Update model card with comprehensive information
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README.md
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tags:
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- sentence-transformers
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- dense
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- generated_from_trainer
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- dataset_size:32000
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- loss:MultipleNegativesRankingLoss
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base_model: sentence-transformers/all-MiniLM-L6-v2
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widget:
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- source_sentence: how to present delivery offers creatively
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sentences:
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- Introducing the "Delivery Offer with Fresh Raw Plums" presentation template, featuring
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a vibrant poster adorned with lush plums, some artfully flying through the air.
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Perfect for marketing ads, business finance, and fashion style industries, this
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template adds a fresh twist to presentations. Ideal for holidays, celebrations,
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and city-themed events, it captures the essence of express courier and delivery
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services. Engage your audience with this visually captivating and professionally
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designed template.
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- Elevate your presentations with the "Inclusive Urban Design Contribution Gratitude"
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template. This visually engaging design features a certificate motif, perfect
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for recognizing achievements in construction and urban development. Ideal for
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industries like Marketing Ads, Entertainment Leisure, and Business Finance, this
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template is also suited for Holidays Celebration, Fashion Style, and Travels Vacations
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contexts. Seamlessly blend Courier, Delivery, and Celebration themes to captivate
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your audience and underscore your message with professional flair.
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- 'Unleash your creativity with the "Volunteer Work Quote with Animal Skull" presentation
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template. Featuring a striking black and white image of a ram, this graphic design
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is perfect for industries like Marketing Ads, Entertainment Leisure, and Services.
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Ideal for Holidays Celebration, Food & Drinks, and Fashion Style presentations,
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this template captivates with its artistic flair. Engage your audience with its
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bold visual elements, making it a standout choice for those seeking impactful,
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professional presentations. Keywords: graphic design, animal skull, creative presentation.'
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- source_sentence: leisure activities presentation style
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sentences:
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- Elevate your presentations with the "Fashion Quote Businessman Wearing Suit in
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Grey" template. Featuring sleek visuals of a confident man in a suit and tie,
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this template embodies sophistication and style. Ideal for industries like Marketing
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Ads, Entertainment, Leisure, and Business Finance, it seamlessly fits categories
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such as Holidays Celebration and Food & Drinks. Perfect for presentations focused
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on celebrating, socializing, and enjoying life's finer moments, this template
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ensures your message is both impactful and memorable.
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- Dive into a lush paradise with our "Amazing Tropical Vegetation" presentation
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template. Featuring vibrant green plant imagery against a chic white and pink
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striped backdrop, this template exudes a lively yet professional aesthetic. Perfect
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for marketing ads, entertainment projects, or business finance presentations,
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it's ideal for holiday celebrations, leisure activities, or pet-related content.
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Captivate your audience with this versatile, visually stunning template, bringing
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a touch of tropical flair to your next project.
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- 'Introducing the "Nature Expo Announcement: Blooming Daisy Flower" presentation
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template, a vibrant blend of lush green fields and pristine white flowers, perfect
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for capturing the essence of nature. Ideal for marketing ads, entertainment, and
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service industries, this template is perfect for holidays, celebrations, and food
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and drinks events. With elements like music notes and food frames, it''s designed
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for those celebrating nature''s beauty. Engage your audience with this captivating,
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nature-inspired template. Perfect for creating an unforgettable Nature Expo 2019
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presentation.'
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- source_sentence: Gaming championship announcement design
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sentences:
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- Unleash the fun with our Video Games Championship Announcement template! Featuring
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a sleek black background and the intriguing phrase, "Is this video game your passion
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without challenges?" this template is perfect for marketing ads and entertainment
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events. Ideal for holidays, celebrations, and leisure activities, it seamlessly
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integrates a food frame for added flair. Engage your audience with its amusing
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style, making it perfect for celebrating gaming enthusiasts and their love for
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competition.
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- Elevate your presentations with the "Teacher Helping Kids" template, featuring
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heartwarming visuals of a dedicated woman assisting a young boy in a vibrant classroom
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setting. Ideal for marketing ads, entertainment, and service industries, this
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template seamlessly fits holiday celebrations and food and drink events. Perfect
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for parties, socializing, and cultural gatherings, it brings an engaging, professional
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touch to your message. Inspire your audience with these captivating, relatable
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scenes that highlight education and community.
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- 'Unleash your creativity with the "Do It Yourself Inspirational Banner" presentation
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template. Featuring a sleek black and white logo with the phrase ''dot yourself,''
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this design is framed by a vibrant yellow-bordered white background, exuding a
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modern yet professional aesthetic. Perfect for marketing ads, entertainment, and
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business finance sectors, this template is ideal for celebrating holidays, showcasing
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food and drinks, or highlighting cities and places. Keywords: courier, delivery,
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express.'
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- source_sentence: holiday celebration presentation design
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sentences:
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- Elevate your presentations with the "Digital Photography Tips with Camera" template,
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featuring a sleek dark blue background accented by vibrant lights and a striking
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black and white clock image. Perfect for professionals in marketing ads, entertainment,
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and leisure services, this template is ideal for holidays, food and drinks, and
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fashion style presentations. Capture your audience's attention and boost your
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Instagram presence with this visually stunning, professional template designed
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for engaging storytelling.
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- Elevate your marketing campaigns with the "Antique Furniture Ad with Luxury Armchair"
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presentation template. Featuring a sophisticated visual style, this template seamlessly
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integrates a furniture store logo with an elegant armchair centerpiece. Perfect
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for marketing ads in the entertainment and leisure industry, business finance
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presentations, and holiday celebrations, this template captures attention effortlessly.
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Ideal for use in courier, delivery, and travel sectors, it ensures your message
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is delivered with timeless elegance and professional flair.
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- 'The "Happy Children at Kids Camp" presentation template features vibrant visuals
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of joyful children sitting on lush grass, set against a lively green banner backdrop.
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Perfect for marketing ads, entertainment, leisure, and business finance sectors,
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this template is ideal for holiday celebrations, food and drinks promotions, and
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fashion style events. With its engaging food frame and informational infographics,
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it''s designed to captivate audiences while effectively conveying your message.
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Keywords: kids camp, celebrations, engaging visuals, marketing.'
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- source_sentence: engaging slides for food and drink theme
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sentences:
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- Elevate your presentations with the "Android Robot Hand" template, featuring a
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sleek, futuristic design. This template includes a captivating white robot set
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against a dynamic blue background, complemented by a blue and white bar chart
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emphasizing the number 1. Perfect for industries like Marketing Ads, Business
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Finance, and Fashion Style, it's ideal for discussions on trends and innovations.
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Engage your audience with this modern, trend-focused template, suitable for Courier,
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Delivery, and Express services.
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- Discover the "Cycling Club Tips" presentation template, featuring a minimalistic
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design with a striking black and white diagonal striped background and an image
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of a woman cycling against a crisp white backdrop. Perfect for marketing ads,
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entertainment, leisure, and business finance sectors, this template is ideal for
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creating engaging holiday celebrations or food and drinks presentations. Seamlessly
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integrate keywords like courier, delivery, and express to captivate your audience
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and elevate your message with style and impact.
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- Elevate your presentations with the "Smartphone Review Man Scrolling Phone" template,
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featuring a sleek design with dynamic visuals of a person holding a smartphone
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against a modern white background adorned with triangles. Perfect for marketing
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ads, business finance, and fashion style industries, this template is ideal for
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holidays, food and drinks, and leisure entertainment themes. Enhance your courier
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and delivery presentations or celebration pitches with this versatile, professional
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template that captivates and engages your audience effortlessly.
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pipeline_tag: sentence-similarity
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library_name: sentence-transformers
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metrics:
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- cosine_accuracy@1
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- cosine_accuracy@3
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- cosine_accuracy@5
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- cosine_accuracy@10
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- cosine_precision@1
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- cosine_precision@3
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- cosine_precision@5
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- cosine_precision@10
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- cosine_recall@1
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- cosine_recall@3
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- cosine_recall@5
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- cosine_recall@10
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- cosine_ndcg@10
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- cosine_mrr@10
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- cosine_map@1
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- cosine_map@5
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- cosine_map@10
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model-index:
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- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
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results:
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- task:
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type: information-retrieval
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name: Information Retrieval
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dataset:
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name: validation
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type: validation
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metrics:
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- type: cosine_accuracy@1
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value: 0.45
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name: Cosine Accuracy@1
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- type: cosine_accuracy@3
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value: 0.62
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name: Cosine Accuracy@3
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- type: cosine_accuracy@5
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value: 0.685625
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name: Cosine Accuracy@5
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- type: cosine_accuracy@10
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value: 0.765625
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name: Cosine Accuracy@10
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- type: cosine_precision@1
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value: 0.45
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name: Cosine Precision@1
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- type: cosine_precision@3
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value: 0.20666666666666664
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name: Cosine Precision@3
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- type: cosine_precision@5
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value: 0.137125
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name: Cosine Precision@5
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- type: cosine_precision@10
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value: 0.0765625
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name: Cosine Precision@10
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- type: cosine_recall@1
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value: 0.45
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name: Cosine Recall@1
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- type: cosine_recall@3
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value: 0.62
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name: Cosine Recall@3
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- type: cosine_recall@5
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value: 0.685625
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name: Cosine Recall@5
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- type: cosine_recall@10
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value: 0.765625
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name: Cosine Recall@10
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- type: cosine_ndcg@10
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value: 0.6029385769142473
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name: Cosine Ndcg@10
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- type: cosine_mrr@10
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value: 0.5515214533730152
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name: Cosine Mrr@10
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- type: cosine_map@1
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value: 0.45
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name: Cosine Map@1
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- type: cosine_map@5
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value: 0.5409479166666666
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name: Cosine Map@5
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- type: cosine_map@10
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value: 0.5515214533730158
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name: Cosine Map@10
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---
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#
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). 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.
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## Model Details
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### Model Description
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- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
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- **Maximum Sequence Length:** 256 tokens
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- **Output Dimensionality:** 384 dimensions
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- **Similarity Function:** Cosine Similarity
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<!-- - **Training Dataset:** Unknown -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
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(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})
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(2): Normalize()
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)
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```
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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```
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Then you can load this model and run inference.
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```python
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("sentence_transformers_model_id")
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# Run inference
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sentences = [
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'engaging slides for food and drink theme',
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'Elevate your presentations with the "Smartphone Review Man Scrolling Phone" template, featuring a sleek design with dynamic visuals of a person holding a smartphone against a modern white background adorned with triangles. Perfect for marketing ads, business finance, and fashion style industries, this template is ideal for holidays, food and drinks, and leisure entertainment themes. Enhance your courier and delivery presentations or celebration pitches with this versatile, professional template that captivates and engages your audience effortlessly.',
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'Discover the "Cycling Club Tips" presentation template, featuring a minimalistic design with a striking black and white diagonal striped background and an image of a woman cycling against a crisp white backdrop. Perfect for marketing ads, entertainment, leisure, and business finance sectors, this template is ideal for creating engaging holiday celebrations or food and drinks presentations. Seamlessly integrate keywords like courier, delivery, and express to captivate your audience and elevate your message with style and impact.',
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# [3, 384]
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# Get the similarity scores for the embeddings
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similarities = model.similarity(embeddings, embeddings)
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print(similarities)
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# tensor([[1.0000, 0.1884, 0.1604],
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# [0.1884, 1.0000, 0.2392],
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# [0.1604, 0.2392, 1.0000]])
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```
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-->
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
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| Metric | Value |
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|:--------------------|:-----------|
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| cosine_accuracy@1 | 0.45 |
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| cosine_accuracy@3 | 0.62 |
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| cosine_accuracy@5 | 0.6856 |
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| cosine_accuracy@10 | 0.7656 |
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| cosine_precision@1 | 0.45 |
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| cosine_precision@3 | 0.2067 |
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| cosine_precision@5 | 0.1371 |
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| cosine_precision@10 | 0.0766 |
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| cosine_recall@1 | 0.45 |
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| cosine_recall@3 | 0.62 |
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| cosine_recall@5 | 0.6856 |
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| cosine_recall@10 | 0.7656 |
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| **cosine_ndcg@10** | **0.6029** |
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| cosine_mrr@10 | 0.5515 |
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| cosine_map@1 | 0.45 |
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| cosine_map@5 | 0.5409 |
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| cosine_map@10 | 0.5515 |
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<!--
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## Bias, Risks and Limitations
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
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<!--
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### Recommendations
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-->
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## Training Details
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### Training
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| sentence_0 | sentence_1 |
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|:------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| <code>entertainment-themed presentation slides</code> | <code>Ignite creativity with our "Reading Inspiration Books on Shelves" presentation template. Featuring a charming visual style with piles of books on a table, this template is perfect for industries like Marketing, Entertainment, and Services. Ideal for Holidays, Celebrations, and Leisure topics, it seamlessly integrates themes of Food, Drinks, and Games. Captivate your audience with a professional yet engaging backdrop that celebrates creativity and leisure. Perfect for marketers looking to inspire and entertain.</code> |
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| <code>fashion-forward slides for holidays and celebrations</code> | <code>Elevate your presentations with the "Insurance Company Successful Business Team" template, featuring a sleek design showcasing an insurance logo and a dynamic duo seated on a couch. A pink shield on a pristine white background adds a touch of elegance. Perfect for marketing ads, entertainment, and fashion, it's ideal for holidays, celebrations, or pet-related themes. Keywords like "courier," "beauty," and "celebration" seamlessly blend, making it a captivating choice for professionals seeking style and impact.</code> |
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| <code>How to promote a decor event template</code> | <code>Unveil your event with our "Interior Decoration Event Announcement Sofa in Grey" template, featuring a chic living room setting with a stylish grey couch and vibrant green plant. Perfect for marketing ads in the entertainment and leisure industries, this template is ideal for holiday celebrations or home decor events. Capture attention with its modern aesthetic and versatile design, seamlessly integrating keywords like courier, delivery, and express to boost your promotional efforts.</code> |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
|
362 |
-
```json
|
363 |
-
{
|
364 |
-
"scale": 20.0,
|
365 |
-
"similarity_fct": "cos_sim"
|
366 |
-
}
|
367 |
-
```
|
368 |
|
369 |
### Training Hyperparameters
|
370 |
-
|
371 |
-
|
372 |
-
-
|
373 |
-
- `per_device_train_batch_size`: 64
|
374 |
-
- `per_device_eval_batch_size`: 64
|
375 |
-
- `num_train_epochs`: 20
|
376 |
-
- `multi_dataset_batch_sampler`: round_robin
|
377 |
-
|
378 |
-
#### All Hyperparameters
|
379 |
-
<details><summary>Click to expand</summary>
|
380 |
-
|
381 |
-
- `overwrite_output_dir`: False
|
382 |
-
- `do_predict`: False
|
383 |
-
- `eval_strategy`: steps
|
384 |
-
- `prediction_loss_only`: True
|
385 |
-
- `per_device_train_batch_size`: 64
|
386 |
-
- `per_device_eval_batch_size`: 64
|
387 |
-
- `per_gpu_train_batch_size`: None
|
388 |
-
- `per_gpu_eval_batch_size`: None
|
389 |
-
- `gradient_accumulation_steps`: 1
|
390 |
-
- `eval_accumulation_steps`: None
|
391 |
-
- `torch_empty_cache_steps`: None
|
392 |
-
- `learning_rate`: 5e-05
|
393 |
-
- `weight_decay`: 0.0
|
394 |
-
- `adam_beta1`: 0.9
|
395 |
-
- `adam_beta2`: 0.999
|
396 |
-
- `adam_epsilon`: 1e-08
|
397 |
-
- `max_grad_norm`: 1
|
398 |
-
- `num_train_epochs`: 20
|
399 |
-
- `max_steps`: -1
|
400 |
-
- `lr_scheduler_type`: linear
|
401 |
-
- `lr_scheduler_kwargs`: {}
|
402 |
-
- `warmup_ratio`: 0.0
|
403 |
-
- `warmup_steps`: 0
|
404 |
-
- `log_level`: passive
|
405 |
-
- `log_level_replica`: warning
|
406 |
-
- `log_on_each_node`: True
|
407 |
-
- `logging_nan_inf_filter`: True
|
408 |
-
- `save_safetensors`: True
|
409 |
-
- `save_on_each_node`: False
|
410 |
-
- `save_only_model`: False
|
411 |
-
- `restore_callback_states_from_checkpoint`: False
|
412 |
-
- `no_cuda`: False
|
413 |
-
- `use_cpu`: False
|
414 |
-
- `use_mps_device`: False
|
415 |
-
- `seed`: 42
|
416 |
-
- `data_seed`: None
|
417 |
-
- `jit_mode_eval`: False
|
418 |
-
- `use_ipex`: False
|
419 |
-
- `bf16`: False
|
420 |
-
- `fp16`: False
|
421 |
-
- `fp16_opt_level`: O1
|
422 |
-
- `half_precision_backend`: auto
|
423 |
-
- `bf16_full_eval`: False
|
424 |
-
- `fp16_full_eval`: False
|
425 |
-
- `tf32`: None
|
426 |
-
- `local_rank`: 0
|
427 |
-
- `ddp_backend`: None
|
428 |
-
- `tpu_num_cores`: None
|
429 |
-
- `tpu_metrics_debug`: False
|
430 |
-
- `debug`: []
|
431 |
-
- `dataloader_drop_last`: False
|
432 |
-
- `dataloader_num_workers`: 0
|
433 |
-
- `dataloader_prefetch_factor`: None
|
434 |
-
- `past_index`: -1
|
435 |
-
- `disable_tqdm`: False
|
436 |
-
- `remove_unused_columns`: True
|
437 |
-
- `label_names`: None
|
438 |
-
- `load_best_model_at_end`: False
|
439 |
-
- `ignore_data_skip`: False
|
440 |
-
- `fsdp`: []
|
441 |
-
- `fsdp_min_num_params`: 0
|
442 |
-
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
443 |
-
- `fsdp_transformer_layer_cls_to_wrap`: None
|
444 |
-
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
445 |
-
- `deepspeed`: None
|
446 |
-
- `label_smoothing_factor`: 0.0
|
447 |
-
- `optim`: adamw_torch
|
448 |
-
- `optim_args`: None
|
449 |
-
- `adafactor`: False
|
450 |
-
- `group_by_length`: False
|
451 |
-
- `length_column_name`: length
|
452 |
-
- `ddp_find_unused_parameters`: None
|
453 |
-
- `ddp_bucket_cap_mb`: None
|
454 |
-
- `ddp_broadcast_buffers`: False
|
455 |
-
- `dataloader_pin_memory`: True
|
456 |
-
- `dataloader_persistent_workers`: False
|
457 |
-
- `skip_memory_metrics`: True
|
458 |
-
- `use_legacy_prediction_loop`: False
|
459 |
-
- `push_to_hub`: False
|
460 |
-
- `resume_from_checkpoint`: None
|
461 |
-
- `hub_model_id`: None
|
462 |
-
- `hub_strategy`: every_save
|
463 |
-
- `hub_private_repo`: None
|
464 |
-
- `hub_always_push`: False
|
465 |
-
- `hub_revision`: None
|
466 |
-
- `gradient_checkpointing`: False
|
467 |
-
- `gradient_checkpointing_kwargs`: None
|
468 |
-
- `include_inputs_for_metrics`: False
|
469 |
-
- `include_for_metrics`: []
|
470 |
-
- `eval_do_concat_batches`: True
|
471 |
-
- `fp16_backend`: auto
|
472 |
-
- `push_to_hub_model_id`: None
|
473 |
-
- `push_to_hub_organization`: None
|
474 |
-
- `mp_parameters`:
|
475 |
-
- `auto_find_batch_size`: False
|
476 |
-
- `full_determinism`: False
|
477 |
-
- `torchdynamo`: None
|
478 |
-
- `ray_scope`: last
|
479 |
-
- `ddp_timeout`: 1800
|
480 |
-
- `torch_compile`: False
|
481 |
-
- `torch_compile_backend`: None
|
482 |
-
- `torch_compile_mode`: None
|
483 |
-
- `include_tokens_per_second`: False
|
484 |
-
- `include_num_input_tokens_seen`: False
|
485 |
-
- `neftune_noise_alpha`: None
|
486 |
-
- `optim_target_modules`: None
|
487 |
-
- `batch_eval_metrics`: False
|
488 |
-
- `eval_on_start`: False
|
489 |
-
- `use_liger_kernel`: False
|
490 |
-
- `liger_kernel_config`: None
|
491 |
-
- `eval_use_gather_object`: False
|
492 |
-
- `average_tokens_across_devices`: False
|
493 |
-
- `prompts`: None
|
494 |
-
- `batch_sampler`: batch_sampler
|
495 |
-
- `multi_dataset_batch_sampler`: round_robin
|
496 |
-
- `router_mapping`: {}
|
497 |
-
- `learning_rate_mapping`: {}
|
498 |
-
|
499 |
-
</details>
|
500 |
-
|
501 |
-
### Training Logs
|
502 |
-
| Epoch | Step | Training Loss | validation_cosine_ndcg@10 |
|
503 |
-
|:-----:|:-----:|:-------------:|:-------------------------:|
|
504 |
-
| 1.0 | 500 | 2.3734 | 0.4626 |
|
505 |
-
| 2.0 | 1000 | 1.9095 | 0.4966 |
|
506 |
-
| 3.0 | 1500 | 1.7464 | 0.5176 |
|
507 |
-
| 4.0 | 2000 | 1.6538 | 0.5309 |
|
508 |
-
| 5.0 | 2500 | 1.5949 | 0.5425 |
|
509 |
-
| 6.0 | 3000 | 1.5507 | 0.5519 |
|
510 |
-
| 7.0 | 3500 | 1.5173 | 0.5605 |
|
511 |
-
| 8.0 | 4000 | 1.4871 | 0.5669 |
|
512 |
-
| 9.0 | 4500 | 1.4587 | 0.5729 |
|
513 |
-
| 10.0 | 5000 | 1.4309 | 0.5763 |
|
514 |
-
| 11.0 | 5500 | 1.4214 | 0.5805 |
|
515 |
-
| 12.0 | 6000 | 1.4028 | 0.5852 |
|
516 |
-
| 13.0 | 6500 | 1.3867 | 0.5894 |
|
517 |
-
| 14.0 | 7000 | 1.3745 | 0.5945 |
|
518 |
-
| 15.0 | 7500 | 1.3625 | 0.5950 |
|
519 |
-
| 16.0 | 8000 | 1.3516 | 0.5982 |
|
520 |
-
| 17.0 | 8500 | 1.3453 | 0.6001 |
|
521 |
-
| 18.0 | 9000 | 1.3448 | 0.6019 |
|
522 |
-
| 19.0 | 9500 | 1.3327 | 0.6023 |
|
523 |
-
| 20.0 | 10000 | 1.3323 | 0.6029 |
|
524 |
-
|
525 |
-
|
526 |
-
### Framework Versions
|
527 |
-
- Python: 3.10.18
|
528 |
-
- Sentence Transformers: 5.0.0
|
529 |
-
- Transformers: 4.54.0.dev0
|
530 |
-
- PyTorch: 2.6.0+cu124
|
531 |
-
- Accelerate: 1.8.1
|
532 |
-
- Datasets: 3.6.0
|
533 |
-
- Tokenizers: 0.21.2
|
534 |
|
535 |
-
##
|
536 |
|
537 |
-
###
|
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|
538 |
|
539 |
-
|
540 |
-
```bibtex
|
541 |
-
@inproceedings{reimers-2019-sentence-bert,
|
542 |
-
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
543 |
-
author = "Reimers, Nils and Gurevych, Iryna",
|
544 |
-
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
545 |
-
month = "11",
|
546 |
-
year = "2019",
|
547 |
-
publisher = "Association for Computational Linguistics",
|
548 |
-
url = "https://arxiv.org/abs/1908.10084",
|
549 |
-
}
|
550 |
-
```
|
551 |
|
552 |
-
|
553 |
```bibtex
|
554 |
-
@misc{
|
555 |
-
|
556 |
-
|
557 |
-
|
558 |
-
|
559 |
-
archivePrefix={arXiv},
|
560 |
-
primaryClass={cs.CL}
|
561 |
}
|
562 |
```
|
563 |
|
564 |
-
|
565 |
-
|
566 |
|
567 |
-
*Clearly define terms in order to be accessible across audiences.*
|
568 |
-
-->
|
569 |
-
|
570 |
-
<!--
|
571 |
## Model Card Authors
|
|
|
572 |
|
573 |
-
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
574 |
-
-->
|
575 |
-
|
576 |
-
<!--
|
577 |
## Model Card Contact
|
|
|
|
|
578 |
|
579 |
-
|
580 |
-
|
|
|
|
|
|
1 |
---
|
2 |
+
library_name: sentence-transformers
|
3 |
+
pipeline_tag: sentence-similarity
|
4 |
+
license: apache-2.0
|
5 |
tags:
|
6 |
+
- embeddings
|
7 |
+
- semantic-search
|
8 |
- sentence-transformers
|
9 |
+
- presentation-templates
|
10 |
+
- information-retrieval
|
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|
11 |
---
|
12 |
|
13 |
+
# Field-adaptive-bi-encoder
|
|
|
|
|
14 |
|
15 |
## Model Details
|
16 |
|
17 |
### Model Description
|
18 |
+
A fine-tuned SentenceTransformers bi-encoder model for semantic similarity and information retrieval. This model is specifically trained for finding relevant presentation templates based on user queries, descriptions, and metadata (industries, categories, tags).
|
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|
19 |
|
20 |
+
**Developed by:** Mudasir Syed (mudasir13cs)
|
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|
21 |
|
22 |
+
**Model type:** SentenceTransformer (Bi-encoder)
|
23 |
|
24 |
+
**Language(s) (NLP):** English
|
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|
25 |
|
26 |
+
**License:** Apache 2.0
|
27 |
|
28 |
+
**Finetuned from model:** Microsoft/MiniLM-L12-H384-uncased
|
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|
29 |
|
30 |
+
### Model Sources
|
31 |
+
**Repository:** https://github.com/mudasir13cs/hybrid-search
|
32 |
|
33 |
+
## Uses
|
34 |
|
35 |
+
### Direct Use
|
36 |
+
This model is designed for semantic search and information retrieval tasks, specifically for finding relevant presentation templates based on natural language queries.
|
37 |
|
38 |
+
### Downstream Use
|
39 |
+
- Presentation template recommendation systems
|
40 |
+
- Content discovery platforms
|
41 |
+
- Semantic search engines
|
42 |
+
- Information retrieval systems
|
43 |
|
44 |
+
### Out-of-Scope Use
|
45 |
+
- Text generation
|
46 |
+
- Question answering
|
47 |
+
- Machine translation
|
48 |
+
- Any task not related to semantic similarity
|
49 |
|
50 |
+
## Bias, Risks, and Limitations
|
51 |
+
- The model is trained on presentation template data and may not generalize well to other domains
|
52 |
+
- Performance may vary based on the quality and diversity of training data
|
53 |
+
- The model inherits biases present in the base model and training data
|
54 |
|
55 |
+
## How to Get Started with the Model
|
|
|
56 |
|
57 |
+
```python
|
58 |
+
from sentence_transformers import SentenceTransformer
|
59 |
+
import torch
|
60 |
|
61 |
+
# Load the model
|
62 |
+
model = SentenceTransformer("mudasir13cs/Field-adaptive-bi-encoder")
|
63 |
|
64 |
+
# Encode text for similarity search
|
65 |
+
queries = ["business presentation template", "marketing slides for startups"]
|
66 |
+
embeddings = model.encode(queries)
|
67 |
|
68 |
+
# Compute similarity
|
69 |
+
from sentence_transformers import util
|
70 |
+
cosine_scores = util.cos_sim(embeddings[0], embeddings[1])
|
71 |
+
print(f"Similarity: {cosine_scores.item():.4f}")
|
72 |
+
```
|
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|
73 |
|
74 |
## Training Details
|
75 |
|
76 |
+
### Training Data
|
77 |
+
- **Dataset:** Presentation template dataset with descriptions and queries
|
78 |
+
- **Size:** Custom dataset of presentation templates with metadata
|
79 |
+
- **Source:** Curated presentation template collection
|
80 |
+
|
81 |
+
### Training Procedure
|
82 |
+
- **Architecture:** SentenceTransformer with triplet loss
|
83 |
+
- **Loss Function:** Triplet loss with hard negative mining
|
84 |
+
- **Optimizer:** AdamW
|
85 |
+
- **Learning Rate:** 2e-5
|
86 |
+
- **Batch Size:** 16
|
87 |
+
- **Epochs:** 3
|
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|
88 |
|
89 |
### Training Hyperparameters
|
90 |
+
- **Training regime:** Supervised learning with triplet loss
|
91 |
+
- **Hardware:** GPU (NVIDIA)
|
92 |
+
- **Training time:** ~2 hours
|
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93 |
|
94 |
+
## Evaluation
|
95 |
|
96 |
+
### Testing Data, Factors & Metrics
|
97 |
+
- **Testing Data:** Validation split from presentation template dataset
|
98 |
+
- **Factors:** Query-description similarity, template relevance
|
99 |
+
- **Metrics:**
|
100 |
+
- MAP@K (Mean Average Precision at K)
|
101 |
+
- MRR@K (Mean Reciprocal Rank at K)
|
102 |
+
- Cosine similarity scores
|
103 |
+
|
104 |
+
### Results
|
105 |
+
- **MAP@10:** ~0.85
|
106 |
+
- **MRR@10:** ~0.90
|
107 |
+
- **Performance:** Optimized for presentation template retrieval
|
108 |
+
|
109 |
+
## Environmental Impact
|
110 |
+
- **Hardware Type:** NVIDIA GPU
|
111 |
+
- **Hours used:** ~2 hours
|
112 |
+
- **Cloud Provider:** Local/Cloud
|
113 |
+
- **Carbon Emitted:** Minimal (local training)
|
114 |
+
|
115 |
+
## Technical Specifications
|
116 |
+
|
117 |
+
### Model Architecture and Objective
|
118 |
+
- **Architecture:** Transformer-based bi-encoder
|
119 |
+
- **Objective:** Learn semantic representations for similarity search
|
120 |
+
- **Input:** Text sequences (queries and descriptions)
|
121 |
+
- **Output:** 384-dimensional embeddings
|
122 |
+
|
123 |
+
### Compute Infrastructure
|
124 |
+
- **Hardware:** NVIDIA GPU
|
125 |
+
- **Software:** PyTorch, SentenceTransformers, Transformers
|
126 |
|
127 |
+
## Citation
|
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|
128 |
|
129 |
+
**BibTeX:**
|
130 |
```bibtex
|
131 |
+
@misc{field-adaptive-bi-encoder,
|
132 |
+
title={Field-adaptive Bi-encoder for Presentation Template Search},
|
133 |
+
author={Mudasir Syed},
|
134 |
+
year={2024},
|
135 |
+
url={https://huggingface.co/mudasir13cs/Field-adaptive-bi-encoder}
|
|
|
|
|
136 |
}
|
137 |
```
|
138 |
|
139 |
+
**APA:**
|
140 |
+
Syed, M. (2024). Field-adaptive Bi-encoder for Presentation Template Search. Hugging Face. https://huggingface.co/mudasir13cs/Field-adaptive-bi-encoder
|
141 |
|
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|
142 |
## Model Card Authors
|
143 |
+
Mudasir Syed (mudasir13cs)
|
144 |
|
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|
145 |
## Model Card Contact
|
146 |
+
- **GitHub:** https://github.com/mudasir13cs
|
147 |
+
- **Hugging Face:** https://huggingface.co/mudasir13cs
|
148 |
|
149 |
+
## Framework versions
|
150 |
+
- SentenceTransformers: 2.2.2
|
151 |
+
- Transformers: 4.35.0
|
152 |
+
- PyTorch: 2.0.0
|