 | 1030 | 0.821 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1685 | 1040 | 0.9551 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1701 | 1050 | 0.8831 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1717 | 1060 | 0.8877 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1734 | 1070 | 0.9293 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1750 | 1080 | 1.1628 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1766 | 1090 | 1.0334 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1782 | 1100 | 0.9041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1798 | 1110 | 0.8715 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1815 | 1120 | 0.6835 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1831 | 1130 | 0.9067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1847 | 1140 | 0.9845 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1863 | 1150 | 0.9605 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1879 | 1160 | 0.9137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1896 | 1170 | 0.8297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1912 | 1180 | 0.9854 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1928 | 1190 | 1.0456 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1944 | 1200 | 0.8366 | 0.2868 | 0.2325 | 0.5528 | 0.1413 | 0.2869 | 0.0953 | 0.1302 | 0.0794 | 0.7002 | 0.1748 | 0.4492 | 0.3688 | 0.3810 | 0.2984 | | 0.1960 | 1210 | 0.7654 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1977 | 1220 | 0.977 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1993 | 1230 | 0.64 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2009 | 1240 | 1.3624 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2025 | 1250 | 1.2971 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2041 | 1260 | 1.1123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2058 | 1270 | 0.9836 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2074 | 1280 | 0.7819 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2090 | 1290 | 0.8977 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2106 | 1300 | 0.9156 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2122 | 1310 | 0.8029 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2139 | 1320 | 1.1394 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2155 | 1330 | 0.9088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2171 | 1340 | 0.8174 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2187 | 1350 | 1.3159 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2203 | 1360 | 1.0255 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2220 | 1370 | 1.1159 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2236 | 1380 | 0.9766 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2252 | 1390 | 0.9058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2268 | 1400 | 0.88 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2284 | 1410 | 0.8224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2301 | 1420 | 0.6394 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2317 | 1430 | 0.7517 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2333 | 1440 | 0.8308 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2349 | 1450 | 0.811 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2365 | 1460 | 0.8963 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2382 | 1470 | 0.9781 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2398 | 1480 | 0.8422 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2414 | 1490 | 0.8144 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2430 | 1500 | 0.7655 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2446 | 1510 | 0.6322 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2463 | 1520 | 0.6661 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2479 | 1530 | 0.7723 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2495 | 1540 | 0.7734 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2511 | 1550 | 0.8246 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2527 | 1560 | 0.7604 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2544 | 1570 | 0.8196 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2560 | 1580 | 0.7278 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2576 | 1590 | 0.7076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2592 | 1600 | 0.6913 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2608 | 1610 | 0.6974 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2625 | 1620 | 0.7015 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2641 | 1630 | 0.677 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2657 | 1640 | 0.7185 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2673 | 1650 | 0.665 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2689 | 1660 | 0.7026 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2706 | 1670 | 0.6374 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2722 | 1680 | 0.652 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2738 | 1690 | 0.7426 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2754 | 1700 | 0.6444 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2770 | 1710 | 0.663 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2787 | 1720 | 0.6476 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2803 | 1730 | 0.6857 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2819 | 1740 | 0.6229 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2835 | 1750 | 0.5756 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2851 | 1760 | 0.6839 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2868 | 1770 | 0.8267 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2884 | 1780 | 0.8146 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2900 | 1790 | 0.7093 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2916 | 1800 | 0.7307 | 0.2597 | 0.2742 | 0.6859 | 0.2218 | 0.4912 | 0.2921 | 0.1728 | 0.3219 | 0.7381 | 0.2529 | 0.4898 | 0.4819 | 0.5037 | 0.3989 | | 0.2932 | 1810 | 0.606 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2949 | 1820 | 0.6338 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2965 | 1830 | 0.5849 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2981 | 1840 | 0.699 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2997 | 1850 | 0.6164 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3013 | 1860 | 0.574 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3030 | 1870 | 0.5819 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3046 | 1880 | 0.5177 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3062 | 1890 | 0.6006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3078 | 1900 | 0.6981 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3094 | 1910 | 0.885 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3111 | 1920 | 1.2742 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3127 | 1930 | 0.7133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3143 | 1940 | 0.7271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3159 | 1950 | 1.3258 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3175 | 1960 | 1.2689 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3192 | 1970 | 0.6723 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3208 | 1980 | 0.3596 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3224 | 1990 | 0.4078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3240 | 2000 | 0.287 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3256 | 2010 | 0.2375 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3273 | 2020 | 0.2259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3289 | 2030 | 0.3889 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3305 | 2040 | 0.7391 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3321 | 2050 | 0.5417 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3338 | 2060 | 0.4933 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3354 | 2070 | 0.426 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3370 | 2080 | 0.4222 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3386 | 2090 | 0.4132 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3402 | 2100 | 0.4133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3419 | 2110 | 0.3989 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3435 | 2120 | 0.4035 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3451 | 2130 | 0.3804 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3467 | 2140 | 0.3597 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3483 | 2150 | 0.3793 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3500 | 2160 | 0.3633 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3516 | 2170 | 0.3504 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3532 | 2180 | 0.3475 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3548 | 2190 | 0.3467 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3564 | 2200 | 0.3412 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3581 | 2210 | 0.3665 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3597 | 2220 | 0.3585 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3613 | 2230 | 0.3335 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3629 | 2240 | 0.329 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3645 | 2250 | 0.3193 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3662 | 2260 | 0.3256 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3678 | 2270 | 0.325 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3694 | 2280 | 0.3312 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3710 | 2290 | 0.3323 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3726 | 2300 | 0.3192 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3743 | 2310 | 0.3366 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3759 | 2320 | 0.3247 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3775 | 2330 | 0.3207 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3791 | 2340 | 0.3238 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3807 | 2350 | 0.3217 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3824 | 2360 | 0.336 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3840 | 2370 | 0.3043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3856 | 2380 | 0.3043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3872 | 2390 | 0.3193 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3888 | 2400 | 0.3145 | 0.2338 | 0.4041 | 0.7329 | 0.2612 | 0.4511 | 0.3624 | 0.2742 | 0.3903 | 0.2020 | 0.2560 | 0.3127 | 0.5038 | 0.4262 | 0.3701 | | 0.3905 | 2410 | 0.319 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3921 | 2420 | 0.3097 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3937 | 2430 | 0.2817 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3953 | 2440 | 0.3168 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3969 | 2450 | 0.2941 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3986 | 2460 | 0.2902 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4002 | 2470 | 0.3095 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4018 | 2480 | 0.3149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4034 | 2490 | 0.2949 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4050 | 2500 | 0.3057 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4067 | 2510 | 0.2982 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4083 | 2520 | 0.3064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4099 | 2530 | 0.3169 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4115 | 2540 | 0.2922 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4131 | 2550 | 0.2999 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4148 | 2560 | 0.2803 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4164 | 2570 | 0.3118 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4180 | 2580 | 0.309 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4196 | 2590 | 0.2894 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4212 | 2600 | 0.3126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4229 | 2610 | 0.2949 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4245 | 2620 | 0.3204 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4261 | 2630 | 0.2868 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4277 | 2640 | 0.3168 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4293 | 2650 | 0.3245 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4310 | 2660 | 0.316 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4326 | 2670 | 0.2822 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4342 | 2680 | 0.3046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4358 | 2690 | 0.2908 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4374 | 2700 | 0.2542 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4391 | 2710 | 0.3079 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4407 | 2720 | 0.2821 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4423 | 2730 | 0.2863 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4439 | 2740 | 0.2889 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4455 | 2750 | 0.282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4472 | 2760 | 0.29 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4488 | 2770 | 0.2973 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4504 | 2780 | 0.3018 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4520 | 2790 | 0.2938 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4536 | 2800 | 0.2835 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4553 | 2810 | 0.2773 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4569 | 2820 | 0.2867 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4585 | 2830 | 0.2954 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4601 | 2840 | 0.3035 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4617 | 2850 | 0.2905 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4634 | 2860 | 0.2821 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4650 | 2870 | 0.2815 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4666 | 2880 | 0.298 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4682 | 2890 | 0.2905 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4698 | 2900 | 0.2821 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4715 | 2910 | 0.2904 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4731 | 2920 | 0.2992 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4747 | 2930 | 0.2834 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4763 | 2940 | 0.2855 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4779 | 2950 | 0.2775 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4796 | 2960 | 0.2994 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4812 | 2970 | 0.2939 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4828 | 2980 | 0.2999 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4844 | 2990 | 0.2935 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4860 | 3000 | 0.2714 | 0.2471 | 0.3962 | 0.7912 | 0.2469 | 0.4488 | 0.3739 | 0.2677 | 0.3976 | 0.1890 | 0.2485 | 0.2962 | 0.4538 | 0.4259 | 0.3679 | | 0.4877 | 3010 | 0.2819 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4893 | 3020 | 0.2679 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4909 | 3030 | 0.2789 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4925 | 3040 | 0.2865 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4941 | 3050 | 0.2852 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4958 | 3060 | 0.2706 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4974 | 3070 | 0.2935 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4990 | 3080 | 0.272 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5006 | 3090 | 0.2915 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5022 | 3100 | 0.2826 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5039 | 3110 | 0.2652 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5055 | 3120 | 0.2887 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5071 | 3130 | 0.2613 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5087 | 3140 | 0.283 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5103 | 3150 | 0.2945 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5120 | 3160 | 0.2877 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5136 | 3170 | 0.2889 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5152 | 3180 | 0.268 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5168 | 3190 | 0.2911 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5184 | 3200 | 0.2785 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5201 | 3210 | 0.2711 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5217 | 3220 | 0.2911 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5233 | 3230 | 0.2649 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5249 | 3240 | 0.3054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5265 | 3250 | 0.2531 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5282 | 3260 | 0.2767 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5298 | 3270 | 0.2853 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5314 | 3280 | 0.2731 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5330 | 3290 | 0.2776 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5346 | 3300 | 0.2725 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5363 | 3310 | 0.281 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5379 | 3320 | 0.2666 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5395 | 3330 | 0.2654 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5411 | 3340 | 0.2909 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5427 | 3350 | 0.2598 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5444 | 3360 | 0.2837 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5460 | 3370 | 0.2855 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5476 | 3380 | 0.2601 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5492 | 3390 | 0.268 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5508 | 3400 | 0.2681 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5525 | 3410 | 0.2663 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5541 | 3420 | 0.2837 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5557 | 3430 | 0.259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5573 | 3440 | 0.2622 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5590 | 3450 | 0.2825 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5606 | 3460 | 0.2921 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5622 | 3470 | 0.2721 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5638 | 3480 | 0.2797 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5654 | 3490 | 0.2899 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5671 | 3500 | 0.2745 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5687 | 3510 | 0.2665 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5703 | 3520 | 0.2908 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5719 | 3530 | 0.2492 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5735 | 3540 | 0.2562 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5752 | 3550 | 0.2616 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5768 | 3560 | 0.2775 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5784 | 3570 | 0.2736 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5800 | 3580 | 0.2862 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5816 | 3590 | 0.2582 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5833 | 3600 | 0.2547 | 0.2371 | 0.3994 | 0.7786 | 0.2418 | 0.4072 | 0.3469 | 0.2615 | 0.4070 | 0.1551 | 0.2294 | 0.2533 | 0.4270 | 0.4161 | 0.3508 | | 0.5849 | 3610 | 0.2822 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5865 | 3620 | 0.2622 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5881 | 3630 | 0.2691 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5897 | 3640 | 0.2585 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5914 | 3650 | 0.2927 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5930 | 3660 | 0.2593 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5946 | 3670 | 0.2501 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5962 | 3680 | 0.2796 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5978 | 3690 | 0.2622 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5995 | 3700 | 0.2508 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6011 | 3710 | 0.2891 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6027 | 3720 | 0.274 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6043 | 3730 | 0.2769 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6059 | 3740 | 0.2617 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6076 | 3750 | 0.2557 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6092 | 3760 | 0.2634 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6108 | 3770 | 0.262 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6124 | 3780 | 0.2696 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6140 | 3790 | 0.2608 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6157 | 3800 | 0.2592 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6173 | 3810 | 0.2757 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6189 | 3820 | 0.2672 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6205 | 3830 | 0.2523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6221 | 3840 | 0.2775 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6238 | 3850 | 0.2621 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6254 | 3860 | 0.275 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6270 | 3870 | 0.2727 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6286 | 3880 | 0.2709 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6302 | 3890 | 0.2749 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6319 | 3900 | 0.2844 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6335 | 3910 | 0.2713 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6351 | 3920 | 0.2711 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6367 | 3930 | 0.2523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6383 | 3940 | 0.2789 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6400 | 3950 | 0.2639 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6416 | 3960 | 0.2609 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6432 | 3970 | 0.2699 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6448 | 3980 | 0.2614 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6464 | 3990 | 0.2567 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6481 | 4000 | 1.2987 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6497 | 4010 | 1.4783 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6513 | 4020 | 1.7162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6529 | 4030 | 1.2907 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6545 | 4040 | 1.2583 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6562 | 4050 | 1.0498 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6578 | 4060 | 1.8076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6594 | 4070 | 1.215 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6610 | 4080 | 1.1462 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6626 | 4090 | 0.9511 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6643 | 4100 | 0.6151 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6659 | 4110 | 0.7482 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6675 | 4120 | 0.8572 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6691 | 4130 | 0.7722 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6707 | 4140 | 0.6085 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6724 | 4150 | 0.6644 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6740 | 4160 | 0.6423 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6756 | 4170 | 0.7482 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6772 | 4180 | 0.9649 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6788 | 4190 | 0.9205 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6805 | 4200 | 0.7746 | 0.2822 | 0.4484 | 0.7622 | 0.2944 | 0.5133 | 0.4592 | 0.2717 | 0.4451 | 0.3682 | 0.2594 | 0.2342 | 0.5123 | 0.4209 | 0.4055 | | 0.6821 | 4210 | 0.5752 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6837 | 4220 | 0.6221 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6853 | 4230 | 0.526 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6869 | 4240 | 0.455 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6886 | 4250 | 0.4964 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6902 | 4260 | 0.935 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6918 | 4270 | 0.6227 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6934 | 4280 | 0.5594 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6950 | 4290 | 0.496 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6967 | 4300 | 0.5907 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6983 | 4310 | 0.5163 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6999 | 4320 | 0.468 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7015 | 4330 | 0.5214 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7031 | 4340 | 0.625 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7048 | 4350 | 0.593 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7064 | 4360 | 0.5852 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7080 | 4370 | 0.5648 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7096 | 4380 | 0.6791 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7112 | 4390 | 0.7008 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7129 | 4400 | 0.6731 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7145 | 4410 | 0.654 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7161 | 4420 | 0.6135 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7177 | 4430 | 0.6206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7193 | 4440 | 0.5056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7210 | 4450 | 0.5201 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7226 | 4460 | 0.5894 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7242 | 4470 | 0.5571 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7258 | 4480 | 0.5979 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7274 | 4490 | 0.6202 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7291 | 4500 | 0.5544 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7307 | 4510 | 0.6122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7323 | 4520 | 0.5631 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7339 | 4530 | 0.5284 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7355 | 4540 | 0.6899 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7372 | 4550 | 0.5838 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7388 | 4560 | 0.6806 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7404 | 4570 | 0.5413 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7420 | 4580 | 0.5956 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7436 | 4590 | 0.6044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7453 | 4600 | 0.5857 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7469 | 4610 | 0.5664 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7485 | 4620 | 0.5097 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7501 | 4630 | 0.4912 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7517 | 4640 | 0.6049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7534 | 4650 | 0.5389 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7550 | 4660 | 0.555 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7566 | 4670 | 0.6238 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7582 | 4680 | 0.6447 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7598 | 4690 | 0.5606 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7615 | 4700 | 0.5165 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7631 | 4710 | 0.5839 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7647 | 4720 | 0.5189 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7663 | 4730 | 0.584 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7679 | 4740 | 0.5744 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7696 | 4750 | 0.5351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7712 | 4760 | 0.5953 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7728 | 4770 | 0.5725 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7744 | 4780 | 0.5688 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7761 | 4790 | 0.5004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7777 | 4800 | 0.5378 | 0.3514 | 0.4652 | 0.7429 | 0.3103 | 0.5406 | 0.4361 | 0.2797 | 0.4267 | 0.3843 | 0.2727 | 0.3474 | 0.5341 | 0.4249 | 0.4243 | | 0.7793 | 4810 | 0.5244 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7809 | 4820 | 0.6241 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7825 | 4830 | 0.4844 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7842 | 4840 | 0.4401 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7858 | 4850 | 0.499 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7874 | 4860 | 0.5326 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7890 | 4870 | 0.4981 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7906 | 4880 | 0.5659 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7923 | 4890 | 0.5364 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7939 | 4900 | 0.5479 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7955 | 4910 | 0.4653 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7971 | 4920 | 0.5005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7987 | 4930 | 0.5624 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8004 | 4940 | 0.4399 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8020 | 4950 | 0.4859 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8036 | 4960 | 0.5087 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8052 | 4970 | 0.511 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8068 | 4980 | 0.5819 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8085 | 4990 | 0.4462 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8101 | 5000 | 0.4882 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8117 | 5010 | 0.5306 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8133 | 5020 | 0.507 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8149 | 5030 | 0.4471 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8166 | 5040 | 0.5333 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8182 | 5050 | 0.4353 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8198 | 5060 | 0.5615 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8214 | 5070 | 0.5629 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8230 | 5080 | 0.5131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8247 | 5090 | 0.4789 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8263 | 5100 | 0.4934 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8279 | 5110 | 0.5285 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8295 | 5120 | 0.4414 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8311 | 5130 | 0.5262 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8328 | 5140 | 0.4645 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8344 | 5150 | 0.4532 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8360 | 5160 | 0.4421 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8376 | 5170 | 0.4375 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8392 | 5180 | 0.5234 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8409 | 5190 | 0.4803 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8425 | 5200 | 0.4872 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8441 | 5210 | 0.451 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8457 | 5220 | 0.4388 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8473 | 5230 | 0.5182 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8490 | 5240 | 0.5302 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8506 | 5250 | 0.4643 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8522 | 5260 | 0.5581 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8538 | 5270 | 0.4643 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8554 | 5280 | 0.5288 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8571 | 5290 | 0.4133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8587 | 5300 | 0.4664 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8603 | 5310 | 0.4814 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8619 | 5320 | 0.5256 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8635 | 5330 | 0.4904 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8652 | 5340 | 0.4495 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8668 | 5350 | 0.5389 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8684 | 5360 | 0.4497 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8700 | 5370 | 0.4776 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8716 | 5380 | 0.5441 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8733 | 5390 | 0.4473 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8749 | 5400 | 0.5598 | 0.3381 | 0.4668 | 0.7306 | 0.3137 | 0.5415 | 0.4550 | 0.2840 | 0.4169 | 0.4719 | 0.2735 | 0.3582 | 0.5311 | 0.4076 | 0.4299 | | 0.8765 | 5410 | 0.4726 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8781 | 5420 | 0.4966 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8797 | 5430 | 0.4644 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8814 | 5440 | 0.4084 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8830 | 5450 | 0.4913 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8846 | 5460 | 0.5708 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8862 | 5470 | 0.5577 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8878 | 5480 | 0.4839 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8895 | 5490 | 0.461 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8911 | 5500 | 0.4799 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8927 | 5510 | 0.5608 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8943 | 5520 | 0.4625 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8959 | 5530 | 0.4765 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8976 | 5540 | 0.4348 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8992 | 5550 | 0.4424 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9008 | 5560 | 0.4147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9024 | 5570 | 0.433 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9040 | 5580 | 0.4628 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9057 | 5590 | 0.4466 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9073 | 5600 | 0.4563 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9089 | 5610 | 0.4508 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9105 | 5620 | 0.4619 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9121 | 5630 | 0.4264 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9138 | 5640 | 0.5157 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9154 | 5650 | 0.4721 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9170 | 5660 | 0.4518 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9186 | 5670 | 0.4101 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9202 | 5680 | 0.4092 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9219 | 5690 | 0.4042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9235 | 5700 | 0.3852 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9251 | 5710 | 0.375 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9267 | 5720 | 0.3548 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9283 | 5730 | 0.3461 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9300 | 5740 | 0.3396 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9316 | 5750 | 0.3465 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9332 | 5760 | 0.347 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9348 | 5770 | 0.3365 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9364 | 5780 | 0.3299 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9381 | 5790 | 0.3417 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9397 | 5800 | 0.3423 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9413 | 5810 | 0.3512 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9429 | 5820 | 0.3353 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9445 | 5830 | 0.3291 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9462 | 5840 | 0.3162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9478 | 5850 | 0.3326 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9494 | 5860 | 0.345 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9510 | 5870 | 0.2998 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9526 | 5880 | 0.307 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9543 | 5890 | 0.3019 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9559 | 5900 | 0.3169 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9575 | 5910 | 0.2857 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9591 | 5920 | 0.3018 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9607 | 5930 | 0.2954 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9624 | 5940 | 0.2953 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9640 | 5950 | 0.2861 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9656 | 5960 | 0.3384 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9672 | 5970 | 0.2968 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9688 | 5980 | 0.3191 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9705 | 5990 | 0.3069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9721 | 6000 | 0.3025 | 0.3322 | 0.4606 | 0.6623 | 0.3084 | 0.5552 | 0.4463 | 0.2714 | 0.4404 | 0.7084 | 0.2888 | 0.3529 | 0.4924 | 0.4138 | 0.4410 | | 0.9737 | 6010 | 0.2891 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9753 | 6020 | 0.3038 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9769 | 6030 | 0.2931 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9786 | 6040 | 0.3145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9802 | 6050 | 0.3046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9818 | 6060 | 0.2896 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9834 | 6070 | 0.2926 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9850 | 6080 | 0.3025 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9867 | 6090 | 0.2798 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9883 | 6100 | 0.3006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9899 | 6110 | 0.2695 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9915 | 6120 | 0.3017 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9931 | 6130 | 0.2955 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9948 | 6140 | 0.2699 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9964 | 6150 | 0.2955 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9980 | 6160 | 0.2963 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9996 | 6170 | 0.2988 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.3.0 - Transformers: 4.46.2 - PyTorch: 2.1.1+cu121 - Accelerate: 0.34.2 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## 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} } ```