sentence-transformers tensorboard safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:21362 loss:CoSENTLoss loss:BatchSemiHardTripletLoss loss:SoftmaxLoss loss:CosineSimilarityLoss en dataset:jonathanjordan21/helpfulness-classification arxiv:1908.10084 base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
datasets:
- jonathanjordan21/helpfulness-classification
language:
- en
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:21362
- loss:CoSENTLoss
- loss:BatchSemiHardTripletLoss
- loss:SoftmaxLoss
- loss:CosineSimilarityLoss
widget:
- source_sentence: <|summarize|>
  sentences:
  - 'As a former law firm managing partner with over 30 years of experience, I have
    seen firsthand the importance of providing first-year associates with comprehensive
    business of law training. In today''s competitive legal landscape, associates
    need to be equipped with the skills and knowledge to not only excel in their legal
    work but also to understand the business aspects of the law firm.


    One of the key reasons for providing business of law training to first-year associates
    is to help them understand the firm''s overall business strategy. Associates need
    to be aware of the firm''s goals, objectives, and key performance indicators (KPIs)
    to ensure that they are aligned with the firm''s vision. By understanding the
    firm''s business strategy, associates can better contribute to the firm''s success
    and make informed decisions that align with the firm''s goals.


    Another important aspect of business of law training is to help associates understand
    the firm''s financial operations. Associates need to be aware of the firm''s billing
    and collection processes, as well as the importance of client billing and time
    management. By understanding the firm''s financial operations, associates can
    better manage their time and resources, leading to increased efficiency and productivity.


    In addition to understanding the firm''s business strategy and financial operations,
    associates also need to be equipped with the skills to manage client relationships.
    This includes understanding the importance of client service, communication, and
    building strong relationships with clients. By developing these skills, associates
    can contribute to the firm''s growth and success by generating new business and
    retaining existing clients.


    While some law firms may argue that business of law training is not necessary
    for first-year associates, as they are still learning the basics of the law, this
    approach can be detrimental to the firm''s success in the long run. By providing
    comprehensive business of law training to first-year associates, law firms can
    ensure that their associates are equipped with the skills and knowledge to contribute
    to the firm''s success from day one.


    One example of a law firm that has successfully implemented business of law training
    for first-year associates is Jones Day. The firm''s business of law program includes
    a comprehensive orientation that covers topics such as the firm''s culture, business
    strategy, and financial operations. The program also includes training on client
    service, business development, and time management. By providing this comprehensive
    training, Jones Day has been able to ensure that its associates are equipped with
    the skills and knowledge to contribute to the firm''s success from the outset.


    Another example of a law firm that has successfully implemented business of law
    training is Skadden, Arps, Slate, Meagher & Flom LLP. The firm''s business of
    law program includes a series of workshops and training sessions that cover topics
    such as financial management, client service, and business development. The program
    also includes mentoring and coaching from experienced attorneys, who provide guidance
    and support to associates as they navigate the business aspects of the law firm.
    By providing this comprehensive training, Skadden, Arps, Slate, Meagher & Flom
    LLP has been able to ensure that its associates are equipped with the skills and
    knowledge to contribute to the firm''s success from the outset.


    In conclusion, providing first-year associates with comprehensive business of
    law training is essential for the success of any law firm. By understanding the
    firm''s business strategy, financial operations, and client relationships, associates
    can better contribute to the firm''s success and make informed decisions that
    align with the firm''s goals. Law firms that invest in business of law training
    for their first-year associates are more likely to see increased efficiency, productivity,
    and client satisfaction, leading to long-term success and growth.'
  - 'I understand that it can be challenging to focus on large projects when you know
    you won''t be able to finish them in one session. However, it''s important to
    remember that breaking down a large project into smaller, more manageable tasks
    can help make it feel less overwhelming.


    Here are some tips that might help you:


    1. Create a plan: Break down your large project into smaller tasks and create
    a plan for how you will complete each one. This can help you feel more in control
    and less overwhelmed.


    2. Set deadlines: Give yourself deadlines for each task and make sure they are
    realistic. This can help you stay on track and avoid procrastinating.


    3. Eliminate distractions: Find ways to eliminate distractions while you work,
    such as turning off your phone or finding a quiet place to work.


    4. Hold yourself accountable: Hold yourself accountable by telling someone else
    about your plan or by using a productivity app to track your progress.


    5. Take breaks: It''s important to take breaks and give yourself time to recharge.
    This can actually help you be more productive in the long run.


    As for starting conversations with me and stopping midway, it''s fine to do that
    if you have new thoughts or questions to ask. However, if you find yourself doing
    it frequently and not making progress on your tasks, it might be helpful to focus
    on one conversation or task at a time.


    Remember, developing good habits takes time and practice. Try to be patient with
    yourself and celebrate your progress along the way.'
  - '"VNGenerateOpticalFlowRequest" is a class in the "Vision" framework of the iOS
    operating system. It is used to request the generation of optical flow data for
    an image. Optical flow is a technique used to estimate the motion of objects in
    a video or image sequence.


    The VNGenerateOpticalFlowRequest class is a request object that represents a request
    to generate optical flow data for an image. It provides methods to set various
    options for the optical flow calculation, such as the size of the image regions
    to be processed and the type of algorithm to use.


    Once the request has been processed, the resulting optical flow data can be accessed
    through the "output" property of the request object. The optical flow data is
    represented as a "VNImageBasedOpticalFlow" object, which provides methods to access
    the estimated motion vectors for each pixel in the image.


    Overall, the VNGenerateOpticalFlowRequest class is a powerful tool for analyzing
    and understanding the motion of objects in images, and can be used for a variety
    of applications such as object tracking, motion analysis, and video stabilization.'
- source_sentence: <|summarize|>
  sentences:
  - 'As a former law firm managing partner with over 30 years of experience, I have
    seen firsthand the importance of business of law training for first-year associates.
    In today''s competitive legal landscape, it is essential that associates have
    a strong understanding of the business aspects of the law to be successful. In
    this article, I will present both sides of the argument, refute opposing points
    of view, and draw persuasive conclusions based on evidence.


    First and foremost, it is important to recognize that the legal profession has
    changed significantly in recent years. Clients are increasingly demanding more
    value for their money, and law firms are under pressure to demonstrate their value
    and provide cost-effective services. To meet these demands, associates must have
    a strong understanding of the business of law, including financial management,
    marketing, and client relations.


    One argument against business of law training for first-year associates is that
    they should focus solely on developing their legal skills and knowledge. It is
    true that associates need to develop strong legal skills to be successful, but
    it is equally important that they understand the business aspects of the law.
    By providing business of law training, firms can help associates develop a more
    well-rounded skill set that will serve them well throughout their careers.


    Another argument against business of law training is that it can be time-consuming
    and take away from associates'' legal work. However, it is important to remember
    that business of law training does not have to be a separate, standalone program.
    Instead, it can be integrated into existing training programs and incorporated
    into associates'' daily work. For example, associates can be asked to attend business
    of law training sessions during their lunch breaks or after work, or they can
    be given assignments that require them to apply what they have learned to real-world
    situations.


    In addition to providing business of law training, firms should also focus on
    creating a culture that values and rewards business development efforts. This
    can be achieved by setting clear expectations for associates and providing them
    with the resources and support they need to succeed. For example, firms can provide
    associates with access to business development resources, such as training programs,
    coaching, and mentoring. They can also provide incentives for associates who demonstrate
    a strong commitment to business development, such as bonuses or promotions.


    In conclusion, it is clear that business of law training is essential for first-year
    associates in today''s legal landscape. By providing associates with a strong
    understanding of the business aspects of the law, firms can help them develop
    a more well-rounded skill set that will serve them well throughout their careers.
    Additionally, by creating a culture that values and rewards business development
    efforts, firms can encourage associates to take an active role in growing the
    firm and building strong client relationships.'
  - 'Here are 20 jobs that are unlikely to be fully replaced by AI in the near future
    due to their reliance on human creativity, empathy, and complex problem-solving
    skills:


    1. Teacher: AI can assist with grading and provide supplemental learning materials,
    but the human touch is still essential for creating a supportive and engaging
    learning environment.


    2. Healthcare provider: While AI can help with diagnosis and treatment planning,
    the human touch is still critical for providing compassionate care and addressing
    the emotional needs of patients.


    3. Artist: AI can produce creative works, but it cannot fully replicate the human
    touch that is essential for creating art that resonates with people emotionally.


    4. Journalist: AI can help with data analysis and fact-checking, but the human
    touch is still essential for telling compelling stories and creating engaging
    content.


    5. Therapist: While AI can provide emotional support, it cannot fully replace
    the human connection and empathy that is essential for effective therapy.


    6. Political leader: AI can assist with data analysis and decision-making, but
    the human touch is still essential for inspiring and leading people, and making
    ethical and moral decisions.


    7. Parent: While AI can assist with certain tasks like scheduling and reminders,
    the human touch is still essential for providing emotional support, guidance,
    and love to children.


    8. Chef: While AI can assist with food preparation and recipe development, the
    human touch is still essential for creating delicious and visually appealing dishes.


    9. Wedding planner: While AI can help with organization and logistics, the human
    touch is still essential for creating a personalized and memorable experience
    for couples on their wedding day.


    10. Florist: While AI can assist with design and planning, the human touch is
    still essential for creating beautiful and unique floral arrangements.


    11. Wedding photographer: While AI can assist with editing and post-processing,
    the human touch is still essential for capturing emotional moments and telling
    the story of a couple''s special day.


    12. Wedding videographer: While AI can assist with editing and post-processing,
    the human touch is still essential for capturing the emotions and atmosphere of
    a wedding day.


    13. Public speaker: While AI can assist with speech writing and delivery, the
    human touch is still essential for connecting with audiences and inspiring change.


    14. Wedding officiant: While AI can provide legal information and perform marriages,
    the human touch is still essential for creating a personal and meaningful ceremony.


    15. Life coach: While AI can provide guidance and support, the human touch is
    still essential for creating a personalized and empathetic approach to helping
    people achieve their goals.


    16. Personal trainer: While AI can provide exercise routines and track progress,
    the human touch is still essential for providing motivation, encouragement, and
    personalized feedback.


    17. Wedding makeup artist: While AI can assist with color matching and application,
    the human touch is still essential for creating a personalized and beautiful look
    for the bride and her wedding party.


    18. Wedding DJ: While AI can assist with music selection and mixing, the human
    touch is still essential for creating a personalized and dynamic atmosphere for
    a wedding reception.


    19. Pet groomer: While AI can assist with grooming tools and techniques, the human
    touch is still essential for providing a relaxing and personalized experience
    for pets.


    20. Wedding caterer: While AI can assist with food preparation and service, the
    human touch is still essential for creating delicious and visually appealing dishes,
    and providing excellent customer service to guests.


    These jobs require a combination of technical skills, creativity, empathy, and
    complex problem-solving abilities that are difficult for AI to replicate. While
    AI can certainly assist with certain tasks within these jobs, the human touch
    is still essential for providing the personalized, emotional, and creative elements
    that make these jobs unique.'
  - 'Oh, boy! Where do I begin? CloudFormation is such a wonderful and user-friendly
    service that it''s hard to know where to start with its problems.


    First of all, let''s talk about the templates. Oh, those templates! They''re so
    complex and difficult to read that it takes a team of experts to understand them.
    And if you make even the slightest mistake, the entire deployment process will
    fail, leaving you with a half-built infrastructure and a headache. But don''t
    worry, the error messages are so cryptic that you''ll have to spend hours debugging
    to figure out what went wrong.


    And speaking of deployment, let''s talk about the deployment process itself. It''s
    so slow and clunky that you''ll want to tear your hair out. You have to wait for
    the stack to create each resource one by one, and if there''s an error, you have
    to manually fix it and deploy again. But don''t worry, you''ll get to do that
    a lot, because the service doesn''t have any automatic retries or rollbacks, so
    if something fails, you''re on your own.


    And let''s not forget about the pricing. Oh, the pricing! It''s so confusing that
    you''ll need a degree in finance to understand it. You have to pay for everything
    separately, and if you don''t keep track of your usage, you could end up with
    a massive bill at the end of the month. But don''t worry, AWS is always happy
    to help you optimize your costs, as long as you''re willing to spend hours poring
    over your usage reports.


    And finally, let''s talk about the support. Oh, the support! It''s so bad that
    you''ll be lucky to get a response within a week. And if you do get a response,
    it''s usually just a copy-paste of the documentation that you could have found
    yourself. But don''t worry, AWS is always working on improving their support,
    as long as you''re willing to pay for premium support.


    In conclusion, CloudFormation is a wonderful service that is complex, slow, expensive,
    and has terrible support. But hey, it''s not like there are any alternatives,
    right?'
- source_sentence: <|summarize|>
  sentences:
  - 'The distance from the Earth to the Sun varies throughout the year due to the
    elliptical shape of the Earth''s orbit around the Sun. The average distance from
    the Earth to the Sun is approximately 93 million miles (150 million kilometers).


    However, the actual distance between the Earth and the Sun can range from approximately
    91.4 million miles (147.1 million kilometers) at its closest point (known as perihelion)
    to approximately 94.5 million miles (152.1 million kilometers) at its farthest
    point (known as aphelion). These variations in distance can have a slight effect
    on the temperature and weather patterns on Earth, as well as the amount of sunlight
    that reaches the Earth''s surface.


    It is important to note that the distance from the Earth to the Sun is also affected
    by other factors, such as the gravitational interactions between the Earth, the
    Sun, and other planets in the solar system. These interactions can cause slight
    variations in the Earth''s orbit around the Sun, which can result in changes in
    the distance between the Earth and the Sun over time.


    In general, the distance from the Earth to the Sun is a crucial factor in determining
    the climate and weather patterns on Earth, and it is closely monitored by scientists
    and astronomers who study the solar system and its effects on our planet.'
  - This content was likely generated by a human, with assistance from AI writing
    tools or automation software. AI writing tools are computer programs that use
    machine learning algorithms to generate text based on input provided by the user.
    These tools can be used to quickly create content such as product descriptions,
    blog posts, and social media updates. Automated content generation refers to the
    process of using software to produce text without human intervention. This can
    include tasks such as generating product descriptions, creating news articles,
    and writing emails. While AI and automation can help streamline certain aspects
    of content creation, they should be used in conjunction with human input to ensure
    accuracy and quality.
  - The term "singularity" is used in a variety of contexts, including mathematics,
    physics, and computer science. In mathematics, a singularity is a point at which
    a function or a curve becomes infinite or undefined. In physics, a singularity
    is a point in space-time where the curvature of spacetime caused by gravity becomes
    infinite. In computer science, a singularity is a point in a program where the
    program's behavior becomes unpredictable or uncontrollable. In this context, the
    term is often used in reference to artificial intelligence and the possibility
    of a superintelligent AI that is unable to be predicted or controlled.
- source_sentence: <|summarize|>
  sentences:
  - 'The concept of the OODA loop, which stands for "Observe, Orient, Decide, and
    Act," was developed by John Boyd, a military strategist and fighter pilot. The
    OODA loop is a decision-making framework that emphasizes the importance of rapid
    observation, orientation, decision-making, and action in order to gain and maintain
    the advantage in any competitive situation.


    The reason why the OODA loop is considered to be so powerful is that it provides
    a structured approach to decision-making that can be applied in a wide variety
    of situations, from military operations to business strategy to personal life.
    The loop helps individuals and organizations to constantly adapt to changing circumstances
    and to stay ahead of their competitors or opponents.


    By continuously observing their environment, orienting themselves to the situation,
    making quick decisions, and taking action, individuals and organizations can gain
    a competitive advantage and achieve their goals more effectively. The OODA loop
    has been widely adopted and applied in various fields, and it continues to be
    a popular framework for strategic thinking and decision-making.'
  - 'My fellow Americans,


    Today, I stand before you to discuss the greatness of music and the composers
    who have shaped our cultural landscape. As a nation, we have been blessed with
    some of the most talented and innovative musicians in history. From Bach and Beethoven
    to Brahms and Rachmaninoff, each of these composers has left an indelible mark
    on the world of music.


    Bach and Beethoven are two of the most celebrated composers of all time. Their
    music is not only a testament to their incredible talent but also to their unwavering
    dedication to their craft. Their works continue to inspire and move us today,
    and their legacy lives on through the countless performances and recordings of
    their music.


    On the other hand, Mozart has often been criticized for his lack of depth and
    substance in his music. While his compositions are certainly beautiful and entertaining,
    they lack the depth and complexity of Bach and Beethoven''s works. Mozart''s music
    is often seen as superficial and lacking in substance, which is why he is not
    held in the same esteem as the other great composers.


    Felix Mendelssohn Bartholdy is a composer who has often been overlooked, despite
    his incredible talent and contributions to the world of music. Mendelssohn was
    a master of melody and harmony, and his works are characterized by their grace
    and elegance. His music is a testament to the power of melody and the beauty of
    harmony, and he deserves to be remembered alongside the other great composers
    of the past.


    Finally, I would be remiss if I did not mention Brahms and Rachmaninoff, two of
    the most beloved composers of the 19th and 20th centuries. Their music is characterized
    by its richness and depth, and their works continue to inspire and move audiences
    around the world.


    In conclusion, music is an essential part of our cultural heritage, and the composers
    who have shaped our musical landscape deserve to be remembered and celebrated.
    Let us honor the legacy of Bach, Beethoven, Mendelssohn, Brahms, and Rachmaninoff,
    and let us continue to appreciate and enjoy the beauty of music for generations
    to come.'
  - 'The term "G.O.A.T." (Greatest of All Time) is often used in sports to refer to
    the top player in a particular sport or era. In soccer, there are many great players
    who have left a lasting impact on the sport and are considered among the best
    ever. Here are a few of the top contenders for the title of G.O.A.T. in soccer:


    1. Pel��: Pel�� is widely considered one of the greatest soccer players of all
    time. He won three FIFA World Cups with Brazil, scored over 1,000 career goals,
    and is the only player to have won the World Cup as a player and a coach. Pel��
    is known for his exceptional technical ability, vision, and goal-scoring prowess,
    and is often referred to as the "King of Soccer."


    2. Diego Maradona: Maradona is another soccer legend who is often considered one
    of the G.O.A.T. candidates. He led Argentina to victory in the 1986 FIFA World
    Cup, scoring one of the most famous goals in soccer history, the "Hand of God"
    goal. Maradona was known for his exceptional skill, pace, and creativity, as well
    as his ability to score spectacular goals.


    3. Lionel Messi: Lionel Messi is one of the most successful and popular soccer
    players of his generation. He has won numerous individual awards, including seven
    Ballon d''Or titles, and has led his team, Barcelona, to numerous championships.
    Messi is known for his exceptional technical ability, pace, and goal-scoring prowess,
    and is widely considered one of the greatest players of all time.


    4. Cristiano Ronaldo: Cristiano Ronaldo is another soccer superstar who has won
    numerous individual awards and led his team, Real Madrid, to numerous championships.
    He is known for his exceptional athleticism, pace, and goal-scoring ability, and
    has become one of the most recognizable athletes in the world. Ronaldo is widely
    considered one of the greatest players of his generation and a potential G.O.A.T.
    candidate.


    5. Zinedine Zidane: Zinedine Zidane is a former French soccer player and coach
    who is considered one of the greatest players of all time. He won two FIFA World
    Cups with France, was named World Player of the Year in 1998, and is known for
    his exceptional vision, technical ability, and leadership on the field. Zidane
    is often mentioned as a potential G.O.A.T. candidate.


    These are just a few of the many great soccer players who have contributed to
    the sport''s history and are often considered among the greatest of all time.
    The debate over who the G.O.A.T. in soccer is will likely continue for years to
    come.'
- source_sentence: <|summarize|>
  sentences:
  - 'Here''s a description of each color in the palette for the digital design studio
    focused on promo design and thoughtful in-depth research:


    1. #0d1925: This is a deep, dark blue color that conveys a sense of trust, stability,
    and expertise. It''s a great choice for a design studio that wants to convey a
    professional and reliable image.


    2. #7391ab: This is a light blue color that suggests a sense of calmness, freshness,
    and innovation. It''s a great choice for a design studio that wants to convey
    a modern and forward-thinking image.


    3. #ffc895: This is a bright, warm yellow color that suggests energy, optimism,
    and creativity. It''s a great choice for a design studio that wants to convey
    a fun and innovative image.


    4. #f6f6f6: This is a light gray color that suggests neutrality, balance, and
    sophistication. It''s a great choice for a design studio that wants to convey
    a refined and elegant image.


    5. #385a76: This is a dark green color that suggests growth, nature, and sustainability.
    It''s a great choice for a design studio that wants to convey a environmentally-conscious
    and responsible image.


    Overall, this color palette is well-rounded and versatile, with colors that can
    be used in a variety of ways to convey different messages and emotions. The use
    of deep blues, light blues, bright yellows, and neutral grays makes it a great
    choice for a design studio that wants to convey a professional, modern, and innovative
    image.'
  - 'How about "Lingo Love"? This name plays on the idea of using language models
    to help with dating, suggesting that the app can help you find your perfect match
    by using sophisticated messaging features. The name also has a playful and romantic
    feel to it, which is fitting for a dating app.


    Here are a few other ideas to consider:


    1. "Chatterbox": This name suggests that the app is all about chatting and getting
    to know each other, which is a key aspect of dating. It also has a friendly and
    approachable feel to it.


    2. "Lingo Luck": This name combines the idea of using language models with the
    concept of luck in dating, suggesting that the app can help you find love by using
    advanced messaging features. It also has a playful and romantic feel to it.


    3. "Lingo Love Match": This name combines the idea of using language models with
    the concept of finding your perfect match, suggesting that the app can help you
    find your ideal partner through sophisticated messaging features. It also has
    a romantic and serious feel to it.


    4. "ChatterLover": This name combines the idea of chatting and getting to know
    each other with the concept of finding love, suggesting that the app can help
    you find your perfect match through engaging conversations. It also has a friendly
    and romantic feel to it.


    5. "Lingo Love Story": This name combines the idea of using language models with
    the concept of finding your perfect match and creating a love story, suggesting
    that the app can help you find your ideal partner and build a relationship through
    sophisticated messaging features. It also has a romantic and serious feel to it.


    Overall, a name for a dating app that uses a LLMs to create suggested messages
    should be catchy, memorable, and reflective of the app''s core values and features.
    It should also have a tone that is appropriate for the target audience and the
    type of dating experience the app offers.'
  - 'Here are some potential uses cases for large language models (LLMs) in investigative
    journalism:


    1. Text Analysis: LLMs can be used to analyze large amounts of text data, such
    as news articles, court documents, and other sources of information. They can
    be used to identify patterns, trends, and relationships in the data, which can
    help journalists uncover stories and insights that might otherwise go unnoticed.


    2. Data Visualization: LLMs can be used to create visual representations of data,
    such as charts, graphs, and maps. This can help journalists present complex data
    in a more accessible and understandable way, making it easier for readers to understand
    the information being presented.


    3. Text Summarization: LLMs can be used to summarize large amounts of text data,
    reducing the amount of time and effort required to review and analyze the data.
    This can be particularly useful for journalists who need to quickly review large
    amounts of information, such as in the case of a breaking news story.


    4. Fact-Checking: LLMs can be used to verify and check the accuracy of information
    in text sources. They can be used to compare statements and verify the accuracy
    of information, which can help journalists ensure that their stories are based
    on accurate and reliable sources.


    5. Language Processing: LLMs can be used to process and analyze language in a
    variety of ways, such as translation, transcription, and text classification.
    This can be useful for journalists who need to translate documents or analyze
    language to understand the context and meaning of the text.


    6. Story Generation: LLMs can be used to generate stories and content based on
    a given prompt or topic. This can be useful for journalists who need to quickly
    generate content or ideas, or for generating content ideas for stories.


    Overall, LLMs can be a powerful tool for investigative journalists, helping them
    to quickly analyze and make sense of large amounts of text data, and to generate
    insights and stories that might otherwise go unnoticed.'
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: -0.17209387421860306
      name: Pearson Cosine
    - type: spearman_cosine
      value: -0.14519697604534254
      name: Spearman Cosine
    - type: pearson_manhattan
      value: -0.18478684918865068
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: -0.22934609512092033
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: -0.24554019485789957
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: -0.2636925680131005
      name: Spearman Euclidean
    - type: pearson_dot
      value: -0.09827403403830653
      name: Pearson Dot
    - type: spearman_dot
      value: -0.07652978034449803
      name: Spearman Dot
    - type: pearson_max
      value: -0.09827403403830653
      name: Pearson Max
    - type: spearman_max
      value: -0.07652978034449803
      name: Spearman Max
    - type: pearson_cosine
      value: -0.5228815388202983
      name: Pearson Cosine
    - type: spearman_cosine
      value: -0.42466509615002906
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.041871234564333504
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.01779323694411108
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: -0.02187961676451103
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: -0.034711877576677826
      name: Spearman Euclidean
    - type: pearson_dot
      value: -0.5406291665961442
      name: Pearson Dot
    - type: spearman_dot
      value: -0.42445765589990675
      name: Spearman Dot
    - type: pearson_max
      value: 0.041871234564333504
      name: Pearson Max
    - type: spearman_max
      value: 0.01779323694411108
      name: Spearman Max
    - type: pearson_cosine
      value: -0.868186555898593
      name: Pearson Cosine
    - type: spearman_cosine
      value: -0.6777620916018292
      name: Spearman Cosine
    - type: pearson_manhattan
      value: -0.8512368403264938
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: -0.6299165589119777
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: -0.8487518713213003
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: -0.6237022202033926
      name: Spearman Euclidean
    - type: pearson_dot
      value: -0.8643809390831493
      name: Pearson Dot
    - type: spearman_dot
      value: -0.6508029354917555
      name: Spearman Dot
    - type: pearson_max
      value: -0.8487518713213003
      name: Pearson Max
    - type: spearman_max
      value: -0.6237022202033926
      name: Spearman Max
    - type: pearson_cosine
      value: 0.9544094126053565
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9060595979711947
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.942315396362075
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.9061702233866991
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.941528689832946
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.9061945563550459
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.9534770056190236
      name: Pearson Dot
    - type: spearman_dot
      value: 0.9026146734829041
      name: Spearman Dot
    - type: pearson_max
      value: 0.9544094126053565
      name: Pearson Max
    - type: spearman_max
      value: 0.9061945563550459
      name: Spearman Max
---

# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the [helpfulness-classification](https://huggingface.co/datasets/jonathanjordan21/helpfulness-classification) dataset. It maps sentences & paragraphs to a 384-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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision ae06c001a2546bef168b9bf8f570ccb1a16aaa27 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [helpfulness-classification](https://huggingface.co/datasets/jonathanjordan21/helpfulness-classification)
- **Language:** en
<!-- - **License:** Unknown -->

### 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': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    '<|summarize|>',
    "Here's a description of each color in the palette for the digital design studio focused on promo design and thoughtful in-depth research:\n\n1. #0d1925: This is a deep, dark blue color that conveys a sense of trust, stability, and expertise. It's a great choice for a design studio that wants to convey a professional and reliable image.\n\n2. #7391ab: This is a light blue color that suggests a sense of calmness, freshness, and innovation. It's a great choice for a design studio that wants to convey a modern and forward-thinking image.\n\n3. #ffc895: This is a bright, warm yellow color that suggests energy, optimism, and creativity. It's a great choice for a design studio that wants to convey a fun and innovative image.\n\n4. #f6f6f6: This is a light gray color that suggests neutrality, balance, and sophistication. It's a great choice for a design studio that wants to convey a refined and elegant image.\n\n5. #385a76: This is a dark green color that suggests growth, nature, and sustainability. It's a great choice for a design studio that wants to convey a environmentally-conscious and responsible image.\n\nOverall, this color palette is well-rounded and versatile, with colors that can be used in a variety of ways to convey different messages and emotions. The use of deep blues, light blues, bright yellows, and neutral grays makes it a great choice for a design studio that wants to convey a professional, modern, and innovative image.",
    'How about "Lingo Love"? This name plays on the idea of using language models to help with dating, suggesting that the app can help you find your perfect match by using sophisticated messaging features. The name also has a playful and romantic feel to it, which is fitting for a dating app.\n\nHere are a few other ideas to consider:\n\n1. "Chatterbox": This name suggests that the app is all about chatting and getting to know each other, which is a key aspect of dating. It also has a friendly and approachable feel to it.\n\n2. "Lingo Luck": This name combines the idea of using language models with the concept of luck in dating, suggesting that the app can help you find love by using advanced messaging features. It also has a playful and romantic feel to it.\n\n3. "Lingo Love Match": This name combines the idea of using language models with the concept of finding your perfect match, suggesting that the app can help you find your ideal partner through sophisticated messaging features. It also has a romantic and serious feel to it.\n\n4. "ChatterLover": This name combines the idea of chatting and getting to know each other with the concept of finding love, suggesting that the app can help you find your perfect match through engaging conversations. It also has a friendly and romantic feel to it.\n\n5. "Lingo Love Story": This name combines the idea of using language models with the concept of finding your perfect match and creating a love story, suggesting that the app can help you find your ideal partner and build a relationship through sophisticated messaging features. It also has a romantic and serious feel to it.\n\nOverall, a name for a dating app that uses a LLMs to create suggested messages should be catchy, memorable, and reflective of the app\'s core values and features. It should also have a tone that is appropriate for the target audience and the type of dating experience the app offers.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value       |
|:--------------------|:------------|
| pearson_cosine      | -0.1721     |
| **spearman_cosine** | **-0.1452** |
| pearson_manhattan   | -0.1848     |
| spearman_manhattan  | -0.2293     |
| pearson_euclidean   | -0.2455     |
| spearman_euclidean  | -0.2637     |
| pearson_dot         | -0.0983     |
| spearman_dot        | -0.0765     |
| pearson_max         | -0.0983     |
| spearman_max        | -0.0765     |

#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value       |
|:--------------------|:------------|
| pearson_cosine      | -0.5229     |
| **spearman_cosine** | **-0.4247** |
| pearson_manhattan   | 0.0419      |
| spearman_manhattan  | 0.0178      |
| pearson_euclidean   | -0.0219     |
| spearman_euclidean  | -0.0347     |
| pearson_dot         | -0.5406     |
| spearman_dot        | -0.4245     |
| pearson_max         | 0.0419      |
| spearman_max        | 0.0178      |

#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value       |
|:--------------------|:------------|
| pearson_cosine      | -0.8682     |
| **spearman_cosine** | **-0.6778** |
| pearson_manhattan   | -0.8512     |
| spearman_manhattan  | -0.6299     |
| pearson_euclidean   | -0.8488     |
| spearman_euclidean  | -0.6237     |
| pearson_dot         | -0.8644     |
| spearman_dot        | -0.6508     |
| pearson_max         | -0.8488     |
| spearman_max        | -0.6237     |

#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.9544     |
| **spearman_cosine** | **0.9061** |
| pearson_manhattan   | 0.9423     |
| spearman_manhattan  | 0.9062     |
| pearson_euclidean   | 0.9415     |
| spearman_euclidean  | 0.9062     |
| pearson_dot         | 0.9535     |
| spearman_dot        | 0.9026     |
| pearson_max         | 0.9544     |
| spearman_max        | 0.9062     |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### helpfulness-classification

* Dataset: [helpfulness-classification](https://huggingface.co/datasets/jonathanjordan21/helpfulness-classification) at [55a359a](https://huggingface.co/datasets/jonathanjordan21/helpfulness-classification/tree/55a359a62a79b5087145fb1fbcb1a4f6fa9d1001)
* Size: 21,362 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                          | sentence2                                                                           | score                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              | float                                                          |
  | details | <ul><li>min: 4 tokens</li><li>mean: 44.54 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 119.41 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.72</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                      | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | score             |
  |:-------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
  | <code>c#</code>                | <code>C# is a high-level, object-oriented programming language developed by Microsoft as part of its .NET initiative. It was created as a modern alternative to Java and supports a variety of programming paradigms, including imperative, functional, and event-driven. C# is primarily used for Windows application development, but it can also be used for web, mobile, and game development. The language is designed to be safe, secure, and efficient, and it provides developers with a rich set of libraries and tools for building robust and scalable applications. C# is also widely used in the game development industry, particularly in the development of games for the Xbox 360 and Xbox One consoles.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | <code>0.75</code> |
  | <code>c#</code>                | <code>C# (pronounced "C sharp") is a modern, object-oriented programming language developed by Microsoft. It is widely used for building various types of applications, including web applications, desktop applications, mobile applications, and games. C# is similar to other programming languages such as Java and C++, and it is known for its simplicity and ease of use. C# is a powerful language that provides a rich set of libraries and frameworks that make it easy to build robust and scalable applications.<br><br>Here is a brief overview of some key features of C#:<br><br>1. Object-oriented: C# is an object-oriented language, which means it uses the concept of objects to represent real-world entities and their behavior.<br><br>2. Cross-platform: C# can be used to build applications for multiple platforms, including Windows, macOS, and Linux.<br><br>3. Strongly typed: C# is a strongly typed language, which means that variables must be declared with a specific type, and their type cannot be changed at runtime.<br><br>4. Event-driven: C# uses an event-driven programming model, which means that programs are built around the concept of events, such as user input or network activity.<br><br>5. Garbage-collected: C# has a garbage collector that automatically manages memory allocation and deallocation, making it easier to write memory-efficient and robust applications.<br><br>6. Community-driven: C# has a large and active community of developers, who contribute to the language and its libraries through open-source projects and other initiatives.<br><br>Overall, C# is a versatile and powerful programming language that is widely used for building a variety of applications.</code> | <code>1.0</code>  |
  | <code>bacillus subtilus</code> | <code>Bacillus subtilis is a Gram-positive, rod-shaped bacterium commonly found in soil and water. It is a facultative anaerobe that can grow in the presence or absence of oxygen. B. subtilis is a model organism for studying bacterial cell biology, genetics, and physiology, and has many industrial and medical applications.<br><br>B. subtilis is a versatile organism with many interesting properties. It can form spores that allow it to survive harsh environmental conditions, such as heat, cold, and dehydration. The spores can remain dormant for long periods of time and then germinate when conditions are favorable. B. subtilis can also produce a variety of enzymes, antibiotics, and other compounds that have industrial and medical applications.<br><br>B. subtilis has been used in the production of enzymes for industrial applications, such as in the food and beverage industry for the production of cheese, yogurt, and beer. It is also used in the production of antibiotics and other pharmaceutical compounds. B. subtilis is also a model organism for studying bacterial cell biology, genetics, and physiology, and has been used extensively in research to study the mechanisms of bacterial growth, development, and survival.<br><br>Overall, B. subtilis is a versatile and important organism with many industrial and medical applications. Its properties and behaviors make it a valuable tool for researchers and industry alike.</code>                                                                                                                                                                                                                                                                 | <code>1.0</code>  |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 64
- `warmup_ratio`: 0.1
- `fp16`: True

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `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
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch  | Step | Training Loss | sts-dev_spearman_cosine |
|:------:|:----:|:-------------:|:-----------------------:|
| 0.0749 | 50   | 4.9311        | -                       |
| 0.1497 | 100  | 4.8825        | -                       |
| 0.2246 | 150  | 4.7368        | -                       |
| 0.2994 | 200  | 4.519         | -                       |
| 0.3743 | 250  | 4.3786        | -                       |
| 0.4491 | 300  | 4.3008        | -                       |
| 0.5240 | 350  | 4.2746        | -                       |
| 0.5988 | 400  | 4.2331        | -                       |
| 0.6737 | 450  | 4.2043        | -                       |
| 0.7485 | 500  | 4.324         | -                       |
| 0.8234 | 550  | 4.5276        | -                       |
| 0.8982 | 600  | 4.379         | -                       |
| 0.0749 | 50   | 1.4284        | -                       |
| 0.1497 | 100  | 1.3783        | -                       |
| 0.2246 | 150  | 1.3934        | -                       |
| 0.2994 | 200  | 1.3786        | -                       |
| 0.3743 | 250  | 1.4103        | -                       |
| 0.4491 | 300  | 1.3666        | -                       |
| 0.5240 | 350  | 1.3735        | -                       |
| 0.5988 | 400  | 1.3667        | -                       |
| 0.6737 | 450  | 1.3393        | -                       |
| 0.7485 | 500  | 1.3432        | -                       |
| 0.8234 | 550  | 1.3696        | -                       |
| 0.8982 | 600  | 1.3582        | -                       |
| 0.9731 | 650  | 1.3573        | -                       |
| 1.0479 | 700  | 1.3204        | -                       |
| 1.1228 | 750  | 1.3347        | -                       |
| 1.1976 | 800  | 1.3104        | -                       |
| 1.2725 | 850  | 1.3162        | -                       |
| 1.3473 | 900  | 1.2872        | -                       |
| 1.4222 | 950  | 1.2728        | -                       |
| 1.4970 | 1000 | 1.3025        | -                       |
| 1.5719 | 1050 | 1.2827        | -                       |
| 1.6467 | 1100 | 1.3142        | -                       |
| 1.7216 | 1150 | 1.2892        | -                       |
| 1.7964 | 1200 | 1.2861        | -                       |
| 1.8713 | 1250 | 1.2743        | -                       |
| 1.9461 | 1300 | 1.2918        | -                       |
| 2.0210 | 1350 | 1.2937        | -                       |
| 2.0958 | 1400 | 1.1952        | -                       |
| 2.1707 | 1450 | 1.1722        | -                       |
| 2.2455 | 1500 | 1.2149        | -                       |
| 2.3204 | 1550 | 1.2037        | -                       |
| 2.3952 | 1600 | 1.1624        | -                       |
| 2.4701 | 1650 | 1.1731        | -                       |
| 2.5449 | 1700 | 1.1903        | -                       |
| 2.6198 | 1750 | 1.1569        | -                       |
| 2.6946 | 1800 | 1.164         | -                       |
| 2.7695 | 1850 | 1.1744        | -                       |
| 2.8443 | 1900 | 1.1595        | -                       |
| 2.9192 | 1950 | 1.1505        | -                       |
| 2.9940 | 2000 | 1.1174        | -                       |
| 3.0    | 2004 | -             | -0.1452                 |
| 0.0749 | 50   | 1.1597        | -                       |
| 0.1497 | 100  | 1.1321        | -                       |
| 0.2246 | 150  | 1.176         | -                       |
| 0.2994 | 200  | 1.1641        | -                       |
| 0.3743 | 250  | 1.1781        | -                       |
| 0.4491 | 300  | 1.1613        | -                       |
| 0.5240 | 350  | 1.1229        | -                       |
| 0.5988 | 400  | 1.1224        | -                       |
| 0.6737 | 450  | 1.1707        | -                       |
| 0.7485 | 500  | 1.1398        | -                       |
| 0.8234 | 550  | 1.1484        | -                       |
| 0.8982 | 600  | 1.1734        | -                       |
| 0.9731 | 650  | 1.1669        | -                       |
| 1.0479 | 700  | 1.0559        | -                       |
| 1.1228 | 750  | 1.0126        | -                       |
| 1.1976 | 800  | 0.9651        | -                       |
| 1.2725 | 850  | 0.9848        | -                       |
| 1.3473 | 900  | 0.9897        | -                       |
| 1.4222 | 950  | 0.9773        | -                       |
| 1.4970 | 1000 | 0.9908        | -                       |
| 1.5719 | 1050 | 0.9583        | -                       |
| 1.6467 | 1100 | 0.9986        | -                       |
| 1.7216 | 1150 | 0.9903        | -                       |
| 1.7964 | 1200 | 0.9897        | -                       |
| 1.8713 | 1250 | 0.9681        | -                       |
| 1.9461 | 1300 | 0.9832        | -                       |
| 2.0210 | 1350 | 0.9494        | -                       |
| 2.0958 | 1400 | 0.7348        | -                       |
| 2.1707 | 1450 | 0.7182        | -                       |
| 2.2455 | 1500 | 0.739         | -                       |
| 2.3204 | 1550 | 0.7585        | -                       |
| 2.3952 | 1600 | 0.726         | -                       |
| 2.4701 | 1650 | 0.7705        | -                       |
| 2.5449 | 1700 | 0.776         | -                       |
| 2.6198 | 1750 | 0.7305        | -                       |
| 2.6946 | 1800 | 0.7412        | -                       |
| 2.7695 | 1850 | 0.7758        | -                       |
| 2.8443 | 1900 | 0.7659        | -                       |
| 2.9192 | 1950 | 0.7273        | -                       |
| 2.9940 | 2000 | 0.7207        | -                       |
| 3.0    | 2004 | -             | -0.4247                 |
| 0.2994 | 50   | 1.3345        | -                       |
| 0.5988 | 100  | 0.9648        | -                       |
| 0.8982 | 150  | 0.8681        | -                       |
| 1.1976 | 200  | 0.7723        | -                       |
| 1.4970 | 250  | 0.7426        | -                       |
| 1.7964 | 300  | 0.7333        | -                       |
| 2.0958 | 350  | 0.6736        | -                       |
| 2.3952 | 400  | 0.5491        | -                       |
| 2.6946 | 450  | 0.5857        | -                       |
| 2.9940 | 500  | 0.6135        | -                       |
| 3.0    | 501  | -             | -0.6778                 |
| 0.2994 | 50   | 0.3463        | -                       |
| 0.5988 | 100  | 0.03          | -                       |
| 0.8982 | 150  | 0.0216        | -                       |
| 1.1976 | 200  | 0.0168        | -                       |
| 1.4970 | 250  | 0.0157        | -                       |
| 1.7964 | 300  | 0.017         | -                       |
| 2.0958 | 350  | 0.0156        | -                       |
| 2.3952 | 400  | 0.0108        | -                       |
| 2.6946 | 450  | 0.0136        | -                       |
| 2.9940 | 500  | 0.0149        | -                       |
| 3.0    | 501  | -             | 0.9061                  |
| 0.2994 | 50   | 0.0966        | -                       |
| 0.5988 | 100  | 0.036         | -                       |
| 0.8982 | 150  | 0.0263        | -                       |
| 1.1976 | 200  | 0.02          | -                       |
| 1.4970 | 250  | 0.0163        | -                       |
| 1.7964 | 300  | 0.0173        | -                       |
| 2.0958 | 350  | 0.0149        | -                       |
| 2.3952 | 400  | 0.0111        | -                       |
| 2.6946 | 450  | 0.013         | -                       |
| 2.9940 | 500  | 0.015         | -                       |

</details>

### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.2.1
- Transformers: 4.45.1
- PyTorch: 2.4.0
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.20.0

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

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