Add new SparseEncoder model

1_SpladePooling/config.json

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+{
+    "pooling_strategy": "max",
+    "activation_function": "relu",
+    "word_embedding_dimension": 30522
+}

README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sparse-encoder
+- sparse
+- splade
+- generated_from_trainer
+- dataset_size:90000
+- loss:SpladeLoss
+- loss:SparseMultipleNegativesRankingLoss
+- loss:FlopsLoss
+base_model: distilbert/distilbert-base-uncased
+widget:
+- text: yvette mimieux net worth
+- text: is sweet potato fries a bad calorie for you
+- text: 'The name Gunnar is a Swedish baby name. In Swedish the meaning of the name
+    Gunnar is: Battle strong. American Meaning: The name Gunnar is an American baby
+    name.In American the meaning of the name Gunnar is: Battle strong.Teutonic Meaning:
+    The name Gunnar is a Teutonic baby name.In Teutonic the meaning of the name Gunnar
+    is: Bold warrior. Norse Meaning: The name Gunnar is a Norse baby name. In Norse
+    the meaning of the name Gunnar is: Iighter. Scandinavian Meaning: The name Gunnar
+    is a Scandinavian baby name.he name Gunnar is a Teutonic baby name. In Teutonic
+    the meaning of the name Gunnar is: Bold warrior. Norse Meaning: The name Gunnar
+    is a Norse baby name. In Norse the meaning of the name Gunnar is: Iighter. Scandinavian
+    Meaning: The name Gunnar is a Scandinavian baby name.'
+- text: what your fsh test results indicate
+- text: 1 Lymphoma, the most common canine cancer, usually requires only chemotherapy
+    and its cost can come up to be around $450 to $500. 2  Osteosarcoma, another type
+    of canine cancer, is usually treated with chemotherapy along with amputation surgery.3  This
+    type of chemotherapy treatment costs approximately $450.nother factor is the type
+    of drugs used in the process. The size of the dog that needs to undergo chemotherapy
+    can also impact the cost. Even a dog very small in size with a single cancerous
+    lesion can cost $200 for chemotherapy, while the same problem on a larger breed
+    could cost more than $1,000 a month.
+datasets:
+- sentence-transformers/msmarco
+pipeline_tag: feature-extraction
+library_name: sentence-transformers
+metrics:
+- dot_accuracy@1
+- dot_accuracy@3
+- dot_accuracy@5
+- dot_accuracy@10
+- dot_precision@1
+- dot_precision@3
+- dot_precision@5
+- dot_precision@10
+- dot_recall@1
+- dot_recall@3
+- dot_recall@5
+- dot_recall@10
+- dot_ndcg@10
+- dot_mrr@10
+- dot_map@100
+- query_active_dims
+- query_sparsity_ratio
+- corpus_active_dims
+- corpus_sparsity_ratio
+co2_eq_emissions:
+  emissions: 53.738116037369885
+  energy_consumed: 0.1382501660330276
+  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: 0.458
+  hardware_used: 1 x NVIDIA GeForce RTX 3090
+model-index:
+- name: splade-distilbert-base-uncased trained on MS MARCO triplets
+  results:
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoMSMARCO
+      type: NanoMSMARCO
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.38
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.66
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.72
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.86
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.38
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.22
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.14400000000000002
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.08599999999999998
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.38
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.66
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.72
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.86
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.6227350359947015
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.5469285714285713
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.5547419639747225
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 23.6200008392334
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.999226131942886
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 86.9286117553711
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.9971519359230925
+      name: Corpus Sparsity Ratio
+    - type: dot_accuracy@1
+      value: 0.38
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.66
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.72
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.86
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.38
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.22
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.14400000000000002
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.08599999999999998
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.38
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.66
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.72
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.86
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.6227350359947015
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.5469285714285713
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.5547419639747225
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 23.6200008392334
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.999226131942886
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 86.9286117553711
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.9971519359230925
+      name: Corpus Sparsity Ratio
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoNFCorpus
+      type: NanoNFCorpus
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.38
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.54
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.58
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.62
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.38
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.36666666666666664
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.32799999999999996
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.266
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.021751342131059177
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.07406240621283516
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.09358105221669372
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.11969365467146144
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.3145066096009473
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.4676904761904762
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.1297946574794519
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 18.780000686645508
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.9993847060911262
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 164.79444885253906
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.9946007978227986
+      name: Corpus Sparsity Ratio
+    - type: dot_accuracy@1
+      value: 0.38
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.54
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.58
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.62
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.38
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.36666666666666664
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.32799999999999996
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.266
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.021751342131059177
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.07406240621283516
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.09358105221669372
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.11969365467146144
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.3145066096009473
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.4676904761904762
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.1297946574794519
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 18.780000686645508
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.9993847060911262
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 164.79444885253906
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.9946007978227986
+      name: Corpus Sparsity Ratio
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoNQ
+      type: NanoNQ
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.52
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.7
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.72
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.78
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.52
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.2333333333333333
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.14400000000000002
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.08199999999999999
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.5
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.66
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.68
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.75
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.6400441027431699
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.6203571428571428
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.6045362504066882
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 26.940000534057617
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.999117357953802
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 109.75908660888672
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.99640393530539
+      name: Corpus Sparsity Ratio
+    - type: dot_accuracy@1
+      value: 0.52
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.7
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.72
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.78
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.52
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.2333333333333333
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.14400000000000002
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.08199999999999999
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.5
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.66
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.68
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.75
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.6400441027431699
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.6203571428571428
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.6045362504066882
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 26.940000534057617
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.999117357953802
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 109.75908660888672
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.99640393530539
+      name: Corpus Sparsity Ratio
+  - task:
+      type: sparse-nano-beir
+      name: Sparse Nano BEIR
+    dataset:
+      name: NanoBEIR mean
+      type: NanoBEIR_mean
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.4266666666666667
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.6333333333333334
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.6733333333333332
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.7533333333333333
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.4266666666666667
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.2733333333333333
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.20533333333333334
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.14466666666666664
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.3005837807103531
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.46468746873761174
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.49786035073889795
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.5765645515571538
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.5257619161129395
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.5449920634920634
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.4296909572869542
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 23.11333401997884
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.9992427319959382
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 113.39544145649826
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.9962847964924808
+      name: Corpus Sparsity Ratio
+    - type: dot_accuracy@1
+      value: 0.531773940345369
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.6966718995290424
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.7506122448979593
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.8230455259026688
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.531773940345369
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.3291470434327577
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.25535321821036105
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.18231397174254316
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.3098648701114875
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.4649217800565723
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.5185635370811593
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.6012619916233037
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.570153816546152
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.6274141187610573
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.49205810209697926
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 40.89984672205474
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.9986599879849926
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 112.74959253323884
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.9963059566039827
+      name: Corpus Sparsity Ratio
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoClimateFEVER
+      type: NanoClimateFEVER
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.26
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.46
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.58
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.7
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.26
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.16
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.124
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.094
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.13166666666666665
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.22899999999999998
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.26966666666666667
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.3686666666666667
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.29349270164622415
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.3810238095238095
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.22256540249654586
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 52.70000076293945
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.9982733765558306
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 152.81748962402344
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.9949932019650081
+      name: Corpus Sparsity Ratio
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoDBPedia
+      type: NanoDBPedia
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.76
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.88
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.88
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.9
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.76
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.6066666666666667
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.5840000000000001
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.514
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.08858465342637668
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.17454996352915306
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.2491328586940767
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.358206868666866
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.632129041524978
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.8133333333333332
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.490256496112331
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 22.979999542236328
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.9992471004671307
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 103.23821258544922
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.9966175803490777
+      name: Corpus Sparsity Ratio
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoFEVER
+      type: NanoFEVER
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.8
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.9
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.96
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.98
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.8
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.30666666666666664
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.204
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.10399999999999998
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.7666666666666667
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.8666666666666667
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.9333333333333332
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.9433333333333332
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.8746461544855423
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.8663333333333334
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.8475954415954416
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 41.619998931884766
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.9986363934561338
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 154.98318481445312
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.9949222467461355
+      name: Corpus Sparsity Ratio
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoFiQA2018
+      type: NanoFiQA2018
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.32
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.54
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.6
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.7
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.32
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.24
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.172
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.10599999999999998
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.1620793650793651
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.36180158730158724
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.4036349206349206
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.4941031746031746
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.38562756897614053
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.43427777777777765
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.3209789709019607
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 22.68000030517578
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.9992569294179551
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 89.73922729492188
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.9970598510158272
+      name: Corpus Sparsity Ratio
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoHotpotQA
+      type: NanoHotpotQA
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.9
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.96
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.96
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.96
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.9
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.5199999999999999
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.324
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.172
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.45
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.78
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.81
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.86
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.8341414369684795
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.9266666666666665
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.7830202707785705
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 43.13999938964844
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.9985865932969776
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 108.24322509765625
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.9964535998591949
+      name: Corpus Sparsity Ratio
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoQuoraRetrieval
+      type: NanoQuoraRetrieval
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.84
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.94
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.96
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.96
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.84
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.35999999999999993
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.22399999999999995
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.12199999999999997
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.774
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.8853333333333333
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.902
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.93
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.891220122907666
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.8916666666666666
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.8739838721281753
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 21.780000686645508
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.999286416332919
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 24.841062545776367
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.9991861259895886
+      name: Corpus Sparsity Ratio
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoSCIDOCS
+      type: NanoSCIDOCS
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.44
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.56
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.62
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.76
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.44
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.2733333333333333
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.22399999999999998
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.162
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.09266666666666667
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.1696666666666667
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.22966666666666669
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.3306666666666666
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.32673772222029135
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.5299920634920635
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.24677603468739176
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 40.18000030517578
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.9986835724950798
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 145.2197265625
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.995242129396419
+      name: Corpus Sparsity Ratio
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoArguAna
+      type: NanoArguAna
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.12
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.44
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.54
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.72
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.12
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.14666666666666667
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.10800000000000003
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.07200000000000001
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.12
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.44
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.54
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.72
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.40190838047249483
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.30241269841269836
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.3152551920928219
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 142.10000610351562
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.9953443415862815
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 133.527099609375
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.995625217888429
+      name: Corpus Sparsity Ratio
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoSciFact
+      type: NanoSciFact
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.54
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.64
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.74
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.78
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.54
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.2333333333333333
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.16
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.08599999999999998
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.495
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.615
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.715
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.76
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.6395168665161247
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.6164444444444444
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.6051482940863745
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 54.2400016784668
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.9982229211166219
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 172.6594696044922
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.9943431141601305
+      name: Corpus Sparsity Ratio
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoTouche2020
+      type: NanoTouche2020
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.6530612244897959
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.8367346938775511
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.8979591836734694
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.9795918367346939
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.6530612244897959
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.6122448979591837
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.5795918367346938
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.5040816326530612
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.045827950812536176
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.1279025170251966
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.19531048384271374
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.32173552649478127
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.5552938710432158
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.7592565597667638
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.4021024805202534
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 20.53061294555664
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.9993273503392452
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 106.17720031738281
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.9965212895512292
+      name: Corpus Sparsity Ratio
+---
+
+# splade-distilbert-base-uncased trained on MS MARCO triplets
+
+This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space   and can be used for semantic search and sparse retrieval.
+## Model Details
+
+### Model Description
+- **Model Type:** SPLADE Sparse Encoder
+- **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 30522 dimensions
+- **Similarity Function:** Dot Product
+- **Training Dataset:**
+    - [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco)
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
+
+### Full Model Architecture
+
+```
+SparseEncoder(
+  (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM 
+  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
+)
+```
+
+## 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 SparseEncoder
+
+# Download from the 🤗 Hub
+model = SparseEncoder("tomaarsen/splade-distilbert-base-uncased-msmarco-mrl")
+# Run inference
+queries = [
+    "canine ultrasound cost",
+]
+documents = [
+    'VetInfo indicates that any type of canine ultrasound costs anywhere from $300 to $500 depending on what region of the country you live in and whether or not a veterinarian or a technician performs the procedure.',
+    '1 Lymphoma, the most common canine cancer, usually requires only chemotherapy and its cost can come up to be around $450 to $500. 2  Osteosarcoma, another type of canine cancer, is usually treated with chemotherapy along with amputation surgery.3  This type of chemotherapy treatment costs approximately $450.nother factor is the type of drugs used in the process. The size of the dog that needs to undergo chemotherapy can also impact the cost. Even a dog very small in size with a single cancerous lesion can cost $200 for chemotherapy, while the same problem on a larger breed could cost more than $1,000 a month.',
+    'Plant Life. There are many different plants in the rain forest. Some of the plants include vines, bromeliads, the passion fruit plant and the Victorian water lily. Vines in the rainforest can be as thick as the average human average human body and some can grow to be 3,000 ft long.',
+]
+query_embeddings = model.encode_query(queries)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings.shape)
+# [1, 30522] [3, 30522]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+# tensor([[33.7473, 25.6638,  0.2965]])
+```
+
+<!--
+### 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
+
+#### Sparse Information Retrieval
+
+* Datasets: `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
+* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator)
+
+| Metric                | NanoMSMARCO | NanoNFCorpus | NanoNQ   | NanoClimateFEVER | NanoDBPedia | NanoFEVER  | NanoFiQA2018 | NanoHotpotQA | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
+|:----------------------|:------------|:-------------|:---------|:-----------------|:------------|:-----------|:-------------|:-------------|:-------------------|:------------|:------------|:------------|:---------------|
+| dot_accuracy@1        | 0.38        | 0.38         | 0.52     | 0.26             | 0.76        | 0.8        | 0.32         | 0.9          | 0.84               | 0.44        | 0.12        | 0.54        | 0.6531         |
+| dot_accuracy@3        | 0.66        | 0.54         | 0.7      | 0.46             | 0.88        | 0.9        | 0.54         | 0.96         | 0.94               | 0.56        | 0.44        | 0.64        | 0.8367         |
+| dot_accuracy@5        | 0.72        | 0.58         | 0.72     | 0.58             | 0.88        | 0.96       | 0.6          | 0.96         | 0.96               | 0.62        | 0.54        | 0.74        | 0.898          |
+| dot_accuracy@10       | 0.86        | 0.62         | 0.78     | 0.7              | 0.9         | 0.98       | 0.7          | 0.96         | 0.96               | 0.76        | 0.72        | 0.78        | 0.9796         |
+| dot_precision@1       | 0.38        | 0.38         | 0.52     | 0.26             | 0.76        | 0.8        | 0.32         | 0.9          | 0.84               | 0.44        | 0.12        | 0.54        | 0.6531         |
+| dot_precision@3       | 0.22        | 0.3667       | 0.2333   | 0.16             | 0.6067      | 0.3067     | 0.24         | 0.52         | 0.36               | 0.2733      | 0.1467      | 0.2333      | 0.6122         |
+| dot_precision@5       | 0.144       | 0.328        | 0.144    | 0.124            | 0.584       | 0.204      | 0.172        | 0.324        | 0.224              | 0.224       | 0.108       | 0.16        | 0.5796         |
+| dot_precision@10      | 0.086       | 0.266        | 0.082    | 0.094            | 0.514       | 0.104      | 0.106        | 0.172        | 0.122              | 0.162       | 0.072       | 0.086       | 0.5041         |
+| dot_recall@1          | 0.38        | 0.0218       | 0.5      | 0.1317           | 0.0886      | 0.7667     | 0.1621       | 0.45         | 0.774              | 0.0927      | 0.12        | 0.495       | 0.0458         |
+| dot_recall@3          | 0.66        | 0.0741       | 0.66     | 0.229            | 0.1745      | 0.8667     | 0.3618       | 0.78         | 0.8853             | 0.1697      | 0.44        | 0.615       | 0.1279         |
+| dot_recall@5          | 0.72        | 0.0936       | 0.68     | 0.2697           | 0.2491      | 0.9333     | 0.4036       | 0.81         | 0.902              | 0.2297      | 0.54        | 0.715       | 0.1953         |
+| dot_recall@10         | 0.86        | 0.1197       | 0.75     | 0.3687           | 0.3582      | 0.9433     | 0.4941       | 0.86         | 0.93               | 0.3307      | 0.72        | 0.76        | 0.3217         |
+| **dot_ndcg@10**       | **0.6227**  | **0.3145**   | **0.64** | **0.2935**       | **0.6321**  | **0.8746** | **0.3856**   | **0.8341**   | **0.8912**         | **0.3267**  | **0.4019**  | **0.6395**  | **0.5553**     |
+| dot_mrr@10            | 0.5469      | 0.4677       | 0.6204   | 0.381            | 0.8133      | 0.8663     | 0.4343       | 0.9267       | 0.8917             | 0.53        | 0.3024      | 0.6164      | 0.7593         |
+| dot_map@100           | 0.5547      | 0.1298       | 0.6045   | 0.2226           | 0.4903      | 0.8476     | 0.321        | 0.783        | 0.874              | 0.2468      | 0.3153      | 0.6051      | 0.4021         |
+| query_active_dims     | 23.62       | 18.78        | 26.94    | 52.7             | 22.98       | 41.62      | 22.68        | 43.14        | 21.78              | 40.18       | 142.1       | 54.24       | 20.5306        |
+| query_sparsity_ratio  | 0.9992      | 0.9994       | 0.9991   | 0.9983           | 0.9992      | 0.9986     | 0.9993       | 0.9986       | 0.9993             | 0.9987      | 0.9953      | 0.9982      | 0.9993         |
+| corpus_active_dims    | 86.9286     | 164.7944     | 109.7591 | 152.8175         | 103.2382    | 154.9832   | 89.7392      | 108.2432     | 24.8411            | 145.2197    | 133.5271    | 172.6595    | 106.1772       |
+| corpus_sparsity_ratio | 0.9972      | 0.9946       | 0.9964   | 0.995            | 0.9966      | 0.9949     | 0.9971       | 0.9965       | 0.9992             | 0.9952      | 0.9956      | 0.9943      | 0.9965         |
+
+#### Sparse Nano BEIR
+
+* Dataset: `NanoBEIR_mean`
+* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
+  ```json
+  {
+      "dataset_names": [
+          "msmarco",
+          "nfcorpus",
+          "nq"
+      ]
+  }
+  ```
+
+| Metric                | Value      |
+|:----------------------|:-----------|
+| dot_accuracy@1        | 0.4267     |
+| dot_accuracy@3        | 0.6333     |
+| dot_accuracy@5        | 0.6733     |
+| dot_accuracy@10       | 0.7533     |
+| dot_precision@1       | 0.4267     |
+| dot_precision@3       | 0.2733     |
+| dot_precision@5       | 0.2053     |
+| dot_precision@10      | 0.1447     |
+| dot_recall@1          | 0.3006     |
+| dot_recall@3          | 0.4647     |
+| dot_recall@5          | 0.4979     |
+| dot_recall@10         | 0.5766     |
+| **dot_ndcg@10**       | **0.5258** |
+| dot_mrr@10            | 0.545      |
+| dot_map@100           | 0.4297     |
+| query_active_dims     | 23.1133    |
+| query_sparsity_ratio  | 0.9992     |
+| corpus_active_dims    | 113.3954   |
+| corpus_sparsity_ratio | 0.9963     |
+
+#### Sparse Nano BEIR
+
+* Dataset: `NanoBEIR_mean`
+* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
+  ```json
+  {
+      "dataset_names": [
+          "climatefever",
+          "dbpedia",
+          "fever",
+          "fiqa2018",
+          "hotpotqa",
+          "msmarco",
+          "nfcorpus",
+          "nq",
+          "quoraretrieval",
+          "scidocs",
+          "arguana",
+          "scifact",
+          "touche2020"
+      ]
+  }
+  ```
+
+| Metric                | Value      |
+|:----------------------|:-----------|
+| dot_accuracy@1        | 0.5318     |
+| dot_accuracy@3        | 0.6967     |
+| dot_accuracy@5        | 0.7506     |
+| dot_accuracy@10       | 0.823      |
+| dot_precision@1       | 0.5318     |
+| dot_precision@3       | 0.3291     |
+| dot_precision@5       | 0.2554     |
+| dot_precision@10      | 0.1823     |
+| dot_recall@1          | 0.3099     |
+| dot_recall@3          | 0.4649     |
+| dot_recall@5          | 0.5186     |
+| dot_recall@10         | 0.6013     |
+| **dot_ndcg@10**       | **0.5702** |
+| dot_mrr@10            | 0.6274     |
+| dot_map@100           | 0.4921     |
+| query_active_dims     | 40.8998    |
+| query_sparsity_ratio  | 0.9987     |
+| corpus_active_dims    | 112.7496   |
+| corpus_sparsity_ratio | 0.9963     |
+
+<!--
+## 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
+
+#### msmarco
+
+* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83)
+* Size: 90,000 training samples
+* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                           | positive                                                                            | negative                                                                            |
+  |:--------|:--------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                          | string                                                                              | string                                                                              |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 9.1 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 79.91 tokens</li><li>max: 218 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 77.09 tokens</li><li>max: 256 tokens</li></ul> |
+* Samples:
+  | query                                        | positive                                                                                                                                                                                                                                                                                                                                               | negative                                                                                                                                                                                                                                                                                    |
+  |:---------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>when do manga complete editions</code> | <code>Volumes 9 and 10 the Sailor Moon Manga Complete Editions are now out. Last week, March 25th, volumes 9 and 10 of the Sailor Moon Manga Complete Editions were released in Japan. Volume 9 features Endymion and Serenity on the cover while volume 10 features all 10 Sailor Guardians.</code>                                                   | <code>Destiny: The Taken King will be released in standard download, collector’s edition download, and both “Collector’s” and “Legendary” game disc editions on September 15, 2015.</code>                                                                                      |
+  | <code>the define of homograph</code>         | <code>LINK / CITE ADD TO WORD LIST. noun. The definition of a homograph is a word that is spelled like another word or other words, but has a different meaning and sometimes sounds different. An example of a homograph is evening, which is the time of day after the sun has set or making something level or flat.</code>                         | <code>As verbs the difference between describe and define. is that describe is to represent in words while define is to determine. As a noun define is. (computing|programming) a kind of macro in source code that replaces one text string with another wherever it occurs.</code>        |
+  | <code>what is a cv in resume writing</code>  | <code>Curriculum Vitae (CV) is Latin for “course of life.” In contrast, resume is French for “summary.” Both CVs & Resumes: 1  Are tailored for the specific job/company you are applying to. 2  Should represent you as the best qualified candidate. 3  Are used to get you an interview.  Do not usually include personal interests.</code> | <code>Resume Samples » Resume Objective » Legal Resume Objective » Legal Assistant Resume Objective. Job Description: Legal assistant is responsible to manage and handle various activities of legal department. Preparing legal documents such as contracts, wills and appeals.</code> |
+* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
+  ```json
+  {
+      "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
+      "lambda_corpus": 0.001,
+      "lambda_query": 5e-05
+  }
+  ```
+
+### Evaluation Dataset
+
+#### msmarco
+
+* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83)
+* Size: 10,000 evaluation samples
+* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                            | positive                                                                           | negative                                                                            |
+  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                             | string                                                                              |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 9.24 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 80.9 tokens</li><li>max: 204 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 78.24 tokens</li><li>max: 234 tokens</li></ul> |
+* Samples:
+  | query                                                                     | positive                                                                                                                                                                                                                                                                                                       | negative                                                                                                                                                                                                                                                                                                                   |
+  |:--------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>the largest vietnamese population in the united states is in</code> | <code>The largest number of Vietnamese outside Vietnam is in Orange County, California (184,153, or 6.1 percent of the county's population), followed by Los Angeles and Santa Clara counties; the three counties accounted for 26 percent of the Vietnamese immigrant population in the United States.</code> | <code>Population by Place in the United States There are 29,257 places in the United States. This section compares Hibbing to the 50 most populous places in the United States. The least populous of the compared places has a population of 371,267.</code>                                                              |
+  | <code>how many calories in a tablespoon of flaxseed</code>                | <code>Calorie Content. A 2-tablespoon serving of ground flaxseed has about 75 calories, according to the U.S. Department of Agriculture. These calories consist of 2.6 grams of protein, 4 grams of carbohydrates -- almost all of which is fiber -- and 6 grams of fat.</code>                                | <code>You can also use flaxseed meal to replace an egg. Use 1 tablespoon flaxseed meal and 3 tablespoons water. You can replace up to two eggs in a recipe in this manner, but do not use flaxseed meal as an egg replacement if you are already using it as an oil replacement. Flaxseed has many health benefits.</code> |
+  | <code>who wrote the house of seven gables</code>                          | <code>The author of The House Of Seven Gables is Nathaniel Hawthorne.</code>                                                                                                                                                                                                                                   | <code>Abigail Adams Wrote To John In 1776: Remember The Ladies Or We'll Rebel Adams wrote a feminist letter to her husband just before U.S. independence.</code>                                                                                                                                                           |
+* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
+  ```json
+  {
+      "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
+      "lambda_corpus": 0.001,
+      "lambda_query": 5e-05
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `load_best_model_at_end`: 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`: 16
+- `per_device_eval_batch_size`: 16
+- `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`: True
+- `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`: None
+- `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
+- `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
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch      | Step     | Training Loss | Validation Loss | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 |
+|:----------:|:--------:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:|
+| 0.0178     | 100      | 173.8874      | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.0356     | 200      | 11.8803       | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.0533     | 300      | 1.0264        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.0711     | 400      | 0.3923        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.0889     | 500      | 0.32          | 0.2369          | 0.4932                  | 0.3001                   | 0.5819             | 0.4584                    | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.1067     | 600      | 0.2483        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.1244     | 700      | 0.28          | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.1422     | 800      | 0.2095        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.16       | 900      | 0.2093        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.1778     | 1000     | 0.1636        | 0.1898          | 0.6051                  | 0.2845                   | 0.6124             | 0.5006                    | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.1956     | 1100     | 0.1661        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.2133     | 1200     | 0.1964        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.2311     | 1300     | 0.1937        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.2489     | 1400     | 0.1771        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.2667     | 1500     | 0.1643        | 0.1549          | 0.5868                  | 0.3176                   | 0.5769             | 0.4938                    | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.2844     | 1600     | 0.1987        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.3022     | 1700     | 0.178         | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.32       | 1800     | 0.1227        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.3378     | 1900     | 0.1478        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.3556     | 2000     | 0.1502        | 0.1563          | 0.6249                  | 0.3309                   | 0.6088             | 0.5215                    | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.3733     | 2100     | 0.1623        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.3911     | 2200     | 0.1703        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.4089     | 2300     | 0.1804        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.4267     | 2400     | 0.121         | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.4444     | 2500     | 0.1451        | 0.1325          | 0.5620                  | 0.3233                   | 0.6197             | 0.5017                    | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.4622     | 2600     | 0.1609        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.48       | 2700     | 0.1415        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.4978     | 2800     | 0.1555        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.5156     | 2900     | 0.1581        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.5333     | 3000     | 0.1351        | 0.1546          | 0.5901                  | 0.3187                   | 0.6299             | 0.5129                    | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.5511     | 3100     | 0.1308        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.5689     | 3200     | 0.1313        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.5867     | 3300     | 0.1248        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.6044     | 3400     | 0.1295        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.6222     | 3500     | 0.1398        | 0.1449          | 0.6096                  | 0.3285                   | 0.5975             | 0.5119                    | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.64       | 3600     | 0.1105        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.6578     | 3700     | 0.0911        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.6756     | 3800     | 0.1683        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.6933     | 3900     | 0.1202        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.7111     | 4000     | 0.135         | 0.1592          | 0.5989                  | 0.3109                   | 0.6460             | 0.5186                    | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.7289     | 4100     | 0.1205        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.7467     | 4200     | 0.1432        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.7644     | 4300     | 0.105         | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.7822     | 4400     | 0.1028        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.8        | 4500     | 0.1386        | 0.1383          | 0.5859                  | 0.3084                   | 0.6276             | 0.5073                    | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.8178     | 4600     | 0.1068        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.8356     | 4700     | 0.1262        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.8533     | 4800     | 0.1182        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.8711     | 4900     | 0.1331        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.8889     | 5000     | 0.1436        | 0.1279          | 0.6261                  | 0.3136                   | 0.6314             | 0.5237                    | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.9067     | 5100     | 0.1182        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.9244     | 5200     | 0.1379        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.9422     | 5300     | 0.1343        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| 0.96       | 5400     | 0.1475        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| **0.9778** | **5500** | **0.0988**    | **0.1311**      | **0.6227**              | **0.3145**               | **0.64**           | **0.5258**                | **-**                        | **-**                   | **-**                 | **-**                    | **-**                    | **-**                          | **-**                   | **-**                   | **-**                   | **-**                      |
+| 0.9956     | 5600     | 0.1072        | -               | -                       | -                        | -                  | -                         | -                            | -                       | -                     | -                        | -                        | -                              | -                       | -                       | -                       | -                          |
+| -1         | -1       | -             | -               | 0.6227                  | 0.3145                   | 0.6400             | 0.5702                    | 0.2935                       | 0.6321                  | 0.8746                | 0.3856                   | 0.8341                   | 0.8912                         | 0.3267                  | 0.4019                  | 0.6395                  | 0.5553                     |
+
+* The bold row denotes the saved checkpoint.
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Energy Consumed**: 0.138 kWh
+- **Carbon Emitted**: 0.054 kg of CO2
+- **Hours Used**: 0.458 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: 4.2.0.dev0
+- Transformers: 4.52.4
+- PyTorch: 2.7.1+cu126
+- Accelerate: 1.5.1
+- Datasets: 2.21.0
+- Tokenizers: 0.21.1
+
+## 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",
+}
+```
+
+#### SpladeLoss
+```bibtex
+@misc{formal2022distillationhardnegativesampling,
+      title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
+      author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
+      year={2022},
+      eprint={2205.04733},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2205.04733},
+}
+```
+
+#### SparseMultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+#### FlopsLoss
+```bibtex
+@article{paria2020minimizing,
+    title={Minimizing flops to learn efficient sparse representations},
+    author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
+    journal={arXiv preprint arXiv:2004.05665},
+    year={2020}
+    }
+```
+
+<!--
+## 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 +1,23 @@
+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForMaskedLM"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.4",
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SparseEncoder",
+  "__version__": {
+    "sentence_transformers": "4.2.0.dev0",
+    "transformers": "4.52.4",
+    "pytorch": "2.7.1+cu126"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "dot"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ece0c5b4cbefbb81d45ad9c6445a55c17a666087262f987c6fb54e1c7e1a264d
+size 267954768

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.sparse_encoder.models.MLMTransformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_SpladePooling",
+    "type": "sentence_transformers.sparse_encoder.models.SpladePooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 256,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}