--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3012496 - loss:MultipleNegativesRankingLoss base_model: sentence-transformers-testing/stsb-bert-tiny-safetensors 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: 9.679189270737199 energy_consumed: 0.024901310697493708 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: 0.15 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: stsb-bert-tiny adapter finetuned on GooAQ pairs results: - task: type: information-retrieval name: Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: cosine_accuracy@1 value: 0.14 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.22 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.26 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.38 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.14 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.07999999999999999 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.05600000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.056666666666666664 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.08666666666666668 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.11166666666666666 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.17833333333333332 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.1412311142763055 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.19938095238095235 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.11363345611144926 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: cosine_accuracy@1 value: 0.42 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.62 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.72 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.86 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.42 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.34 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.344 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.29 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.02634308391586433 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.06038926804951766 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.10265977040056268 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.19610280190566398 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.34151812101104584 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5504126984126985 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.21133731615809154 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: cosine_accuracy@1 value: 0.12 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.18 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.22 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.36 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.12 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.05999999999999999 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.044000000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.036000000000000004 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.12 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.18 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.22 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.34 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.21218661613500586 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.17491269841269838 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.18857101300669993 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: cosine_accuracy@1 value: 0.06 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.1 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.2 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.28 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.06 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.04 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.04800000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.032 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.044000000000000004 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.06199999999999999 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.12488888888888887 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.15574603174603174 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.10395695406287388 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.10821428571428571 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.08041090092126037 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: cosine_accuracy@1 value: 0.36 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: 0.62 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.36 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: 0.07800000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.18 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.31 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.35 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.39 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3504958855767756 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4476349206349205 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.29308037158200173 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: cosine_accuracy@1 value: 0.06 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.26 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.32 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.36 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.06 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.08666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.064 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.036000000000000004 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.06 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.26 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.32 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.36 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.21417075898440763 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.16666666666666663 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.19159156983842277 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: cosine_accuracy@1 value: 0.2 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.26 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.44 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.12 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09600000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07999999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.00377949106046741 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.007274949456892388 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.012714784638321257 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.019303285579015287 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.09870502263453415 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2538809523809524 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.018928657854150332 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: cosine_accuracy@1 value: 0.08 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.18 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.2 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.42 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.08 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.06 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.04 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.042 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.08 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.17 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.19 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2051878697694875 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1506904761904762 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.16101738947158584 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: cosine_accuracy@1 value: 0.7 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.88 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.94 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.32 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: 0.624 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7719999999999999 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.866 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8993333333333333 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7992844609162323 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7798333333333335 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7635205205527187 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: cosine_accuracy@1 value: 0.18 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.26 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.32 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.4 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.18 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.12 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09200000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.066 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.036000000000000004 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.07466666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.09466666666666666 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.13466666666666666 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.1348403477257659 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.24209523809523809 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.10255365352032365 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: cosine_accuracy@1 value: 0.08 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.26 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.32 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.4 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.08 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.08666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.06400000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.04 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.08 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.26 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.32 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2375425714519515 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1856666666666667 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1985205474177431 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: cosine_accuracy@1 value: 0.08 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.22 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.32 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.08 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.07333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.064 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.034 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.08 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.195 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.28 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.3 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.19370675821369307 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.16466666666666668 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1653693334513147 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: cosine_accuracy@1 value: 0.20408163265306123 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5102040816326531 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7551020408163265 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8775510204081632 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.20408163265306123 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.25170068027210885 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.25306122448979596 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.24489795918367346 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.014397370082893721 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.04876234248655414 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.0792610922160282 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.14648888406884147 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2485959675297849 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4082118561710398 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.16376385142142616 name: Cosine Map@100 - task: type: nano-beir name: Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: cosine_accuracy@1 value: 0.20646781789638935 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.33924646781789636 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.41039246467817886 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5121193092621665 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.20646781789638935 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.1419256933542648 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.11762009419152278 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08822291993720567 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.10809127782506864 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.19128922256356135 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.2362967591905488 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.30153648743329886 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.25241711140675877 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2947898009020458 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2040229677928606 name: Cosine Map@100 --- # stsb-bert-tiny adapter finetuned on GooAQ pairs This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers-testing/stsb-bert-tiny-safetensors](https://huggingface.co/sentence-transformers-testing/stsb-bert-tiny-safetensors) on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 128-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. This model was trained using [train_script.py](train_script.py). ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers-testing/stsb-bert-tiny-safetensors](https://huggingface.co/sentence-transformers-testing/stsb-bert-tiny-safetensors) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 128 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': 128, '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("sentence-transformers-testing/stsb-bert-tiny-lora") # 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, 128] # 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 | 0.14 | 0.42 | 0.12 | 0.06 | 0.36 | 0.06 | 0.2 | 0.08 | 0.7 | 0.18 | 0.08 | 0.08 | 0.2041 | | cosine_accuracy@3 | 0.22 | 0.62 | 0.18 | 0.1 | 0.52 | 0.26 | 0.26 | 0.18 | 0.82 | 0.26 | 0.26 | 0.22 | 0.5102 | | cosine_accuracy@5 | 0.26 | 0.72 | 0.22 | 0.2 | 0.54 | 0.32 | 0.3 | 0.2 | 0.88 | 0.32 | 0.32 | 0.3 | 0.7551 | | cosine_accuracy@10 | 0.38 | 0.86 | 0.36 | 0.28 | 0.62 | 0.36 | 0.44 | 0.42 | 0.94 | 0.4 | 0.4 | 0.32 | 0.8776 | | cosine_precision@1 | 0.14 | 0.42 | 0.12 | 0.06 | 0.36 | 0.06 | 0.2 | 0.08 | 0.7 | 0.18 | 0.08 | 0.08 | 0.2041 | | cosine_precision@3 | 0.08 | 0.34 | 0.06 | 0.04 | 0.2067 | 0.0867 | 0.12 | 0.06 | 0.32 | 0.12 | 0.0867 | 0.0733 | 0.2517 | | cosine_precision@5 | 0.056 | 0.344 | 0.044 | 0.048 | 0.14 | 0.064 | 0.096 | 0.04 | 0.224 | 0.092 | 0.064 | 0.064 | 0.2531 | | cosine_precision@10 | 0.05 | 0.29 | 0.036 | 0.032 | 0.078 | 0.036 | 0.08 | 0.042 | 0.118 | 0.066 | 0.04 | 0.034 | 0.2449 | | cosine_recall@1 | 0.0567 | 0.0263 | 0.12 | 0.044 | 0.18 | 0.06 | 0.0038 | 0.08 | 0.624 | 0.036 | 0.08 | 0.08 | 0.0144 | | cosine_recall@3 | 0.0867 | 0.0604 | 0.18 | 0.062 | 0.31 | 0.26 | 0.0073 | 0.17 | 0.772 | 0.0747 | 0.26 | 0.195 | 0.0488 | | cosine_recall@5 | 0.1117 | 0.1027 | 0.22 | 0.1249 | 0.35 | 0.32 | 0.0127 | 0.19 | 0.866 | 0.0947 | 0.32 | 0.28 | 0.0793 | | cosine_recall@10 | 0.1783 | 0.1961 | 0.34 | 0.1557 | 0.39 | 0.36 | 0.0193 | 0.4 | 0.8993 | 0.1347 | 0.4 | 0.3 | 0.1465 | | **cosine_ndcg@10** | **0.1412** | **0.3415** | **0.2122** | **0.104** | **0.3505** | **0.2142** | **0.0987** | **0.2052** | **0.7993** | **0.1348** | **0.2375** | **0.1937** | **0.2486** | | cosine_mrr@10 | 0.1994 | 0.5504 | 0.1749 | 0.1082 | 0.4476 | 0.1667 | 0.2539 | 0.1507 | 0.7798 | 0.2421 | 0.1857 | 0.1647 | 0.4082 | | cosine_map@100 | 0.1136 | 0.2113 | 0.1886 | 0.0804 | 0.2931 | 0.1916 | 0.0189 | 0.161 | 0.7635 | 0.1026 | 0.1985 | 0.1654 | 0.1638 | #### 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 | 0.2065 | | cosine_accuracy@3 | 0.3392 | | cosine_accuracy@5 | 0.4104 | | cosine_accuracy@10 | 0.5121 | | cosine_precision@1 | 0.2065 | | cosine_precision@3 | 0.1419 | | cosine_precision@5 | 0.1176 | | cosine_precision@10 | 0.0882 | | cosine_recall@1 | 0.1081 | | cosine_recall@3 | 0.1913 | | cosine_recall@5 | 0.2363 | | cosine_recall@10 | 0.3015 | | **cosine_ndcg@10** | **0.2524** | | cosine_mrr@10 | 0.2948 | | cosine_map@100 | 0.204 | ## 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: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### 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: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### 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 - `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`: 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 | - | - | 0.1174 | 0.3053 | 0.1405 | 0.0440 | 0.2821 | 0.2297 | 0.0773 | 0.1708 | 0.7830 | 0.1181 | 0.2017 | 0.1447 | 0.1642 | 0.2138 | | 0.0010 | 1 | 3.6449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0256 | 25 | 3.6146 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0512 | 50 | 3.6074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0768 | 75 | 3.5997 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1024 | 100 | 3.5737 | 2.0205 | 0.1178 | 0.3061 | 0.1477 | 0.0461 | 0.2837 | 0.2291 | 0.0804 | 0.1713 | 0.7791 | 0.1205 | 0.2049 | 0.1534 | 0.1731 | 0.2164 | | 0.1279 | 125 | 3.5644 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1535 | 150 | 3.4792 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1791 | 175 | 3.4743 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2047 | 200 | 3.4169 | 1.9114 | 0.1336 | 0.3084 | 0.1446 | 0.0604 | 0.2965 | 0.2350 | 0.0847 | 0.1650 | 0.7806 | 0.1270 | 0.2141 | 0.1633 | 0.1835 | 0.2228 | | 0.2303 | 225 | 3.3535 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2559 | 250 | 3.3336 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2815 | 275 | 3.3038 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3071 | 300 | 3.2576 | 1.8114 | 0.1359 | 0.3260 | 0.1733 | 0.0752 | 0.3167 | 0.2323 | 0.0851 | 0.1753 | 0.7843 | 0.1266 | 0.2218 | 0.1752 | 0.2012 | 0.2330 | | 0.3327 | 325 | 3.2304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3582 | 350 | 3.2133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3838 | 375 | 3.1369 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4094 | 400 | 3.1412 | 1.7379 | 0.1389 | 0.3298 | 0.1930 | 0.0934 | 0.3261 | 0.2310 | 0.0852 | 0.1760 | 0.7850 | 0.1349 | 0.2235 | 0.1863 | 0.2118 | 0.2396 | | 0.4350 | 425 | 3.0782 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4606 | 450 | 3.0948 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4862 | 475 | 3.0696 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5118 | 500 | 3.0641 | 1.6850 | 0.1373 | 0.3307 | 0.1945 | 0.0937 | 0.3301 | 0.2365 | 0.0931 | 0.1950 | 0.7933 | 0.1359 | 0.2231 | 0.1885 | 0.2289 | 0.2447 | | 0.5374 | 525 | 3.0224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5629 | 550 | 2.9927 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5885 | 575 | 2.9796 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6141 | 600 | 2.9624 | 1.6475 | 0.1397 | 0.3321 | 0.2058 | 0.0999 | 0.3422 | 0.2276 | 0.1014 | 0.1901 | 0.7971 | 0.1393 | 0.2258 | 0.1918 | 0.2342 | 0.2482 | | 0.6397 | 625 | 2.9508 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6653 | 650 | 2.958 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6909 | 675 | 2.9428 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7165 | 700 | 2.9589 | 1.6209 | 0.1425 | 0.3344 | 0.2061 | 0.1050 | 0.3427 | 0.2295 | 0.1001 | 0.1868 | 0.7955 | 0.1342 | 0.2298 | 0.1922 | 0.2343 | 0.2487 | | 0.7421 | 725 | 2.9152 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7677 | 750 | 2.9056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7932 | 775 | 2.9111 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8188 | 800 | 2.9107 | 1.6037 | 0.1415 | 0.3401 | 0.2064 | 0.1053 | 0.3523 | 0.2153 | 0.1001 | 0.1934 | 0.7976 | 0.1340 | 0.2302 | 0.1946 | 0.2461 | 0.2505 | | 0.8444 | 825 | 2.8675 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8700 | 850 | 2.9175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8956 | 875 | 2.8592 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9212 | 900 | 2.86 | 1.5941 | 0.1411 | 0.3415 | 0.2180 | 0.1048 | 0.3506 | 0.2210 | 0.0987 | 0.2052 | 0.7988 | 0.1349 | 0.2302 | 0.1946 | 0.2464 | 0.2528 | | 0.9468 | 925 | 2.8603 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9724 | 950 | 2.8909 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9980 | 975 | 2.8819 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0 | 977 | - | - | 0.1412 | 0.3415 | 0.2122 | 0.1040 | 0.3505 | 0.2142 | 0.0987 | 0.2052 | 0.7993 | 0.1348 | 0.2375 | 0.1937 | 0.2486 | 0.2524 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.025 kWh - **Carbon Emitted**: 0.010 kg of CO2 - **Hours Used**: 0.15 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: 3.3.0.dev0 - Transformers: 4.46.2 - PyTorch: 2.5.0+cu121 - Accelerate: 1.0.0 - 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", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```