--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3012496 - loss:MatryoshkaLoss - loss:CachedMultipleNegativesRankingLoss base_model: google-bert/bert-base-uncased widget: - source_sentence: are the sequels better than the prequels? sentences: - '[''Automatically.'', ''When connected to car Bluetooth and,'', ''Manually.'']' - The prequels are also not scared to take risks, making movies which are very different from the original trilogy. The sequel saga, on the other hand, are technically better made films, the acting is more consistent, the CGI is better and the writing is stronger, however it falls down in many other places. - While both public and private sectors use budgets as a key planning tool, public bodies balance budgets, while private sector firms use budgets to predict operating results. The public sector budget matches expenditures on mandated assets and services with receipts of public money such as taxes and fees. - source_sentence: are there bbqs at lake leschenaultia? sentences: - Vestavia Hills. The hummingbird, or, el zunzún as they are often called in the Caribbean, have such a nickname because of their quick movements. The ruby-throated hummingbird, the most commonly seen hummingbird in Alabama, is the inspiration for this restaurant. - Common causes of abdominal tenderness Abdominal tenderness is generally a sign of inflammation or other acute processes in one or more organs. The organs are located around the tender area. Acute processes mean sudden pressure caused by something. For example, twisted or blocked organs can cause point tenderness. - ​Located on 168 hectares of nature reserve, Lake Leschenaultia is the perfect spot for a family day out in the Perth Hills. The Lake offers canoeing, swimming, walk and cycle trails, as well as picnic, BBQ and camping facilities. ... There are picnic tables set amongst lovely Wandoo trees. - source_sentence: how much folic acid should you take prenatal? sentences: - Folic acid is a pregnancy superhero! Taking a prenatal vitamin with the recommended 400 micrograms (mcg) of folic acid before and during pregnancy can help prevent birth defects of your baby's brain and spinal cord. Take it every day and go ahead and have a bowl of fortified cereal, too. - '[''You must be unemployed through no fault of your own, as defined by Virginia law.'', ''You must have earned at least a minimum amount in wages before you were unemployed.'', ''You must be able and available to work, and you must be actively seeking employment.'']' - Wallpaper is printed in batches of rolls. It is important to have the same batch number, to ensure colours match exactly. The batch number is usually located on the wallpaper label close to the pattern number. Remember batch numbers also apply to white wallpapers, as different batches can be different shades of white. - source_sentence: what is the difference between minerals and electrolytes? sentences: - 'North: Just head north of Junk Junction like so. South: Head below Lucky Landing. East: You''re basically landing between Lonely Lodge and the Racetrack. West: The sign is west of Snobby Shores.' - The fasting glucose tolerance test is the simplest and fastest way to measure blood glucose and diagnose diabetes. Fasting means that you have had nothing to eat or drink (except water) for 8 to 12 hours before the test. - In other words, the term “electrolyte” typically implies ionized minerals dissolved within water and beverages. Electrolytes are typically minerals, whereas minerals may or may not be electrolytes. - source_sentence: how can i download youtube videos with internet download manager? sentences: - '[''Go to settings and then click on extensions (top left side in chrome).'', ''Minimise your browser and open the location (folder) where IDM is installed. ... '', ''Find the file “IDMGCExt. ... '', ''Drag this file to your chrome browser and drop to install the IDM extension.'']' - Coca-Cola might rot your teeth and load your body with sugar and calories, but it's actually an effective and safe first line of treatment for some stomach blockages, researchers say. - To fix a disabled iPhone or iPad without iTunes, you have to erase your device. Click on the "Erase iPhone" option and confirm your selection. Wait for a while as the "Find My iPhone" feature will remotely erase your iOS device. Needless to say, it will also disable its lock. datasets: - sentence-transformers/gooaq pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 co2_eq_emissions: emissions: 249.86917485332245 energy_consumed: 0.6428296609055844 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 1.727 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: bert-base-uncased adapter finetuned on GooAQ pairs results: - task: type: information-retrieval name: Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: cosine_accuracy@1 value: 0.3 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.42 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.48 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.54 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.3 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.16 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.11600000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.066 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.14833333333333332 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.21 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.25666666666666665 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2866666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2612531493211831 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3718333333333333 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2163485410063536 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: cosine_accuracy@1 value: 0.48 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.78 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.82 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.92 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.48 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.4599999999999999 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.4159999999999999 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.39 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.04444293833661297 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.10924065240694858 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.14497857436843284 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.24069548747927993 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.45073427319400694 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6354682539682539 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3182747550673792 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: cosine_accuracy@1 value: 0.6 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.84 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.96 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.184 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09799999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.59 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8566666666666666 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9066666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7556216606985078 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.719190476190476 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.701651515151515 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: cosine_accuracy@1 value: 0.22 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.4 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.6 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.22 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.18 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09799999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.11441269841269841 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.21891269841269842 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3109126984126984 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.40793650793650793 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2963633422018188 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.33072222222222225 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.23341351928423923 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: cosine_accuracy@1 value: 0.64 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.74 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.82 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.84 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.64 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.31333333333333335 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.22399999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.11799999999999997 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.32 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.47 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.56 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.59 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5584295792789493 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7015 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.49543351785464007 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: cosine_accuracy@1 value: 0.22 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.46 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.54 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.68 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.22 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.15333333333333332 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.10800000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.068 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.22 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.46 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.54 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.68 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.44155458168172074 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3666904761904761 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.38140126670451624 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: cosine_accuracy@1 value: 0.32 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.44 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.46 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.32 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2866666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.244 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.17800000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.022867372385014545 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.051610132551984836 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.061993511339545566 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.07344138386002937 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.22405550472948219 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3782222222222222 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.08778657539162772 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: cosine_accuracy@1 value: 0.4 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.54 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.62 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.18 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.124 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07200000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.4 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.53 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.59 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.67 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5271006159134835 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4858809523809523 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4878346435046129 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: cosine_accuracy@1 value: 0.84 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.98 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.98 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.84 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.38666666666666655 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.23999999999999994 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.12999999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7573333333333333 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9286666666666668 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9359999999999999 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9793333333333334 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9154478750600358 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9053333333333333 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8889771382049948 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: cosine_accuracy@1 value: 0.3 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.36 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.54 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.68 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.3 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19200000000000003 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.142 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.06466666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.12466666666666669 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.19666666666666666 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2906666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2646043570275534 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3836031746031746 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.20582501612453505 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: cosine_accuracy@1 value: 0.16 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.52 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.72 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.16 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.17333333333333337 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14400000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.16 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.52 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.72 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.47137188069353025 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.36633333333333323 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3750999024240443 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: cosine_accuracy@1 value: 0.38 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.56 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.64 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.38 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07800000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.345 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.525 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.615 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.68 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.521095291928473 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4848333333333332 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4707221516167083 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: cosine_accuracy@1 value: 0.3673469387755102 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8571428571428571 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9387755102040817 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.3673469387755102 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.4965986394557823 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.4489795918367347 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.39387755102040817 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.03066633506656198 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.1123508290418132 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.1616156991422983 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2674040762687923 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.42905651691216934 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6237204405571752 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.32876348596122706 name: Cosine Map@100 - task: type: nano-beir name: Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: cosine_accuracy@1 value: 0.40210361067503925 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6074725274725276 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6891365777080062 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7630769230769231 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.40210361067503925 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26691784406070124 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.2093061224489796 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.14706750392464676 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.247517129041094 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.38926520351898297 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.4577308064048442 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5286777529906109 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.47051450989545496 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.519487042436022 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.399348617561261 name: Cosine Map@100 --- # bert-base-uncased adapter finetuned on GooAQ pairs This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) 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:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) - **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': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tomaarsen/bert-base-uncased-gooaq-peft") # Run inference sentences = [ 'how can i download youtube videos with internet download manager?', "['Go to settings and then click on extensions (top left side in chrome).', 'Minimise your browser and open the location (folder) where IDM is installed. ... ', 'Find the file “IDMGCExt. ... ', 'Drag this file to your chrome browser and drop to install the IDM extension.']", "Coca-Cola might rot your teeth and load your body with sugar and calories, but it's actually an effective and safe first line of treatment for some stomach blockages, researchers say.", ] 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] ``` ## Evaluation ### Metrics #### Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | |:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------| | cosine_accuracy@1 | 0.3 | 0.48 | 0.6 | 0.22 | 0.64 | 0.22 | 0.32 | 0.4 | 0.84 | 0.3 | 0.16 | 0.38 | 0.3673 | | cosine_accuracy@3 | 0.42 | 0.78 | 0.84 | 0.4 | 0.74 | 0.46 | 0.44 | 0.54 | 0.98 | 0.36 | 0.52 | 0.56 | 0.8571 | | cosine_accuracy@5 | 0.48 | 0.82 | 0.9 | 0.5 | 0.82 | 0.54 | 0.46 | 0.62 | 0.98 | 0.54 | 0.72 | 0.64 | 0.9388 | | cosine_accuracy@10 | 0.54 | 0.92 | 0.96 | 0.6 | 0.84 | 0.68 | 0.5 | 0.7 | 1.0 | 0.68 | 0.8 | 0.7 | 1.0 | | cosine_precision@1 | 0.3 | 0.48 | 0.6 | 0.22 | 0.64 | 0.22 | 0.32 | 0.4 | 0.84 | 0.3 | 0.16 | 0.38 | 0.3673 | | cosine_precision@3 | 0.16 | 0.46 | 0.28 | 0.18 | 0.3133 | 0.1533 | 0.2867 | 0.18 | 0.3867 | 0.2 | 0.1733 | 0.2 | 0.4966 | | cosine_precision@5 | 0.116 | 0.416 | 0.184 | 0.14 | 0.224 | 0.108 | 0.244 | 0.124 | 0.24 | 0.192 | 0.144 | 0.14 | 0.449 | | cosine_precision@10 | 0.066 | 0.39 | 0.098 | 0.098 | 0.118 | 0.068 | 0.178 | 0.072 | 0.13 | 0.142 | 0.08 | 0.078 | 0.3939 | | cosine_recall@1 | 0.1483 | 0.0444 | 0.59 | 0.1144 | 0.32 | 0.22 | 0.0229 | 0.4 | 0.7573 | 0.0647 | 0.16 | 0.345 | 0.0307 | | cosine_recall@3 | 0.21 | 0.1092 | 0.8 | 0.2189 | 0.47 | 0.46 | 0.0516 | 0.53 | 0.9287 | 0.1247 | 0.52 | 0.525 | 0.1124 | | cosine_recall@5 | 0.2567 | 0.145 | 0.8567 | 0.3109 | 0.56 | 0.54 | 0.062 | 0.59 | 0.936 | 0.1967 | 0.72 | 0.615 | 0.1616 | | cosine_recall@10 | 0.2867 | 0.2407 | 0.9067 | 0.4079 | 0.59 | 0.68 | 0.0734 | 0.67 | 0.9793 | 0.2907 | 0.8 | 0.68 | 0.2674 | | **cosine_ndcg@10** | **0.2613** | **0.4507** | **0.7556** | **0.2964** | **0.5584** | **0.4416** | **0.2241** | **0.5271** | **0.9154** | **0.2646** | **0.4714** | **0.5211** | **0.4291** | | cosine_mrr@10 | 0.3718 | 0.6355 | 0.7192 | 0.3307 | 0.7015 | 0.3667 | 0.3782 | 0.4859 | 0.9053 | 0.3836 | 0.3663 | 0.4848 | 0.6237 | | cosine_map@100 | 0.2163 | 0.3183 | 0.7017 | 0.2334 | 0.4954 | 0.3814 | 0.0878 | 0.4878 | 0.889 | 0.2058 | 0.3751 | 0.4707 | 0.3288 | #### Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [NanoBEIREvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.4021 | | cosine_accuracy@3 | 0.6075 | | cosine_accuracy@5 | 0.6891 | | cosine_accuracy@10 | 0.7631 | | cosine_precision@1 | 0.4021 | | cosine_precision@3 | 0.2669 | | cosine_precision@5 | 0.2093 | | cosine_precision@10 | 0.1471 | | cosine_recall@1 | 0.2475 | | cosine_recall@3 | 0.3893 | | cosine_recall@5 | 0.4577 | | cosine_recall@10 | 0.5287 | | **cosine_ndcg@10** | **0.4705** | | cosine_mrr@10 | 0.5195 | | cosine_map@100 | 0.3993 | ## Training Details ### Training Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 training samples * Columns: question and answer * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | answer | |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what is the difference between broilers and layers? | An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well. | | what is the difference between chronological order and spatial order? | As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time. | | is kamagra same as viagra? | Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person. | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "CachedMultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 evaluation samples * Columns: question and answer * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | answer | |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | how do i program my directv remote with my tv? | ['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.'] | | are rodrigues fruit bats nocturnal? | Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night. | | why does your heart rate increase during exercise bbc bitesize? | During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it. | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "CachedMultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 1024 - `per_device_eval_batch_size`: 1024 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `seed`: 12 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 1024 - `per_device_eval_batch_size`: 1024 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 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`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 12 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 | |:------:|:----:|:-------------:|:---------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:| | 0 | 0 | - | - | 0.1046 | 0.2182 | 0.1573 | 0.0575 | 0.2597 | 0.1602 | 0.0521 | 0.0493 | 0.7310 | 0.1320 | 0.2309 | 0.1240 | 0.0970 | 0.1826 | | 0.0010 | 1 | 28.4479 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0256 | 25 | 27.0904 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0512 | 50 | 19.016 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0768 | 75 | 12.2306 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1024 | 100 | 9.0613 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1279 | 125 | 7.393 | 3.7497 | 0.2787 | 0.4840 | 0.7029 | 0.2589 | 0.5208 | 0.4094 | 0.2117 | 0.4526 | 0.9042 | 0.2503 | 0.5280 | 0.4922 | 0.4132 | 0.4544 | | 0.1535 | 150 | 6.6613 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1791 | 175 | 6.1911 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2047 | 200 | 5.9305 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2303 | 225 | 5.6825 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2559 | 250 | 5.5326 | 2.8771 | 0.2867 | 0.4619 | 0.7333 | 0.2835 | 0.5549 | 0.4056 | 0.2281 | 0.4883 | 0.9137 | 0.2555 | 0.5114 | 0.5220 | 0.4298 | 0.4673 | | 0.2815 | 275 | 5.1671 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3071 | 300 | 5.2006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3327 | 325 | 5.0447 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3582 | 350 | 4.9647 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3838 | 375 | 4.8521 | 2.5709 | 0.2881 | 0.4577 | 0.7438 | 0.2909 | 0.5712 | 0.4093 | 0.2273 | 0.5141 | 0.9008 | 0.2668 | 0.5117 | 0.5253 | 0.4331 | 0.4723 | | 0.4094 | 400 | 4.8423 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4350 | 425 | 4.7472 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4606 | 450 | 4.6527 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4862 | 475 | 4.61 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5118 | 500 | 4.5451 | 2.4136 | 0.2786 | 0.4464 | 0.7485 | 0.2961 | 0.5638 | 0.4368 | 0.2269 | 0.5125 | 0.8998 | 0.2680 | 0.4938 | 0.5341 | 0.4383 | 0.4726 | | 0.5374 | 525 | 4.5357 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5629 | 550 | 4.481 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5885 | 575 | 4.4669 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6141 | 600 | 4.3886 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6397 | 625 | 4.2929 | 2.3091 | 0.2639 | 0.4475 | 0.7521 | 0.3095 | 0.5619 | 0.4448 | 0.2244 | 0.5178 | 0.9102 | 0.2655 | 0.4809 | 0.5253 | 0.4351 | 0.4722 | | 0.6653 | 650 | 4.2558 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6909 | 675 | 4.3228 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7165 | 700 | 4.2496 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7421 | 725 | 4.2304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7677 | 750 | 4.224 | 2.2440 | 0.2628 | 0.4514 | 0.7387 | 0.3028 | 0.5522 | 0.4313 | 0.2253 | 0.5266 | 0.9211 | 0.2675 | 0.4929 | 0.5232 | 0.4351 | 0.4716 | | 0.7932 | 775 | 4.2821 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8188 | 800 | 4.2686 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8444 | 825 | 4.1657 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8700 | 850 | 4.2297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8956 | 875 | 4.1709 | 2.2142 | 0.2685 | 0.4520 | 0.7569 | 0.2930 | 0.5625 | 0.4486 | 0.2229 | 0.5280 | 0.9153 | 0.2601 | 0.4862 | 0.5199 | 0.4334 | 0.4729 | | 0.9212 | 900 | 4.0771 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9468 | 925 | 4.1492 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9724 | 950 | 4.2074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9980 | 975 | 4.0993 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0 | 977 | - | - | 0.2613 | 0.4507 | 0.7556 | 0.2964 | 0.5584 | 0.4416 | 0.2241 | 0.5271 | 0.9154 | 0.2646 | 0.4714 | 0.5211 | 0.4291 | 0.4705 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.643 kWh - **Carbon Emitted**: 0.250 kg of CO2 - **Hours Used**: 1.727 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.4.0.dev0 - Transformers: 4.46.2 - PyTorch: 2.5.0+cu121 - Accelerate: 0.35.0.dev0 - Datasets: 2.20.0 - Tokenizers: 0.20.3 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### 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} } ``` #### CachedMultipleNegativesRankingLoss ```bibtex @misc{gao2021scaling, title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, year={2021}, eprint={2101.06983}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```