sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:45000 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
language:
- en
library_name: sentence-transformers
license: apache-2.0
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:45000
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Remote sales job with CRM experience

    Business development specialist job description

    Commission-based sales role with flexible schedule'
  sentences:
  - 'Marketing Plans & Research

    Insight to Inform your Strategy

    Your buyer is our preoccupation. So when we work together to develop plans for
    new marketing or product launches, we help you research how your buyers discover
    and decide about solutions like yours. We also help you align overall company
    goals such as revenue growth and customer retention with more specific marketing
    objectives. This yields action oriented, measurable, flexible plans that are aligned
    to results that matter to everyone in the organization.

    - Marketing / Promotion Plan Development

    - Vertical Market Research

    - Competitor Research

    - Persona Development

    Let''s Make Something Great Together!Contact Us

    We capture internal and external perceptions from stakeholders, customers and
    prospects.

    To understand the market and predicted trends, we incorporate relevant 3rd party
    research into the shaping of any strategy.

    Analysis and insight

    We provide highly relevant, easy to implement knowledge to help further important
    marketing decisions.'
  - 'Handpicked Sales and Hot New Products for Busy Women

    Building your Summer Wardrobe with the Basics

    Posts contain affiliate links where I earn a small amount commission on purchases
    through links.

    This post is sponsored by JCPenney but all thoughts are my own!

    Summer is here and that means it is time for a little wardrobe refresh! One thing
    I am doing this year is buying basic pieces that I can mix, match and style with
    others. I teamed up with JCPenney for this post to show you that JCPenney is a
    one stop destination for affordable fashion and home items.'
  - 'Hello, Business Developers!

    My name is Nate Ginsburg and I am a serial entrepreneur. I’m the founder of Premier
    Media, host of the Ecom Exits Podcast, and partner in a handful of other online
    businesses.

    About the role:

    As a Business Development Specialist, with a strong background in sales, you will
    be responsible for identifying, pursuing, and nurturing business opportunities,
    fostering strong client relationships, and expanding our client base. This role
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    and ensuring we meet our client’s talent acquisition needs.

    This is a Remote Part-Time or Full-Time (commission-based) position.

    - Bachelor’s degree in business, marketing, or a related field (preferred).

    - Previous experience in a recruitment agency or the human resources industry,
    with a demonstrated track record of successful sales and client acquisition.

    - Should be able to work independently, from acquiring clients to managing relationships
    with them, ensuring their requirements are met by the back-end team.

    - Should be able to generate leads independently and attend client meetings.

    - Excellent communication and interpersonal skills.

    - Strong negotiation and persuasion abilities.

    - Self-motivated and results-oriented with a high degree of initiative.

    - Ability to work independently and as part of a collaborative team.

    - Proficiency in using CRM software and other relevant sales and marketing tools.

    - Identify and engage with potential clients to understand their staffing and
    recruitment requirements.

    - Develop and maintain a comprehensive understanding of the industries and sectors
    in which the agency operates.

    - Build and maintain a robust sales pipeline to meet and exceed monthly, quarterly,
    and annual revenue targets.

    - Collaborate with our recruitment team to customize solutions that align with
    client needs and deliver value-added services.

    - Prepare and deliver compelling sales presentations to prospective clients.

    - Negotiate and finalize client agreements, contracts, and service-level agreements.

    - Stay up-to-date on industry trends, market dynamics, and competitive landscape.

    - Provide regular reports and updates to the management team on business development
    progress and client acquisition efforts.

    - Work with an existing talented team

    - Completely remote and flexible schedule

    - Location independence

    - You’ll be joining a high-level and fast-paced team, working with exciting projects

    Please note that you’ll be joining a fast-paced and high-achieving team. You’ll
    be held to high expectations and challenged. And you’ll also be supported in your
    growth in this role professionally as well as personally.

    If this sounds interesting to you, please proceed with the application.'
- source_sentence: 'Dock-it Springfield OH contact information

    Dock-it business services Springfield Ohio

    Anthony Young Dock-it phone number'
  sentences:
  - 'VMware testing servers-by-the-minute cloud

    vCloud Air joins the come-and-get-it cloud caper

    Journalists who cover cloud have a recurring nightmare: leaving the “l” out of
    “public cloud” and copping a caning in the comments for their somewhat Freudian
    slip.

    But one vendor that operates a cloud that isn''t entirely public is VMware: Virtzilla''s
    vCloud Air has hitherto been sold as a by-the-month affair.

    The company has, however, signalled it''s keen to get into the “hand me your credit
    card and run up virtual machines for an hour” caper, and has now announced that
    it''s testing just such a service. You – yes even YOU – can apply to be a test
    pilot by signing up here.

    VMware''s calling this vCloud air service “self-provision on-demand” for now,
    and promises access with “nothing more than a browser and a credit card.” Usage
    will be metered by the minute and billed monthly in arrears.

    The company''s talking up the usual vision of vSphere and vCenter everywhere,
    for a One Control Freak To Rule Them All rig that behaves the same whether servers
    are under your desk or on VMware''s largely-rented bit barns.

    The announcement of the test pilot program is noteworthy because VMware''s talking
    up the chance to access “a virtually unlimited pool of resources.”

    That''s a bold statement because compared to the likes of AWS, Google and Microsoft,
    VMware doesn''t have enormous amounts of money to spend on bit barns. With even
    the likes of Rackspace walking away from come-and-get-it public cloud, VMware
    heading in that direction bespeaks either cunning ways of scaling or a big reservoir
    of bucks to throw at the market. ®'
  - 'Click-Through Rate (CTR) is a simple metric that shows that rate at which an
    ad impression receives a click. It’s a core measurement in Search Engine Marketing
    and yet, slightly misunderstood on the surface. Let’s look at this major ad performance
    metric.

    What is Click-Through Rate?

    Whenever and however you generate traffic to your app or website — be it through
    any channel (social, organic, referral, paid, display, email) — your links, banners,
    or ads will have impressions (views) and clicks. Impressions are the number of
    times a link or ad was seen, or rather available on the screen to be seen. Clicks
    are, well, people who clicked the link or ad. Click-Through Rate is the percentage
    of people who saw the impression and clicked it.

    A little fictional case study to better understand CTR

    To better understand CTR, let’s look at an example of a ‘Search Network’ campaign
    in Google Adwords. Since I live in wine country, we’ll be using my, fictional,
    Milani Winery. First, a little context on how Google Adwords works.

    In general, ‘search network’ campaigns through Adwords charge on a cost-per-click
    (aka. pay-per-click) model. Essentially, you’re showing ads at the top of Google
    search results pages for targeted keyword searches. You determine how to show
    your impressions based on factors like geotarget, keyword selection and keyword
    match type. The larger the geotarget and the broader the keyword, the larger the
    pool and likely the lower the Click-Through Rate. The tighter the geotarget and
    more specific the keyword (think niche), the more targeted and smaller the pool
    and thus a likely higher CTR.

    Branding campaigns usually generate a lot of impressions to a wide audience, so
    expect a lower CTR. A Call-to-Action campaign will be more targeted and you’ll
    want to hit a higher CTR.

    Adwords & CTR: Winery Example

    (while I am related to the Sebastiani’s and Mondavi’s, my winery is fictional…for
    now)

    Milani Winery, runs an Adwords campaign to drive visits to their tasting room
    in Northern California. They run three ad groups in a campaign targeting California.
    The first ad group is titled “wine,” and the keywords are ‘broad’ matched to the
    term “wine”. The second ad group is titled “winery,” and targets keywords using
    broad match modifiers, phrases, and exact match keywords. Finally, the third ad
    group in this campaign is titled “tasting room,” and keywords are phrase match
    and exact match only. Which do you think will have the highest Click-Through Rate?

    Keywords for Ad Group 1: “Wine”

    Keywords for Ad Group 2: “Winery”

    Keywords for Ad Group 3: “Tasting Room”

    [tasting room near me]

    [near me tasting room]

    +best +tasting +room

    +tasting +room +visit

    +tasting +room location

    Ad group 1: “Wine” is just too broad and would generate the lowest Click-Through
    Rate (probably around a 0.1%-0.7%). It would return results for things like “Red
    Red Wine” or “How to remove wine stains” — both of which aren’t relevant. Google
    would likely decrease your Quality Score, because of the broadness of the term,
    and your ads probably would show much of the time.

    Ad Group 2: “Winery” is getting closer. Winery is a good search term for increasing
    traffic to a tasting room, but it’s not a bulls-eye match to your target audience.
    Your CTR is likely to be higher here, but not that much more significant. I’d
    probably estimate a Click-Through Rate of around 1%-2.5%. Advertising for this
    keyword could be seen more as a brand awareness campaign.

    Ad group 3: “Tasting Room” is just about right! People searching for tasting rooms
    are likely going to see your ad and find it relevant, especially if your ad is
    enticing or delivering an offer. At this level of targeting expect to see upwards
    of 5-6% and up to maybe 8% in CTR. The volume might be smaller but the relevancy
    and quality is high!

    Anything higher than 10% and you are targeted your audience at level of CTR that
    is usually found when targeting your brand keyword terms, like “Milani Winery.”
    Great Merlot btw!

    * Note, CTR can vary quite a bit depending on bid, landing page quality, and ad
    copy. So, my estimates above assume you have great quality in those three categories.

    The Click-Through Rate Deception

    We’ve now seen an example of CTR in action — targeting broad and delivering low
    CTR, versus tactical targeting generating high CTR. Now we need to talk about
    when the number can lie and why it’s important pair CTR with a quality metric.

    I was once handed an account to optimize that had been running for over a year.
    The previous company had been delivering a very low Cost-Per-Click and a high
    Click-Through Rate. Sounds great on the surface, but as I dove into the data I
    found that they had delivered 98% of the previous years’ clicks and budget on
    a single broad match term that was included in their brand. The keyword was also
    synonymous with the name of a county. To top it off, they were not even measuring
    the quality of visits. Over that previous year, they likely blew $60,000 on irrelevant
    CTR — even though they had great “ad performance” numbers. Don’t always believe
    great ad performance metrics without measuring quality, or viewing actual search
    terms.

    A performance without quality metrics is the really the pitfall of Adwords in
    general. You must have some quality metrics in there that you are tracking. Google
    Analytics by default will import Bounce Rate, Ave. Session Duration, Pages Per
    Session, and % of New Sessions. You should also import your goals from GA to best
    optimize your keyword CTA’s. If for some reason, you can’t access Google Analytics
    and quality metrics, then you can still get an idea of the quality by looking
    at the actual search term data report on the keyword tab of Adwords. That will
    at least tell you if you are hitting a close target.

    Don’t want to manage Adwords yourself? Contact me'
  - 'Dock-it is a Business Services (Unclassified) business in Springfield, OH.

    Dock-it classified under Business Services (Unclassified), with principal employer
    Anthony Young Full Name Report is located in 1938 Broadway, Springfield, Ohio
    OH 45504. For sales, support, account inquiries, and how to be an affiliate, the
    best way to get in touch is through numbers: (937) 340-1110 Full Phone Report.

    It has been operating since , boasting total quality assurance, and an annual
    revenue of Unknown. Their Single Location can be reached with the use of the following
    coordinates: 39.93067,-83.84218. They currently have 1 to 4 employees, and you
    can learn more about their offerings at .

    You can also view their clients’ testimonials, government compliance and annual
    reports through their website. Dock-it aims to strengthen their B2B relationships
    through advertisements and brand promotion.

    Registered codes from Standard Industrial Classification is , and from North American
    Industrial Classification System. Customer feedback is highly appreciated; be
    sure to leave your comments on their website through survey forms. These are necessary
    for company service improvements.

    |Categorized In:||Business Services (Unclassified)|

    |Address:||1938 Broadway, Springfield, OH 45504|

    |Phone Number:||(937) 340-1110 Full Phone Report|

    |Contact Person:||Anthony Young Full Name Report|

    |Business Type:||B2B (Business to Business)|

    |Employee #:||1 to 4|

    |Location Type:||Single Location|

    |Annual Revenue ($):||Unknown|

    |Share This Business:|'
- source_sentence: 'Is Stylelili.com a scam?

    Why is Stylelili.com suspicious?

    Is Stylelili.com a legitimate online shopping platform?'
  sentences:
  - 'By reviewing journal entries, it has been noticed that the base currency has
    a mismatch with the foreign rate, appearing to be incorrectly calculated.

    Example:Account Currency balance in USD is $1,115,354.018 and showing as CAD $1,085,098.1719;
    but it should be $1,429,941.04871 CAD at a current system exchange rate of 1 CAD
    = 0.78 USD. Most of the exchange rates in the sample are between 0.70 and 1. There
    are no transactions with an exchange rate greater than 1. So how is it showing
    a balance greater than Base currency if there are no transactions with an exchange
    rate greater than 1?

    As the calculation is done in a running balance and the transaction for the debit
    that have 1 as transaction rate have more occurrences compared to credit one,
    it caused the balance to be accumulated throughout the year.

    See in this given example:

    Everest kept record for the transaction for each currency, base and foreign, and
    for each of these their own running balance where in the case for this account,
    the carried over amount have accumulated, causing the odd increase in the difference.

    In example given the ending balance are 50 for base currency while in the foreign
    currency it is 80, even though the last entry have 0.70 as exchange rate, the
    running balance rate was 1.60 in actual.

    By default, Everest keep track of the separate amount for each of currency, one
    for base currency and the other one is foreign currency these caused an increase
    in the difference.

    In order to reset this behavior, a suggestion is to clear out the balance by creating
    two journal that uses one journal entry out from account and one journal in entry,
    as an example please find the screenshots below that show switched rate with base
    currency of USD to CAD as foreign currency, with the current balance for the CAD
    account is CAD 70.00.

    Credit: Raditya Perdevi'
  - 'In the current landscape of abundant online shopping options, consumers have
    a plethora of choices. However, these numerous options also come with a hidden
    peril: the presence of scammers posing as genuine businesses. Consequently, when
    evaluating a website’s legitimacy, a tool like Gurustab becomes essential. This
    leads us to the question of whether Stylelili.com is a reputable entity or a fraudulent
    operation.

    Stylelili.com operates as an e-commerce platform, offering a range of products
    from sweaters to hoodies and shirts. Yet, with its prominence as one of the most
    frequently searched websites on Gurustab, a comprehensive assessment was imperative.
    We regularly share our list of the most searched sites on Twitter, providing tips
    on avoiding scams.

    Dubious Registration Date on Stylelili.com

    It’s a common tactic employed by unscrupulous websites to exaggerate their establishment
    date.

    Why? Because consumers are more inclined to trust older online shops over newer
    ones. Consequently, when we encountered their claim on the “About Us” page indicating
    a founding year of 2016, it raised suspicion.

    However, upon cross-referencing with Gurustab and Whois.com, we were startled
    to discover that the website was registered as recently as July 2023. This inconsistency
    casts a shadow of doubt on the legitimacy of Stylelili.com.

    Significant Discounts and Remarkably Low Prices

    One of the most alluring aspects of online shopping is the opportunity to find
    products at prices lower than those in physical stores.

    However, scammers are well aware of this consumer behavior and exploit it to their
    advantage. Stylelili.com entices shoppers with seemingly irresistible deals and
    discounts, promising unbelievable bargains. For instance, a majority of their
    products boast discounts exceeding 80%, an anomaly in the industry.

    While these bargains may appear enticing at first glance, it’s imperative to exercise
    caution when prices seem too good to be true. Online scammers often employ this
    tactic by presenting products at unbelievably low prices to attract potential
    victims. Such rock-bottom prices frequently serve as a telltale sign, indicating
    potentially deceitful intentions. Legitimate businesses strive for sustainable
    profit margins, making excessively low prices a cause for concern.

    Faux Social Media Buttons

    Reputable and genuine online businesses actively maintain a presence on social
    media platforms to connect with their customers, showcase their products, and
    promote transparency. Stylelili.com, however, raises suspicions by displaying
    social media buttons that, upon closer examination, prove to be mere facades.
    Clicking on these buttons redirects users to the main pages of social media platforms
    but lacks any actual content or engagement from the website. This tactic is commonly
    employed to create the illusion of legitimacy and a vibrant online presence.

    Negative Reviews on Trustpilot: A Reason for Alarm

    A brief visit to Trustpilot, a widely-used review platform, reveals a concerning
    pattern regarding Stylelili.com. Numerous customers have voiced skepticism and
    dissatisfaction with their shopping experiences on the site, with some even questioning
    the website’s authenticity.

    Final Verdict: Is Stylelili.com Legit or a Scam?

    In sum, the presence of multiple conspicuous warning signs cannot be overlooked.
    From the remarkably low prices and substantial discounts to the misleading registration
    date, there are ample reasons for concern when it comes to Stylelili.com. The
    inclusion of faux social media buttons and the presence of negative customer reviews
    further strengthen the case against its legitimacy.

    Consumers should exercise utmost caution when considering shopping at Stylelili.com,
    as there are undoubtedly more reliable online fashion retailers available.'
  - 'Are you looking for Spotify Premium Apk Download link? Great! Here you’ll get
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    Features of Spotify Apk:

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    - All latest and older music.

    - Available in miltiple languages.'
- source_sentence: 'What are the skills required for an iPhone app developer?

    What services does Techlopes offer for iPhone app development?

    How can I hire an iPhone app developer?'
  sentences:
  - 'There have been many comments to the same, some logical, some not. I thought
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    board comes to carrier from the consignor, not from the shipper. The consignor
    chooses the carrier and is responsible to pay haulage for his services. Actually,
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    So he overtakes THC charges. I want to buy golf equipment from a seller in USA
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    All comments are export view oriented. What about the impoter? Hi Javier, one
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    should be paid by the shipper who is responsible for the cargo to be delivery
    FOB or since this charge is incurred after the cargo is loaded on board, then
    should it become the liability of the consignee.??

    Hi RK, Trust the handling fees will be forwarders fee which is part of export
    clearance. Hi, Any charges raised prior to the cargo being loaded and stowed on
    board the vessel are for the shipper under FOB. Whats this handling charges? We
    should give that?

    Kindly advise. Hi RK, this would most probably be the Forwarders charges for handling
    the shipment. The Terminal Handling and Bill Charges are the actual port and line
    charges and the Agents Handling would be the fee for the Forwarder. Thus once
    cargo is stowed and secured, a bill of lading can be issued by the vessel. The
    bill of lading is a contract between the shipper and the vessel to move the cargo
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    Sign Up No, Thank you.He was born in Amsterdam, Netherlands and is a Dutch nationality.
    He has almost 6. On the popular complementary channelsthe figures declared by
    the report increases to be somewhere around to be with 1 million subscribers and
    the about a tentative amount million views and searches of his uploaded videos.
    Kwebbelkop is quite successful in his gaming venture and is the creator of a successful
    YouTube channel as well. Kwebbelkop is not the name given by his parents, but
    his birth name is Jordi Van Den Bussche.

    He was born in the year1 st June in the city of Amsterdam, Netherlands and possesses
    a Dutch nationality. He was brought up along with his sister Lauren, but not much
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    gamer might not have let him pierce more into studies and academic lessons. From
    his childhood days, Jordi is into making group of friend for playing games and
    finally started off with the YouTube channel in the year In the month of October,
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    Just after passing of that very year, Jordi decided of starting off with a new
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    By the yearthe count of subscribers reached a count of about 10, After such a
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    a vlog under the name Momsvlog. Meanwhile, while being a gamer, he got his graduate
    degree and started off updating his YouTube channel seriously.

    The three prolific gamers were involved in solving the difficult series of Grand
    Theft Auto Online races. With the passage of time, due to some inconveniences
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    a professional gamer.

    Kwebbelkop has a prosperous gaming career ahead. He had successful started off
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    while playing the tuff games. The team formed by him used to play the various
    races of GTA V online races.

    He is very humble towards his subscriber and viewers. He addresses his fans with
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- source_sentence: 'What is article marketing and how does it work?

    How can I use article marketing to increase traffic to my website?

    What are some effective strategies for an article marketing campaign?'
  sentences:
  - 'Alandari Gray Loveseat

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  - 'Advertising An Article Marketing

    As every website owner knows, content is essential to raising your rankings in
    the search engines. There''s another way to use content to bring even more visitors
    to your site. It''s called article marketing. You can use one article in dozens
    of venues, to multiply your exposure exponentially. More traffic means more sales.
    Let''s look at some strategies you can use in your article marketing campaign.Let''s
    say you sell seeds, gardening supplies and gardening books on your site. You''re
    facing stiff competition, as there are many websites marketing these products.

    How do you get your share of attention? The answer is simple: exposure, as much
    as you can generate! Article marketing is a natural for this purpose. Write one
    article, and use it to advertise your site, over and over again.

    You know your products and their uses best. Write an article on how to start seeds.
    Give detailed and useful information such as equipment required, temperature and
    seasonal concerns, thinning and transplanting. There are many topics you can write
    about.

    Write an article telling your reader how to choose a good pair of garden gloves,
    and the advantages of a leather glove for one task, or a sturdy cotton glove for
    light work.

    Or, tell your reader how a quality bulb planter makes short work of a big planting
    project. I''m sure you get the idea. Do not write an advertisement!

    When your articles are prepared, your related subjectsarticle marketing campaign
    is ready to launch. Do a search using the term ”article directories gardening”.
    You''ll get hundreds or even thousands of results. Visit some of these article
    directories and browse their gardening category. Take a look at the quality of
    their articles. Unfortunately, there are many article directories with poorly
    written articles, so be choosy when selecting those you want to be associated
    with, as quality-conscious directories get the most readers.

    Here''s how it works: you submit your article to the directory. You are not paid
    money, but are rewarded with a byline and a link to your site. Every person who
    reads your article has the opportunity to click through to your site. Give your
    reader useful information in an engaging style, and chances are good they''ll
    come to your site to see what else they can learn. You can submit the same article
    to other directories, and watch your traffic and sales grow.

    Your article marketing strategy should include contacts with other website owners,
    especially those with an e-newsletter. Offer your article as newsletter content
    in exchange for a byline. See if you can negotiate reciprocal links. Of course
    you don''t want to contact direct competition. A florist, greenhouse or organic
    gardening site might be good choices.

    Your article marketing campaign''s success is directly related to the amount of
    exposure you create through your effort. One indirect result of a good article
    marketing campaign is establishing yourself as an expert in your business. Success
    is measured by traffic and sales. Be prolific and diligent!

    About the Author: InsightsOnMarketing provides readers with the latest reviews,
    articles, commentaries and write-ups on all article marketing, article directories,
    article reader related subjects'
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model-index:
- name: hi-di-hi-base
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.8376
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9022
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9204
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9438
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8376
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3007333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18407999999999997
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09437999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8376
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9022
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9204
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9438
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8910666642970089
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.87418634920635
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8762185281448824
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.837
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8996
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9208
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9436
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.837
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.29986666666666667
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18415999999999996
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09435999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.837
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8996
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9208
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9436
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8900999328860697
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8729962698412704
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8749614549818263
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.8256
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8938
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9166
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9402
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8256
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.29793333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18331999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09401999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8256
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8938
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9166
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9402
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8827878243788164
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8643884126984136
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8663736602503759
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.814
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.879
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9082
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9344
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.814
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.293
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18164
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09343999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.814
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.879
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9082
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9344
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8727948445198305
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8531903174603174
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8553014228992841
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.768
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8472
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.879
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9108
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.768
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2824
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17579999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09108
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.768
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8472
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.879
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9108
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8382263417002942
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8151035714285721
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8181338550128306
      name: Cosine Map@100
---

# hi-di-hi-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - json
- **Language:** en
- **License:** apache-2.0

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("carsondial/slinger20241231-1")
# Run inference
sentences = [
    'What is article marketing and how does it work?\nHow can I use article marketing to increase traffic to my website?\nWhat are some effective strategies for an article marketing campaign?',
    "Advertising An Article Marketing\nAs every website owner knows, content is essential to raising your rankings in the search engines. There's another way to use content to bring even more visitors to your site. It's called article marketing. You can use one article in dozens of venues, to multiply your exposure exponentially. More traffic means more sales. Let's look at some strategies you can use in your article marketing campaign.Let's say you sell seeds, gardening supplies and gardening books on your site. You're facing stiff competition, as there are many websites marketing these products.\nHow do you get your share of attention? The answer is simple: exposure, as much as you can generate! Article marketing is a natural for this purpose. Write one article, and use it to advertise your site, over and over again.\nYou know your products and their uses best. Write an article on how to start seeds. Give detailed and useful information such as equipment required, temperature and seasonal concerns, thinning and transplanting. There are many topics you can write about.\nWrite an article telling your reader how to choose a good pair of garden gloves, and the advantages of a leather glove for one task, or a sturdy cotton glove for light work.\nOr, tell your reader how a quality bulb planter makes short work of a big planting project. I'm sure you get the idea. Do not write an advertisement!\nWhen your articles are prepared, your related subjectsarticle marketing campaign is ready to launch. Do a search using the term ”article directories gardening”. You'll get hundreds or even thousands of results. Visit some of these article directories and browse their gardening category. Take a look at the quality of their articles. Unfortunately, there are many article directories with poorly written articles, so be choosy when selecting those you want to be associated with, as quality-conscious directories get the most readers.\nHere's how it works: you submit your article to the directory. You are not paid money, but are rewarded with a byline and a link to your site. Every person who reads your article has the opportunity to click through to your site. Give your reader useful information in an engaging style, and chances are good they'll come to your site to see what else they can learn. You can submit the same article to other directories, and watch your traffic and sales grow.\nYour article marketing strategy should include contacts with other website owners, especially those with an e-newsletter. Offer your article as newsletter content in exchange for a byline. See if you can negotiate reciprocal links. Of course you don't want to contact direct competition. A florist, greenhouse or organic gardening site might be good choices.\nYour article marketing campaign's success is directly related to the amount of exposure you create through your effort. One indirect result of a good article marketing campaign is establishing yourself as an expert in your business. Success is measured by traffic and sales. Be prolific and diligent!\nAbout the Author: InsightsOnMarketing provides readers with the latest reviews, articles, commentaries and write-ups on all article marketing, article directories, article reader related subjects",
    'Alandari Gray Loveseat\nSporting a pleasing roll arm for classic flair, the Alandari loveseat in soft gray exudes an easy elegance sure to please. Feel-good upholstery with linen texturing is made for everyday luxury. Muted-tone toss pillows are a soothing, sophisticated complement.\nFrame constructions have been rigorously tested to simulate the home and transportation environments for improved durability. Frame components are secured with combinations of glue, blocks, interlocking panels and staples. Seats and back spring rails are cut from mixed hardwood and engineered lumber. Stripes and patterns are match cut. All fabrics are pre-approved for wearability and durability against AHFA standards. Cushions are constructed of low melt fiber wrapped over high quality foam. Constructed with a platform seat foundation.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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

#### Information Retrieval

* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | dim_768    | dim_512    | dim_256    | dim_128    | dim_64     |
|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1   | 0.8376     | 0.837      | 0.8256     | 0.814      | 0.768      |
| cosine_accuracy@3   | 0.9022     | 0.8996     | 0.8938     | 0.879      | 0.8472     |
| cosine_accuracy@5   | 0.9204     | 0.9208     | 0.9166     | 0.9082     | 0.879      |
| cosine_accuracy@10  | 0.9438     | 0.9436     | 0.9402     | 0.9344     | 0.9108     |
| cosine_precision@1  | 0.8376     | 0.837      | 0.8256     | 0.814      | 0.768      |
| cosine_precision@3  | 0.3007     | 0.2999     | 0.2979     | 0.293      | 0.2824     |
| cosine_precision@5  | 0.1841     | 0.1842     | 0.1833     | 0.1816     | 0.1758     |
| cosine_precision@10 | 0.0944     | 0.0944     | 0.094      | 0.0934     | 0.0911     |
| cosine_recall@1     | 0.8376     | 0.837      | 0.8256     | 0.814      | 0.768      |
| cosine_recall@3     | 0.9022     | 0.8996     | 0.8938     | 0.879      | 0.8472     |
| cosine_recall@5     | 0.9204     | 0.9208     | 0.9166     | 0.9082     | 0.879      |
| cosine_recall@10    | 0.9438     | 0.9436     | 0.9402     | 0.9344     | 0.9108     |
| **cosine_ndcg@10**  | **0.8911** | **0.8901** | **0.8828** | **0.8728** | **0.8382** |
| cosine_mrr@10       | 0.8742     | 0.873      | 0.8644     | 0.8532     | 0.8151     |
| cosine_map@100      | 0.8762     | 0.875      | 0.8664     | 0.8553     | 0.8181     |

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## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 45,000 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                              | positive                                                                             |
  |:--------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                               |
  | details | <ul><li>min: 12 tokens</li><li>mean: 30.74 tokens</li><li>max: 112 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 382.61 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                                                          | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>How to split the Join form into multiple parts<br>Adding HTML and text to the Join form page<br>Customizing the login logout message</code>                                                               | <code>This tutorial will show you how to split the Join form/page into two or three parts. Whether you want a cleaner looking join form or you want to reduce the number of spam bots this should help.<br>This Tutorial will show you how to add HTML and text to the Join form page. If you have ever wanted to add a few more details or spruce up the Join form then this might be what you have been looking for.<br>This tutorial will show you how to spruce up the login - logout message people see. If you want to add a special message or just change the standard default phrase this one might be for you. It's very simple and only takes a few moments to apply this one.<br>This tutorial will show you how to take advantage and optimize your site using the Page Block feature available in your Administration panel. With a little exploring and tweaking it's possible to boost your site's performance.<br>If your Forum RSS feed links are redirecting to your homepage when clicked on you might need to update the forum module...</code>          |
  | <code>Failed mobile technologies of 2011<br>What happened to Siri<br>Disappointing mobile innovations</code>                                                                                                    | <code>Last year we all got excited about mobile-computing products that failed to deliver<br>It's an awesome time to be a gadget-happy consumer electronics freak. Multi-touch user interfaces. Huge advances in miniaturization and battery life. Cloud-based storage. Mobile computing has never been better.<br>But sometimes, when companies announce incredible new products or technologies, and everybody proclaims that a new era has dawned, and that culture-shifting transformations are about to take place -- nothing happens.<br>Here are five mobile technologies from last year that were supposed to change the world, but didn't.<br>Apple seemed to do everything right with its voice assistant strategy.<br>The company acquired the leading app maker with the best technology. It spent two years perfecting and integrating the technology, and bulking up on servers to handle the number-crunching required to deliver human-like voice interaction.<br>Siri was then launched to huge fanfare.<br>Overnight, people changed how they int...</code> |
  | <code>What are the different types of web usage for voice over campaigns?<br>What are the standard types of web usage for voice over?<br>What are the different ways to use voice over in web campaigns?</code> | <code>Different Types of Web Usage<br>Clearly, nothing has upended the voice over industry quite like the Internet. On all ends of the spectrum, from the basic sourcing of voice over talent to right on down to hyper-targeted, increasingly personalized messaging, it can feel a little “Wild West” to navigate if you’re not going directly through an agent. And one of the biggest stumblers when sourcing for voice talent? The concept of “web usage.”<br>Usage — a.k.a., how the finished voice over is being used — will cause the greatest fluctuation in a voice talent’s quote. As we all know, there are myriad ways any material can be used and distributed online… and not all distribution is created equally.<br>Below, we’ve got a handy little guide detailing all the different, standard types of web usage we tend to come across here at Blue Wave Voiceover (Heck, the way things are going, we might have to update this once more before you finish reading it).<br>Feel free to refer to this when putting together any kind...</code>          |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `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`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `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`: True
- `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_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step    | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:----------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.1137     | 10      | 1.0843        | -                      | -                      | -                      | -                      | -                     |
| 0.2274     | 20      | 0.6597        | -                      | -                      | -                      | -                      | -                     |
| 0.3412     | 30      | 0.3466        | -                      | -                      | -                      | -                      | -                     |
| 0.4549     | 40      | 0.253         | -                      | -                      | -                      | -                      | -                     |
| 0.5686     | 50      | 0.2286        | -                      | -                      | -                      | -                      | -                     |
| 0.6823     | 60      | 0.2007        | -                      | -                      | -                      | -                      | -                     |
| 0.7960     | 70      | 0.1576        | -                      | -                      | -                      | -                      | -                     |
| 0.9097     | 80      | 0.1652        | -                      | -                      | -                      | -                      | -                     |
| 0.9893     | 87      | -             | 0.8880                 | 0.8861                 | 0.8788                 | 0.8657                 | 0.8267                |
| 1.0341     | 90      | 0.1563        | -                      | -                      | -                      | -                      | -                     |
| 1.1478     | 100     | 0.1485        | -                      | -                      | -                      | -                      | -                     |
| 1.2615     | 110     | 0.108         | -                      | -                      | -                      | -                      | -                     |
| 1.3753     | 120     | 0.0874        | -                      | -                      | -                      | -                      | -                     |
| 1.4890     | 130     | 0.0851        | -                      | -                      | -                      | -                      | -                     |
| 1.6027     | 140     | 0.0897        | -                      | -                      | -                      | -                      | -                     |
| 1.7164     | 150     | 0.0803        | -                      | -                      | -                      | -                      | -                     |
| 1.8301     | 160     | 0.0645        | -                      | -                      | -                      | -                      | -                     |
| 1.9439     | 170     | 0.0806        | -                      | -                      | -                      | -                      | -                     |
| 1.9893     | 174     | -             | 0.8906                 | 0.8893                 | 0.8815                 | 0.8712                 | 0.8351                |
| 2.0682     | 180     | 0.0812        | -                      | -                      | -                      | -                      | -                     |
| 2.1819     | 190     | 0.0743        | -                      | -                      | -                      | -                      | -                     |
| 2.2957     | 200     | 0.0531        | -                      | -                      | -                      | -                      | -                     |
| 2.4094     | 210     | 0.0448        | -                      | -                      | -                      | -                      | -                     |
| 2.5231     | 220     | 0.0465        | -                      | -                      | -                      | -                      | -                     |
| 2.6368     | 230     | 0.0486        | -                      | -                      | -                      | -                      | -                     |
| 2.7505     | 240     | 0.0509        | -                      | -                      | -                      | -                      | -                     |
| 2.8643     | 250     | 0.0395        | -                      | -                      | -                      | -                      | -                     |
| 2.9780     | 260     | 0.0521        | -                      | -                      | -                      | -                      | -                     |
| 2.9893     | 261     | -             | 0.8912                 | 0.8897                 | 0.8823                 | 0.8720                 | 0.8375                |
| 3.1023     | 270     | 0.0551        | -                      | -                      | -                      | -                      | -                     |
| 3.2161     | 280     | 0.0412        | -                      | -                      | -                      | -                      | -                     |
| 3.3298     | 290     | 0.0373        | -                      | -                      | -                      | -                      | -                     |
| 3.4435     | 300     | 0.0387        | -                      | -                      | -                      | -                      | -                     |
| 3.5572     | 310     | 0.0438        | -                      | -                      | -                      | -                      | -                     |
| 3.6709     | 320     | 0.0433        | -                      | -                      | -                      | -                      | -                     |
| 3.7846     | 330     | 0.0368        | -                      | -                      | -                      | -                      | -                     |
| 3.8984     | 340     | 0.0418        | -                      | -                      | -                      | -                      | -                     |
| **3.9893** | **348** | **-**         | **0.8911**             | **0.8901**             | **0.8828**             | **0.8728**             | **0.8382**            |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## Citation

### BibTeX

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

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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