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--- |
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library_name: sentence-transformers |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:1830 |
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- loss:TripletLoss |
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widget: |
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- source_sentence: 100% Cotton Throw Blanket for Couch Sofa Bed Outdoors Hypoallergenic |
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83"x70" Brown |
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sentences: |
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- '80''s Lace Headband Costume Accessories for 80s Theme Party, No Headache Neon |
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Lace Bow Headband, Set of 4 |
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Product Description Read more ' |
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- ' |
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' |
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- 'Our Family Recipes Journal |
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' |
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- source_sentence: '"Belkin QODE Ultimate Pro Keyboard Case for iPad Air 2 White"' |
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sentences: |
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- 'Detectorcatty 18 inch Foil Star Shape Balloon Helium Metallic Birthday Summer |
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Outdoor Party Wedding Decor Air Mylar Balloon |
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' |
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- ' |
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' |
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- 'iPad Keyboard Case for iPad 2018 (6th Gen) - iPad 2017 (5th Gen) - iPad Pro 9.7 |
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- iPad Air 2 & 1 - Thin & Light - 360 Rotatable - Wireless/BT - Backlit 10 Color |
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- iPad Case with Keyboard (Silver) |
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Product Description Compatibility: iPad Air (Wi-Fi Only) | A1474 : MD785LL/A, |
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MD786LL/A, MD787LL/A, MD898LL/A, MD788LL/A, MD789LL/A, MD790LL/A, ME906LL/A iPad |
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Air (Wi-Fi/Cellular) | A1475 : ME991LL/A, MF003LL/A, MF009LL/A, MF015LL/A, ME997LL/A, |
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MF529LL/A, MF012LL/A, MF018LL/A, MF020LL/A, MF024LL/A, MF026LL/A, MF028LL/A, MF021LL/A, |
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MF025LL/A, MF027LL/A, MF029LL/A, MF496LL/A, MF520LL/A, MF534LL/A, MF558LL/A, MF502LL/A, |
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MF527LL/A, MF539LL/A, MF563LL/A, ME993LL/A, MF004LL/A, MF010LL/A, MF016LL/A, ME999LL/A, |
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MF532LL/A, MF013LL/A, MF019LL/A iPad Air (Wi-Fi/TD-LTE - China) | A1476 : MD785CH/A, |
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MD768CH/A, MD787CH/A, ME898CH/A, MD788CH/A, MD789CH/A, MD790CH/A, ME906CH/A iPad |
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Air 2 (Wi-Fi Only) | A1566 : MGLW2LL/A, MGKM2LL/A, MGTY2LL/A, MH0W2LL/A, MH182LL/A, |
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MH1J2LL/A, MGL12LL/A, MGKL2LL/A, MGTX2LL/A, MNV62LL/A, MNV72LL/A, MNV22LL/A iPad |
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Air 2 (Wi-Fi/Cellular) | A1567 : MH2V2LL/A, MH2N2LL/A, MH322LL/A, MH2W2LL/A, MH2P2LL/A, |
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MH332LL/A, MH2U2LL/A, MH2M2LL/A, MH312LL/A, MNW22LL/A, MNW32LL/A, MNW12LL/A iPad |
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Pro 9.7" (Wi-Fi Only) | A1673 : MLMP2LL/A, MLMW2LL/A, MLN02LL/A, MLMN2LL/A, MLMV2LL/A, |
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MLMY2LL/A, MLMQ2LL/A, MLMX2LL/A, MLN12LL/A, MM172LL/A, MM192LL/A, MM1A2LL/A iPad |
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Pro 9.7" (Wi-Fi/Cellular) | A1674 : MLPX2LL/A, MLQ42LL/A, MLQ72LL/A, MLPW2LL/A, |
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MLQ32LL/A, MLQ62LL/A, MLPY2LL/A, MLQ52LL/A, MLQ82LL/A, MLYJ2LL/A, MLYL2LL/A, MLYM2LL/A |
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iPad 9.7" 5th Gen (Wi-Fi Only) | A1822 : MP2G2LL/A, MP2J2LL/A, MPGT2LL/A, MPGW2LL/A, |
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MP2F2LL/A, MP2H2LL/A iPad 9.7" 5th Gen (Wi-Fi/Cellular) | A1823 : MP252LL/A, MP2E2LL/A, |
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MPGA2LL/A, MPGC2LL/A, MP242LL/A, MP2D2LL/A iPad 9.7" 6th Gen (Wi-Fi Only) | A1893 |
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: MR7G2LL/A, MR7K2LL/A, MRJN2LL/A, MRJP2LL/A, MR7F2LL/A, MR7J2LL/A iPad 9.7" 6th |
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Gen (Wi-Fi/Cellular) | A1954 : MR702LL/A, MR7D2LL/A, MRM52LL/A, MRM82LL/A, MR6Y2LL/A, |
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MR7C2LL/A ' |
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- source_sentence: onlypuff Pocket Shirts for Women Casual |
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sentences: |
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- 'Womens Hoodies Zip Up Sweatshirt Lightweight Loose Jackets with Pockets Rose |
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Red XL |
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' |
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- 'WOODWORKING FOR BEGINNERS: DIY Project Plans, Step-by-Step Guide to Learn the |
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Best Techniques, Tools, Safety Precautions and Tips to Start Your First Projects |
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with Illustrations and Much More! |
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' |
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- 'Roorsily Transparent Face Covering Unisex Face_Shields Integrated Protective |
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Glasses Goggles, Male and Female Transparent Face Covering, Kitchen Anti-Sputum, |
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Sneeze, and Oil Splash Protection Panels |
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' |
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- source_sentence: '"XR Extinction Rebellion Rebel For Life T-Shirt"' |
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sentences: |
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- 'Raw Paws Soft-Tip Pet Grooming Gloves for Dogs & Cats - Cat Deshedding Glove |
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- Cat Gloves for Grooming - Horse Grooming Gloves - Dog Deshedding Glove - Cat |
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Grooming Glove, Dog Brush Glove for Shedding |
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Product Description Raw Paws dog and cat glove brush for shedding and grooming |
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is the perfect solution for your pet hair needs! Regular brushing reduces shedding |
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and increases blood circulation, which supports healthy skin and a shiny coat. |
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Our glove brush for dogs is gentle enough to feel like a massage to your pet, |
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but effective enough to remove excess hair and debris. This is a product you and |
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your furry friends will both appreciate! Our pet mitt makes a great pet hair removal |
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tool for pets who may not love traditional brushes. You can also use them as grooming |
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gloves for rabbits, ferrets and horses, in addition to cats and dogs. Simply put |
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on the hair remover mitten, tighten the wrist strap to fit your hand, and pet |
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your animal as you normally would. Once the pet brush glove is full of fur, easily |
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peel the accumulation of hair from the mitt in one heap and throw it away. Spend |
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quality time with your pets while keeping them and your house clean with Raw Paws |
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grooming mitts! Raw Paws Pet Food is a family-owned business that believes the |
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best chance of having a happy pet is through high quality nutrition and pet supplies. |
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That''s why we use only pet-safe materials to make our cat gloves for grooming. |
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When you shop with Raw Paws Pet Food you can have peace of mind knowing that you''re |
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using high quality pet shedding grooming gloves on your pet! Pet Shedding Grooming |
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Gloves FROM OUR FAMILY TO YOURS: We only use responsible and ethical sources, |
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so you get the highest-quality products! We hand inspect and ship these pet shedding |
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gloves from our own Indianapolis warehouse! EXCELLENT BATH TIME TOOLS: : These |
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mitts make great dog bath brush gloves! Use a pet grooming mitt prior to bath |
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time to get rid of loose and dirt. During their bath, relax your pet while getting |
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them squeaky clean with our dog brushing gloves! PERFECT FOR ALL YOUR PETS: Raw |
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Paws animal grooming gloves will work on almost any furry friend in your home |
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or on your farm. Use as a ferret brush, rabbit grooming glove or grooming mitt |
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for horses. Great for pets with long hair, short hair, curly locks or coarse strands! |
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Reduce Shedding Great for Bathing Adjustable Wrist Strap 259 Silicone Tips Gently |
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Massage Your Pet Get Hard-to-Reach Places Support Healthy Skin & Coat For Dogs, |
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Cats, Rabbits, Horses Read more We are a pet-rescuing and pet-loving organization |
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that is committed to green shipping practices as well as maintaining an office |
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and warehouse environment that is friendly to the earth. We are a small, family |
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owned American company. We support shelters, charities and non-profits. We love |
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your pets too! WHY RAW PAWS PET FOOD? I started Raw Paws Pet Food to make it practical, |
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affordable and accessible for pet parents to provide their dogs and cats with |
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healthy pet food, treats, chews, whole food toppers and supplements. At Raw Paws |
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Pet Food, we believe that the best chance of having a happy, healthy pet is through |
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high quality nutrition. That''s why we use only fresh, all-natural ingredients |
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sourced from responsible and ethical farms. ~Shelli McDonald , Raw Paws Pet Food |
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Owner When you shop with Raw Paws Pet Food, you can have peace of mind knowing |
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that you''re giving your pet only the best. We strive to ensure that you and your |
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pet are truly happy. We stand behind our brand, and value your business. Meeting |
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your expectations is our #1 priority. Read more ' |
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- 'Mens Floral Chakras V-dye T-Shirt, 3XL Vee Rainbow |
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' |
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- ' |
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' |
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- source_sentence: 100% Cotton Throw Blanket for Couch Sofa Bed Outdoors Hypoallergenic |
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83"x70" Brown |
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sentences: |
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- 'Chapstick Key Chain Holder with Clip Portable Lip Balm Cloth Holder Case |
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' |
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- 'Hetao 100% Cotton Handmade Crochet Round Tablecloth Doilies Lace Table Covers,Beige, |
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27 Inch |
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' |
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- 'Life Clothing Co. Womens Tops Jade Tie Dye Hoodie (XL) |
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' |
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--- |
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# SentenceTransformer |
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This is a [sentence-transformers](https://www.SBERT.net) model trained. 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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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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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### Model Sources |
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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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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel |
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(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}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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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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'100% Cotton Throw Blanket for Couch Sofa Bed Outdoors Hypoallergenic 83"x70" Brown', |
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'Hetao 100% Cotton Handmade Crochet Round Tablecloth Doilies Lace Table Covers,Beige, 27 Inch\n', |
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'Life Clothing Co. Womens Tops Jade Tie Dye Hoodie (XL)\n', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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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.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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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 Dataset |
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#### Unnamed Dataset |
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* Size: 1,830 training samples |
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* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | sentence_2 | |
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|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 16.37 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 161.41 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 74.05 tokens</li><li>max: 512 tokens</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | sentence_2 | |
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|:----------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>"acrylic lollipop holder cake pop stand with sticks, bags, and twist ties"</code> | <code>Zealax 15pcs Treat Bags Gold Polka Dot Print Drawstring Plastic Party Favors for Cookie Roasting Treat Candy Buffet Gift Wrapping Goodies Package, 4.6 inches x 6.7 inches<br></code> | <code>Twinkle Star Solid Brass Heavy Duty Adjustable Twist Hose Nozzle Jet Sweeper Nozzle, TWIS3231<br>Product Description Shut Off Valve Shut Off Valve Adjustable Hose Nozzle Adjustable Twist Hose Nozzle Jet Sweeper Screw Threads 3/4" 3/4" 3/4" 3/4" 3/4" Shut-Off Valve YES YES YES YES NO Material Brass Brass Brass Brass Brass Package Includes 2 Pack 1 Pack 1 Pack 2 Pack 2 Pack Garden Hose Quick Connect Set Garden Hose Quick Connect Set Hose Caps Double Female Swivel Connectors Double Male Quick Connectors Screw Threads 3/4" 3/4" 3/4" 3/4" 3/4" Material Aluminum Brass Brass Brass Brass Package Includes 4 Sets 4 Sets 4 Pack 2 Pack 2 Pack Specifications: Body Material: Brass Package Includes: 1 x adjustable nozzle, 1 x jet sweeper nozzle. Twinkle Star Solid Brass Heavy Duty Adjustable Twist Hose Nozzle Jet Sweeper Nozzle Heavy-duty solid brass construction. With 4 holes at the tip for maximum pressure and water flow, fitted with O-ring seals at the back and front to prevent any leaks. Twisting barrel to adjusts water from a fine mist to a powerful jet stream. Fits standard garden hose, great for watering gardens, car washing, deck, siding & driveway cleaning and more. Notes: 1. Please choose the correct hose size. 2. To prevent leakage, make sure the jet has rubber ring. 3. If water leaks after a long period of use, please replace with a new washer. Read more Adjustable jet rotates from a light stream to powerful stream. Heavy duty brass 3/4”female thread. Solid brass integral inner core, anti - damage, anti - rust, anti - leakage, durable. Read more </code> | |
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| <code>NFL Women's OTS Fleece Hoodie</code> | <code>Ultra Game NFL womens Fleece Hoodie Pullover Sweatshirt Tie Neck<br></code> | <code>Womens Antler Evolution Whitetail Tee Short Sleeve<br></code> | |
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| <code>"Belkin QODE Ultimate Pro Keyboard Case for iPad Air 2 White"</code> | <code>iPad Pro Guide<br></code> | <code>Under Armour Boys' Prototype Short<br>From the manufacturer Read more Read more Read more </code> | |
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* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: |
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```json |
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{ |
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"distance_metric": "TripletDistanceMetric.EUCLIDEAN", |
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"triplet_margin": 25.0 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 54 |
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- `per_device_eval_batch_size`: 54 |
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- `num_train_epochs`: 5 |
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- `multi_dataset_batch_sampler`: round_robin |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 54 |
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- `per_device_eval_batch_size`: 54 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1 |
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- `num_train_epochs`: 5 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `eval_use_gather_object`: False |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
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</details> |
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.1.1 |
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- Transformers: 4.44.2 |
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- PyTorch: 2.4.1+cu121 |
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- Accelerate: 0.34.2 |
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- Datasets: 3.0.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### TripletLoss |
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```bibtex |
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@misc{hermans2017defense, |
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title={In Defense of the Triplet Loss for Person Re-Identification}, |
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author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, |
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year={2017}, |
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eprint={1703.07737}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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