Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,1310 @@
+---
+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) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
+- **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]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](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 [<code>NanoBEIREvaluator</code>](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     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## 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: <code>question</code> and <code>answer</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | question                                                                          | answer                                                                              |
+  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                              |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 11.86 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 60.48 tokens</li><li>max: 138 tokens</li></ul> |
+* Samples:
+  | question                                                                           | answer                                                                                                                                                                                                                                                                                                                |
+  |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>what is the difference between broilers and layers?</code>                   | <code>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.</code>                |
+  | <code>what is the difference between chronological order and spatial order?</code> | <code>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.</code> |
+  | <code>is kamagra same as viagra?</code>                                            | <code>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.</code>                               |
+* Loss: [<code>MatryoshkaLoss</code>](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: <code>question</code> and <code>answer</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | question                                                                          | answer                                                                              |
+  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                              |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 11.88 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 61.03 tokens</li><li>max: 127 tokens</li></ul> |
+* Samples:
+  | question                                                                     | answer                                                                                                                                                                                                                                                                                                                                     |
+  |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>how do i program my directv remote with my tv?</code>                  | <code>['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.']</code>                                                                                               |
+  | <code>are rodrigues fruit bats nocturnal?</code>                             | <code>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.</code>                                                                                                  |
+  | <code>why does your heart rate increase during exercise bbc bitesize?</code> | <code>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.</code> |
+* Loss: [<code>MatryoshkaLoss</code>](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
+<details><summary>Click to expand</summary>
+
+- `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
+
+</details>
+
+### 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "microsoft/mpnet-base",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.2",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.0.dev0",
+    "transformers": "4.46.2",
+    "pytorch": "2.5.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa1f8a3b9d1bf3a1df6a622b2af83742caf5c2f745d1e47743513349c18901b5
+size 437967672

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": "[UNK]"
+}