--- 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: 242.52371141034885 energy_consumed: 0.623932244779674 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: 1.619 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: 0.24 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.42 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.46 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.56 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.24 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.15999999999999998 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.10800000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.13166666666666665 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.20833333333333337 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.24166666666666664 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.29666666666666663 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.25516520961338873 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3378809523809523 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.20756281994556017 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: cosine_accuracy@1 value: 0.54 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.84 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.92 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.54 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.4866666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.4440000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.3899999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.046781664425339056 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.11117774881295754 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.15829952609979633 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2554819210350403 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4644109757573673 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6797460317460318 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3253011706807197 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: cosine_accuracy@1 value: 0.54 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.92 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.54 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2733333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.184 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09599999999999997 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.53 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7766666666666666 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8566666666666666 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8866666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7348538316509182 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6961904761904762 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6788071339639872 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: cosine_accuracy@1 value: 0.24 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: 0.24 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.16 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08800000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.11474603174603175 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.22874603174603172 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3166031746031746 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.3986031746031745 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2925721974861802 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3385 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2372091627126374 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: cosine_accuracy@1 value: 0.6 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.68 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.74 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.88 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2866666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.192 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.118 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.3 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.43 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.48 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.59 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5291588954628265 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6639365079365079 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.45230644038161627 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: cosine_accuracy@1 value: 0.28 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.48 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.54 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.66 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.28 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.16 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.10800000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.066 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.28 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.48 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.54 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.66 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.46795689507567784 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4079126984126984 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.42763462709531985 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: 0.48 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.56 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.32 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.30666666666666664 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.244 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.184 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.02092621665706462 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.053426190783308986 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.06393651269284006 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.08045448545888809 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.23067635403503162 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.39788888888888885 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.09661097314535905 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: cosine_accuracy@1 value: 0.38 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: 0.74 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.38 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.18 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.128 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07600000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.38 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.51 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.71 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5386606354769653 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.490547619047619 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.48961052316839493 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: 0.94 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: 0.24799999999999997 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.12999999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7573333333333332 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.912 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.946 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9793333333333334 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9157663307482551 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9009999999999999 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8893741502029173 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: cosine_accuracy@1 value: 0.26 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.46 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.68 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.26 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.20666666666666664 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.184 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.126 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.054000000000000006 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.12866666666666668 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.18966666666666668 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.25866666666666666 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.24181947685643387 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3803571428571429 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.18652061021747493 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: 0.58 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.74 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.84 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.16 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.19333333333333336 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14800000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08399999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.16 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.58 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.74 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.84 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5045313323048141 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3963333333333333 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.40074428294573644 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: cosine_accuracy@1 value: 0.42 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.58 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.62 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.64 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.42 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.20666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07600000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.4 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.56 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.605 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.64 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5380316349319392 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5056666666666666 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5079821472790408 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: cosine_accuracy@1 value: 0.4489795918367347 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8979591836734694 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9183673469387755 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9795918367346939 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4489795918367347 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.4965986394557823 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.45714285714285713 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.38979591836734706 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.03475887574057735 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.11109807516506923 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.1656210426064535 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2684807614936963 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.43233093716838594 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6532555879494653 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.33493945959592186 name: Cosine Map@100 - task: type: nano-beir name: Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: cosine_accuracy@1 value: 0.4053061224489796 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6213814756671899 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6891051805337519 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7676609105180533 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4053061224489796 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2694819466248038 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.20962637362637365 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.14567660910518054 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.24693944527453943 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.3915472856287718 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.4541123273847895 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5280272058403178 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4727642081975526 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5268627619545987 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.40266180779497585 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") # 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 | 0.24 | 0.54 | 0.54 | 0.24 | 0.6 | 0.28 | 0.32 | 0.38 | 0.84 | 0.26 | 0.16 | 0.42 | 0.449 | | cosine_accuracy@3 | 0.42 | 0.8 | 0.82 | 0.4 | 0.68 | 0.48 | 0.48 | 0.54 | 0.94 | 0.46 | 0.58 | 0.58 | 0.898 | | cosine_accuracy@5 | 0.46 | 0.84 | 0.9 | 0.5 | 0.74 | 0.54 | 0.5 | 0.62 | 0.98 | 0.6 | 0.74 | 0.62 | 0.9184 | | cosine_accuracy@10 | 0.56 | 0.92 | 0.92 | 0.6 | 0.88 | 0.66 | 0.56 | 0.74 | 1.0 | 0.68 | 0.84 | 0.64 | 0.9796 | | cosine_precision@1 | 0.24 | 0.54 | 0.54 | 0.24 | 0.6 | 0.28 | 0.32 | 0.38 | 0.84 | 0.26 | 0.16 | 0.42 | 0.449 | | cosine_precision@3 | 0.16 | 0.4867 | 0.2733 | 0.16 | 0.2867 | 0.16 | 0.3067 | 0.18 | 0.3867 | 0.2067 | 0.1933 | 0.2067 | 0.4966 | | cosine_precision@5 | 0.108 | 0.444 | 0.184 | 0.14 | 0.192 | 0.108 | 0.244 | 0.128 | 0.248 | 0.184 | 0.148 | 0.14 | 0.4571 | | cosine_precision@10 | 0.07 | 0.39 | 0.096 | 0.088 | 0.118 | 0.066 | 0.184 | 0.076 | 0.13 | 0.126 | 0.084 | 0.076 | 0.3898 | | cosine_recall@1 | 0.1317 | 0.0468 | 0.53 | 0.1147 | 0.3 | 0.28 | 0.0209 | 0.38 | 0.7573 | 0.054 | 0.16 | 0.4 | 0.0348 | | cosine_recall@3 | 0.2083 | 0.1112 | 0.7767 | 0.2287 | 0.43 | 0.48 | 0.0534 | 0.51 | 0.912 | 0.1287 | 0.58 | 0.56 | 0.1111 | | cosine_recall@5 | 0.2417 | 0.1583 | 0.8567 | 0.3166 | 0.48 | 0.54 | 0.0639 | 0.6 | 0.946 | 0.1897 | 0.74 | 0.605 | 0.1656 | | cosine_recall@10 | 0.2967 | 0.2555 | 0.8867 | 0.3986 | 0.59 | 0.66 | 0.0805 | 0.71 | 0.9793 | 0.2587 | 0.84 | 0.64 | 0.2685 | | **cosine_ndcg@10** | **0.2552** | **0.4644** | **0.7349** | **0.2926** | **0.5292** | **0.468** | **0.2307** | **0.5387** | **0.9158** | **0.2418** | **0.5045** | **0.538** | **0.4323** | | cosine_mrr@10 | 0.3379 | 0.6797 | 0.6962 | 0.3385 | 0.6639 | 0.4079 | 0.3979 | 0.4905 | 0.901 | 0.3804 | 0.3963 | 0.5057 | 0.6533 | | cosine_map@100 | 0.2076 | 0.3253 | 0.6788 | 0.2372 | 0.4523 | 0.4276 | 0.0966 | 0.4896 | 0.8894 | 0.1865 | 0.4007 | 0.508 | 0.3349 | #### 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 | 0.4053 | | cosine_accuracy@3 | 0.6214 | | cosine_accuracy@5 | 0.6891 | | cosine_accuracy@10 | 0.7677 | | cosine_precision@1 | 0.4053 | | cosine_precision@3 | 0.2695 | | cosine_precision@5 | 0.2096 | | cosine_precision@10 | 0.1457 | | cosine_recall@1 | 0.2469 | | cosine_recall@3 | 0.3915 | | cosine_recall@5 | 0.4541 | | cosine_recall@10 | 0.528 | | **cosine_ndcg@10** | **0.4728** | | cosine_mrr@10 | 0.5269 | | cosine_map@100 | 0.4027 | ## 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 | 28.4268 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0256 | 25 | 24.7252 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0512 | 50 | 13.3628 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0768 | 75 | 7.843 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1024 | 100 | 5.7393 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1279 | 125 | 4.6576 | 2.3368 | 0.2890 | 0.4610 | 0.7408 | 0.2882 | 0.5446 | 0.4091 | 0.2179 | 0.4664 | 0.9079 | 0.2394 | 0.5433 | 0.5003 | 0.4318 | 0.4646 | | 0.1535 | 150 | 4.0846 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1791 | 175 | 3.7129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2047 | 200 | 3.4899 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2303 | 225 | 3.3263 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2559 | 250 | 3.2013 | 1.6545 | 0.2622 | 0.4744 | 0.7456 | 0.2934 | 0.5371 | 0.4326 | 0.2290 | 0.5157 | 0.9130 | 0.2577 | 0.5189 | 0.5155 | 0.4302 | 0.4712 | | 0.2815 | 275 | 2.9109 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3071 | 300 | 2.9064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3327 | 325 | 2.8215 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3582 | 350 | 2.7893 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3838 | 375 | 2.6663 | 1.4146 | 0.2629 | 0.4657 | 0.7330 | 0.2853 | 0.5299 | 0.4346 | 0.2311 | 0.5216 | 0.9172 | 0.2513 | 0.5133 | 0.5429 | 0.4287 | 0.4706 | | 0.4094 | 400 | 2.6672 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4350 | 425 | 2.5587 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4606 | 450 | 2.5001 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4862 | 475 | 2.4476 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5118 | 500 | 2.4127 | 1.2843 | 0.2565 | 0.4668 | 0.7289 | 0.2838 | 0.5392 | 0.4599 | 0.2284 | 0.5238 | 0.9021 | 0.2416 | 0.4971 | 0.5349 | 0.4320 | 0.4688 | | 0.5374 | 525 | 2.414 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5629 | 550 | 2.3723 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5885 | 575 | 2.3418 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6141 | 600 | 2.2862 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6397 | 625 | 2.207 | 1.2078 | 0.2613 | 0.4542 | 0.7382 | 0.2817 | 0.5230 | 0.4664 | 0.2282 | 0.5266 | 0.9095 | 0.2453 | 0.5127 | 0.5414 | 0.4239 | 0.4702 | | 0.6653 | 650 | 2.2305 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6909 | 675 | 2.2409 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7165 | 700 | 2.2001 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7421 | 725 | 2.1923 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7677 | 750 | 2.195 | 1.1538 | 0.2549 | 0.4671 | 0.7333 | 0.2804 | 0.5265 | 0.4659 | 0.2321 | 0.5331 | 0.9086 | 0.2429 | 0.5070 | 0.5430 | 0.4369 | 0.4717 | | 0.7932 | 775 | 2.1826 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8188 | 800 | 2.1754 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8444 | 825 | 2.1141 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8700 | 850 | 2.1572 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8956 | 875 | 2.1126 | 1.1256 | 0.2505 | 0.4622 | 0.7293 | 0.2857 | 0.5286 | 0.4823 | 0.2308 | 0.5397 | 0.9158 | 0.2412 | 0.5050 | 0.5365 | 0.4387 | 0.4728 | | 0.9212 | 900 | 2.0755 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9468 | 925 | 2.1032 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9724 | 950 | 2.1211 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9980 | 975 | 2.0826 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0 | 977 | - | - | 0.2552 | 0.4644 | 0.7349 | 0.2926 | 0.5292 | 0.4680 | 0.2307 | 0.5387 | 0.9158 | 0.2418 | 0.5045 | 0.5380 | 0.4323 | 0.4728 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.624 kWh - **Carbon Emitted**: 0.243 kg of CO2 - **Hours Used**: 1.619 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} } ```