--- 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: microsoft/mpnet-base widget: - source_sentence: how to sign legal documents as power of attorney? sentences: - 'After the principal''s name, write “by” and then sign your own name. Under or after the signature line, indicate your status as POA by including any of the following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.' - '[''From the Home screen, swipe left to Apps.'', ''Tap Transfer my Data.'', ''Tap Menu (...).'', ''Tap Export to SD card.'']' - Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and striking gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted to resemble perfect nugs of cannabis, each of the 10 buds contains 35mg of THC. ... This is a perfect product for both cannabis and chocolate lovers, who appreciate a little twist. - source_sentence: how to delete vdom in fortigate? sentences: - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully removed from the configuration. - 'Both combination birth control pills and progestin-only pills may cause headaches as a side effect. Additional side effects of birth control pills may include: breast tenderness. nausea.' - White cheese tends to show imperfections more readily and as consumers got more used to yellow-orange cheese, it became an expected option. Today, many cheddars are yellow. While most cheesemakers use annatto, some use an artificial coloring agent instead, according to Sachs. - source_sentence: where are earthquakes most likely to occur on earth? sentences: - Zelle in the Bank of the America app is a fast, safe, and easy way to send and receive money with family and friends who have a bank account in the U.S., all with no fees. Money moves in minutes directly between accounts that are already enrolled with Zelle. - It takes about 3 days for a spacecraft to reach the Moon. During that time a spacecraft travels at least 240,000 miles (386,400 kilometers) which is the distance between Earth and the Moon. - Most earthquakes occur along the edge of the oceanic and continental plates. The earth's crust (the outer layer of the planet) is made up of several pieces, called plates. The plates under the oceans are called oceanic plates and the rest are continental plates. - source_sentence: fix iphone is disabled connect to itunes without itunes? sentences: - 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. - How Māui brought fire to the world. One evening, after eating a hearty meal, Māui lay beside his fire staring into the flames. ... In the middle of the night, while everyone was sleeping, Māui went from village to village and extinguished all the fires until not a single fire burned in the world. - Angry Orchard makes a variety of year-round craft cider styles, including Angry Orchard Crisp Apple, a fruit-forward hard cider that balances the sweetness of culinary apples with dryness and bright acidity of bittersweet apples for a complex, refreshing taste. - source_sentence: how to reverse a video on tiktok that's not yours? sentences: - '[''Tap "Effects" at the bottom of your screen — it\''s an icon that looks like a clock. Open the Effects menu. ... '', ''At the end of the new list that appears, tap "Time." Select "Time" at the end. ... '', ''Select "Reverse" — you\''ll then see a preview of your new, reversed video appear on the screen.'']' - Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a total initial investment range of $157,800 to $438,000. The initial cost of a franchise includes several fees -- Unlock this franchise to better understand the costs such as training and territory fees. - Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating. 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: 901.0176370050929 energy_consumed: 2.3180164676412596 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: 5.999 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: MPNet base trained on GooAQ triplets results: - task: type: information-retrieval name: Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER 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: 0.5 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.62 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.26 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.1733333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.11600000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08199999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.12833333333333333 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.23566666666666664 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.2523333333333333 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.3423333333333333 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2832168283343785 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3685714285714285 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.22816684702715823 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: cosine_accuracy@1 value: 0.56 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: 0.88 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.56 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.5 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.436 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.37800000000000006 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.05411706752798353 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.12035295895525228 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.15928246254162917 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.23697530489351543 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4605652479922868 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6701666666666667 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.313461519912651 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: cosine_accuracy@1 value: 0.62 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.84 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.62 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27999999999999997 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.172 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.092 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5766666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7866666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8066666666666668 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8666666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7421816204572005 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7256349206349206 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6984857882513162 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: cosine_accuracy@1 value: 0.4 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.52 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: 0.4 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.188 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.11199999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.24385714285714286 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.37612698412698414 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.429515873015873 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5025952380952381 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.43956943866243664 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.48483333333333334 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.39610909278538586 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: 0.72 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.78 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.84 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.31999999999999995 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.204 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.11799999999999997 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.3 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.48 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.51 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.59 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5463522282651155 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6749126984126984 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4777656892588857 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: cosine_accuracy@1 value: 0.26 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.54 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.82 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.26 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: 0.08199999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.26 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.54 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.82 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5254388867327386 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.43241269841269836 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.44192370495002076 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: cosine_accuracy@1 value: 0.42 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.52 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.54 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: 0.3533333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.29600000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.22999999999999995 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.024846889440892198 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.050109275117862714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.06353201637623539 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.08853093525637233 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2784279013606366 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.48200000000000004 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1099281411687893 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: cosine_accuracy@1 value: 0.46 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.64 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.68 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.46 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.22666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14400000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08399999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.44 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.63 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.67 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.76 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6103091812374759 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5662380952380953 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5687228298733515 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: cosine_accuracy@1 value: 0.92 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: 0.92 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.40666666666666657 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.25599999999999995 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.13399999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7973333333333332 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9453333333333334 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9593333333333334 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9893333333333334 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9468303023215506 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.948888888888889 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9245031746031745 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: cosine_accuracy@1 value: 0.34 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.54 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.64 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.76 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.34 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.21600000000000003 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.148 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.07 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.16 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.22266666666666668 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.3046666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.29180682575954126 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4679126984126984 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.20981154821773768 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: cosine_accuracy@1 value: 0.24 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.68 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.82 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.24 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.16666666666666663 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.136 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08199999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.24 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.68 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.82 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5108280876289467 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.413579365079365 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.42352200577200577 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: cosine_accuracy@1 value: 0.52 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.64 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.72 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.74 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.52 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.22666666666666668 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08399999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.485 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.61 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.705 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.73 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6181538011380482 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5913333333333333 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5833669046006453 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: cosine_accuracy@1 value: 0.5102040816326531 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8163265306122449 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8571428571428571 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9795918367346939 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5102040816326531 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.510204081632653 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.47346938775510194 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.41020408163265304 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.03893285013079613 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.11588553532033441 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.17562928121209787 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2858043118244373 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4588632608031716 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6822238419177193 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.36126308261178003 name: Cosine Map@100 - task: type: nano-beir name: Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: cosine_accuracy@1 value: 0.47001569858712716 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6520251177394034 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7182417582417582 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8061224489795917 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.47001569858712716 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2971951857666143 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.2259591836734694 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.15663108320251176 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.2814682525607806 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.4269339553990077 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.48722766408814117 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5643773684668895 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5163495085148867 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5775929206847574 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4413100253102233 name: Cosine Map@100 --- # MPNet base trained on GooAQ triplets This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) 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:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) - **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: MPNetModel (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/mpnet-base-gooaq-cmnrl-mrl") # Run inference sentences = [ "how to reverse a video on tiktok that's not yours?", '[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']', 'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.', ] 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 | 0.26 | 0.56 | 0.62 | 0.4 | 0.6 | 0.26 | 0.42 | 0.46 | 0.92 | 0.34 | 0.24 | 0.52 | 0.5102 | | cosine_accuracy@3 | 0.46 | 0.78 | 0.82 | 0.52 | 0.72 | 0.54 | 0.52 | 0.64 | 0.98 | 0.54 | 0.5 | 0.64 | 0.8163 | | cosine_accuracy@5 | 0.5 | 0.82 | 0.84 | 0.6 | 0.78 | 0.7 | 0.54 | 0.68 | 0.98 | 0.64 | 0.68 | 0.72 | 0.8571 | | cosine_accuracy@10 | 0.62 | 0.88 | 0.9 | 0.68 | 0.84 | 0.82 | 0.64 | 0.8 | 1.0 | 0.76 | 0.82 | 0.74 | 0.9796 | | cosine_precision@1 | 0.26 | 0.56 | 0.62 | 0.4 | 0.6 | 0.26 | 0.42 | 0.46 | 0.92 | 0.34 | 0.24 | 0.52 | 0.5102 | | cosine_precision@3 | 0.1733 | 0.5 | 0.28 | 0.26 | 0.32 | 0.18 | 0.3533 | 0.2267 | 0.4067 | 0.26 | 0.1667 | 0.2267 | 0.5102 | | cosine_precision@5 | 0.116 | 0.436 | 0.172 | 0.188 | 0.204 | 0.14 | 0.296 | 0.144 | 0.256 | 0.216 | 0.136 | 0.16 | 0.4735 | | cosine_precision@10 | 0.082 | 0.378 | 0.092 | 0.112 | 0.118 | 0.082 | 0.23 | 0.084 | 0.134 | 0.148 | 0.082 | 0.084 | 0.4102 | | cosine_recall@1 | 0.1283 | 0.0541 | 0.5767 | 0.2439 | 0.3 | 0.26 | 0.0248 | 0.44 | 0.7973 | 0.07 | 0.24 | 0.485 | 0.0389 | | cosine_recall@3 | 0.2357 | 0.1204 | 0.7867 | 0.3761 | 0.48 | 0.54 | 0.0501 | 0.63 | 0.9453 | 0.16 | 0.5 | 0.61 | 0.1159 | | cosine_recall@5 | 0.2523 | 0.1593 | 0.8067 | 0.4295 | 0.51 | 0.7 | 0.0635 | 0.67 | 0.9593 | 0.2227 | 0.68 | 0.705 | 0.1756 | | cosine_recall@10 | 0.3423 | 0.237 | 0.8667 | 0.5026 | 0.59 | 0.82 | 0.0885 | 0.76 | 0.9893 | 0.3047 | 0.82 | 0.73 | 0.2858 | | **cosine_ndcg@10** | **0.2832** | **0.4606** | **0.7422** | **0.4396** | **0.5464** | **0.5254** | **0.2784** | **0.6103** | **0.9468** | **0.2918** | **0.5108** | **0.6182** | **0.4589** | | cosine_mrr@10 | 0.3686 | 0.6702 | 0.7256 | 0.4848 | 0.6749 | 0.4324 | 0.482 | 0.5662 | 0.9489 | 0.4679 | 0.4136 | 0.5913 | 0.6822 | | cosine_map@100 | 0.2282 | 0.3135 | 0.6985 | 0.3961 | 0.4778 | 0.4419 | 0.1099 | 0.5687 | 0.9245 | 0.2098 | 0.4235 | 0.5834 | 0.3613 | #### 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 | 0.47 | | cosine_accuracy@3 | 0.652 | | cosine_accuracy@5 | 0.7182 | | cosine_accuracy@10 | 0.8061 | | cosine_precision@1 | 0.47 | | cosine_precision@3 | 0.2972 | | cosine_precision@5 | 0.226 | | cosine_precision@10 | 0.1566 | | cosine_recall@1 | 0.2815 | | cosine_recall@3 | 0.4269 | | cosine_recall@5 | 0.4872 | | cosine_recall@10 | 0.5644 | | **cosine_ndcg@10** | **0.5163** | | cosine_mrr@10 | 0.5776 | | cosine_map@100 | 0.4413 | ## 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`: 2048 - `per_device_eval_batch_size`: 2048 - `learning_rate`: 8e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `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`: 2048 - `per_device_eval_batch_size`: 2048 - `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`: 8e-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`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: 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 | - | - | 0.0419 | 0.1123 | 0.0389 | 0.0309 | 0.0746 | 0.1310 | 0.0311 | 0.0397 | 0.6607 | 0.0638 | 0.2616 | 0.1097 | 0.1098 | 0.1312 | | 0.0007 | 1 | 41.9671 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0682 | 100 | 12.4237 | 1.0176 | 0.3022 | 0.4597 | 0.7934 | 0.4621 | 0.5280 | 0.4849 | 0.2517 | 0.5561 | 0.8988 | 0.3144 | 0.5708 | 0.5755 | 0.4514 | 0.5115 | | 0.1363 | 200 | 3.0536 | 0.6917 | 0.2883 | 0.4588 | 0.7773 | 0.4272 | 0.5264 | 0.5494 | 0.2538 | 0.5837 | 0.9303 | 0.2945 | 0.5493 | 0.5795 | 0.4547 | 0.5133 | | 0.2045 | 300 | 2.2724 | 0.5954 | 0.2944 | 0.4606 | 0.7825 | 0.4522 | 0.5247 | 0.5069 | 0.2554 | 0.5636 | 0.9177 | 0.2861 | 0.5560 | 0.5562 | 0.4667 | 0.5095 | | 0.2727 | 400 | 1.933 | 0.5171 | 0.3027 | 0.4841 | 0.7050 | 0.4406 | 0.4877 | 0.5406 | 0.2768 | 0.6014 | 0.9463 | 0.2989 | 0.5725 | 0.6151 | 0.4680 | 0.5184 | | 0.3408 | 500 | 1.7806 | 0.4745 | 0.3034 | 0.4857 | 0.7537 | 0.4435 | 0.5661 | 0.5529 | 0.2733 | 0.5878 | 0.9470 | 0.3016 | 0.5377 | 0.6073 | 0.4682 | 0.5252 | | 0.4090 | 600 | 1.6253 | 0.4392 | 0.3018 | 0.4790 | 0.7502 | 0.4617 | 0.5478 | 0.5411 | 0.2812 | 0.6220 | 0.9443 | 0.2916 | 0.5210 | 0.5900 | 0.4644 | 0.5228 | | 0.4772 | 700 | 1.5136 | 0.4312 | 0.3175 | 0.4846 | 0.7481 | 0.4168 | 0.5761 | 0.5222 | 0.2825 | 0.6142 | 0.9415 | 0.2888 | 0.5373 | 0.5754 | 0.4675 | 0.5210 | | 0.5453 | 800 | 1.4454 | 0.4022 | 0.3017 | 0.4756 | 0.7307 | 0.4494 | 0.5484 | 0.5184 | 0.2821 | 0.6182 | 0.9440 | 0.2834 | 0.5191 | 0.6071 | 0.4694 | 0.5191 | | 0.6135 | 900 | 1.3711 | 0.3886 | 0.2945 | 0.4602 | 0.7463 | 0.4529 | 0.5433 | 0.5457 | 0.2730 | 0.5972 | 0.9449 | 0.2776 | 0.5183 | 0.6018 | 0.4716 | 0.5175 | | 0.6817 | 1000 | 1.3295 | 0.3688 | 0.2811 | 0.4720 | 0.7275 | 0.4342 | 0.5581 | 0.5418 | 0.2809 | 0.6087 | 0.9421 | 0.2823 | 0.5138 | 0.5729 | 0.4662 | 0.5140 | | 0.7498 | 1100 | 1.267 | 0.3637 | 0.2815 | 0.4666 | 0.7168 | 0.4346 | 0.5348 | 0.5317 | 0.2789 | 0.6056 | 0.9450 | 0.2775 | 0.5117 | 0.6116 | 0.4583 | 0.5119 | | 0.8180 | 1200 | 1.2542 | 0.3514 | 0.2882 | 0.4659 | 0.7275 | 0.4308 | 0.5585 | 0.5373 | 0.2788 | 0.5950 | 0.9433 | 0.2767 | 0.5241 | 0.6141 | 0.4655 | 0.5158 | | 0.8862 | 1300 | 1.2146 | 0.3427 | 0.2932 | 0.4638 | 0.7118 | 0.4453 | 0.5636 | 0.5363 | 0.2788 | 0.6098 | 0.9481 | 0.2825 | 0.5160 | 0.6238 | 0.4619 | 0.5181 | | 0.9543 | 1400 | 1.1892 | 0.3378 | 0.2809 | 0.4610 | 0.7319 | 0.4353 | 0.5397 | 0.5295 | 0.2828 | 0.6029 | 0.9474 | 0.2931 | 0.5078 | 0.6182 | 0.4602 | 0.5147 | | 1.0 | 1467 | - | - | 0.2832 | 0.4606 | 0.7422 | 0.4396 | 0.5464 | 0.5254 | 0.2784 | 0.6103 | 0.9468 | 0.2918 | 0.5108 | 0.6182 | 0.4589 | 0.5163 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 2.318 kWh - **Carbon Emitted**: 0.901 kg of CO2 - **Hours Used**: 5.999 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: 1.1.1 - 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} } ```