metadata

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 model finetuned from sentence-transformers-testing/stsb-bert-tiny-safetensors on the 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.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: sentence-transformers-testing/stsb-bert-tiny-safetensors
Maximum Sequence Length: 512 tokens
Output Dimensionality: 128 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- gooaq
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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

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

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 at b089f72
Size: 3,012,496 training samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 11.86 tokens
max: 21 tokens

min: 14 tokens
mean: 60.48 tokens
max: 138 tokens

	question	answer
type	string	string
details	min: 8 tokens mean: 11.86 tokens max: 21 tokens	min: 14 tokens mean: 60.48 tokens max: 138 tokens

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 with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

gooaq

Dataset: gooaq at b089f72
Size: 3,012,496 evaluation samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 11.88 tokens
max: 22 tokens

min: 14 tokens
mean: 61.03 tokens
max: 127 tokens

	question	answer
type	string	string
details	min: 8 tokens mean: 11.88 tokens max: 22 tokens	min: 14 tokens mean: 61.03 tokens max: 127 tokens

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 with these parameters:

{
    "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.

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

@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

@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}
}