diff --git "a/README.md" "b/README.md" new file mode 100644--- /dev/null +++ "b/README.md" @@ -0,0 +1,2130 @@ +--- +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} +} +``` + + + + + + \ No newline at end of file