Add new SparseEncoder model

7b505e6 verified about 2 months ago

87.1 kB

	---
	language:
	- en
	license: apache-2.0
	tags:
	- sentence-transformers
	- sparse-encoder
	- sparse
	- splade
	- generated_from_trainer
	- dataset_size:99000
	- loss:SpladeLoss
	- loss:SparseMultipleNegativesRankingLoss
	- loss:FlopsLoss
	base_model: distilbert/distilbert-base-uncased
	widget:
	- text: How do I know if a girl likes me at school?
	- text: What are some five star hotel in Jaipur?
	- text: Is it normal to fantasize your wife having sex with another man?
	- text: What is the Sahara, and how do the average temperatures there compare to the
	ones in the Simpson Desert?
	- text: What are Hillary Clinton's most recognized accomplishments while Secretary
	of State?
	datasets:
	- sentence-transformers/quora-duplicates
	pipeline_tag: feature-extraction
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy
	- cosine_accuracy_threshold
	- cosine_f1
	- cosine_f1_threshold
	- cosine_precision
	- cosine_recall
	- cosine_ap
	- cosine_mcc
	- dot_accuracy
	- dot_accuracy_threshold
	- dot_f1
	- dot_f1_threshold
	- dot_precision
	- dot_recall
	- dot_ap
	- dot_mcc
	- euclidean_accuracy
	- euclidean_accuracy_threshold
	- euclidean_f1
	- euclidean_f1_threshold
	- euclidean_precision
	- euclidean_recall
	- euclidean_ap
	- euclidean_mcc
	- manhattan_accuracy
	- manhattan_accuracy_threshold
	- manhattan_f1
	- manhattan_f1_threshold
	- manhattan_precision
	- manhattan_recall
	- manhattan_ap
	- manhattan_mcc
	- max_accuracy
	- max_accuracy_threshold
	- max_f1
	- max_f1_threshold
	- max_precision
	- max_recall
	- max_ap
	- max_mcc
	- active_dims
	- sparsity_ratio
	- 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: 1.4164940270091377
	energy_consumed: 0.02527693261851813
	source: codecarbon
	training_type: fine-tuning
	on_cloud: false
	cpu_model: AMD Ryzen 9 6900HX with Radeon Graphics
	ram_total_size: 30.6114501953125
	hours_used: 0.222
	hardware_used: 1 x NVIDIA GeForce RTX 3070 Ti Laptop GPU
	model-index:
	- name: splade-distilbert-base-uncased trained on Quora Duplicates Questions
	results:
	- task:
	type: sparse-binary-classification
	name: Sparse Binary Classification
	dataset:
	name: quora duplicates dev
	type: quora_duplicates_dev
	metrics:
	- type: cosine_accuracy
	value: 0.758
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.8166326284408569
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 0.6792899408284023
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.5695896148681641
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 0.5487571701720841
	name: Cosine Precision
	- type: cosine_recall
	value: 0.8913043478260869
	name: Cosine Recall
	- type: cosine_ap
	value: 0.6887627674706448
	name: Cosine Ap
	- type: cosine_mcc
	value: 0.508171027288805
	name: Cosine Mcc
	- type: dot_accuracy
	value: 0.765
	name: Dot Accuracy
	- type: dot_accuracy_threshold
	value: 51.6699104309082
	name: Dot Accuracy Threshold
	- type: dot_f1
	value: 0.6762028608582575
	name: Dot F1
	- type: dot_f1_threshold
	value: 46.524925231933594
	name: Dot F1 Threshold
	- type: dot_precision
	value: 0.5816554809843401
	name: Dot Precision
	- type: dot_recall
	value: 0.8074534161490683
	name: Dot Recall
	- type: dot_ap
	value: 0.6335823489360819
	name: Dot Ap
	- type: dot_mcc
	value: 0.4996270089694481
	name: Dot Mcc
	- type: euclidean_accuracy
	value: 0.677
	name: Euclidean Accuracy
	- type: euclidean_accuracy_threshold
	value: -14.272356986999512
	name: Euclidean Accuracy Threshold
	- type: euclidean_f1
	value: 0.48599545798637395
	name: Euclidean F1
	- type: euclidean_f1_threshold
	value: -0.6444530487060547
	name: Euclidean F1 Threshold
	- type: euclidean_precision
	value: 0.3213213213213213
	name: Euclidean Precision
	- type: euclidean_recall
	value: 0.9968944099378882
	name: Euclidean Recall
	- type: euclidean_ap
	value: 0.2032823056922341
	name: Euclidean Ap
	- type: euclidean_mcc
	value: -0.04590966956831287
	name: Euclidean Mcc
	- type: manhattan_accuracy
	value: 0.677
	name: Manhattan Accuracy
	- type: manhattan_accuracy_threshold
	value: -161.77682495117188
	name: Manhattan Accuracy Threshold
	- type: manhattan_f1
	value: 0.48599545798637395
	name: Manhattan F1
	- type: manhattan_f1_threshold
	value: -3.0494537353515625
	name: Manhattan F1 Threshold
	- type: manhattan_precision
	value: 0.3213213213213213
	name: Manhattan Precision
	- type: manhattan_recall
	value: 0.9968944099378882
	name: Manhattan Recall
	- type: manhattan_ap
	value: 0.20444314945561334
	name: Manhattan Ap
	- type: manhattan_mcc
	value: -0.04590966956831287
	name: Manhattan Mcc
	- type: max_accuracy
	value: 0.765
	name: Max Accuracy
	- type: max_accuracy_threshold
	value: 51.6699104309082
	name: Max Accuracy Threshold
	- type: max_f1
	value: 0.6792899408284023
	name: Max F1
	- type: max_f1_threshold
	value: 46.524925231933594
	name: Max F1 Threshold
	- type: max_precision
	value: 0.5816554809843401
	name: Max Precision
	- type: max_recall
	value: 0.9968944099378882
	name: Max Recall
	- type: max_ap
	value: 0.6887627674706448
	name: Max Ap
	- type: max_mcc
	value: 0.508171027288805
	name: Max Mcc
	- type: active_dims
	value: 78.32280731201172
	name: Active Dims
	- type: sparsity_ratio
	value: 0.9974338900690646
	name: Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: NanoMSMARCO
	type: NanoMSMARCO
	metrics:
	- type: dot_accuracy@1
	value: 0.22
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.42
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.52
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.76
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.22
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.13999999999999999
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.10400000000000001
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.07600000000000001
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.22
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.42
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.52
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.76
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.45321847177875746
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.3601269841269841
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.37334906504034243
	name: Dot Map@100
	- type: query_active_dims
	value: 74.76000213623047
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9975506191554868
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 103.06523895263672
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9966232475279261
	name: Corpus Sparsity Ratio
	- type: dot_accuracy@1
	value: 0.22
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.42
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.52
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.76
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.22
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.13999999999999999
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.10400000000000001
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.07600000000000001
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.22
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.42
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.52
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.76
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.45321847177875746
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.3601269841269841
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.37334906504034243
	name: Dot Map@100
	- type: query_active_dims
	value: 74.76000213623047
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9975506191554868
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 103.06523895263672
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9966232475279261
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: NanoNQ
	type: NanoNQ
	metrics:
	- type: dot_accuracy@1
	value: 0.38
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.54
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.62
	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: 0.18
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.12400000000000003
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.06400000000000002
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.36
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.52
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.6
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.61
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.4828377104499333
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.4536666666666666
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.445384784044708
	name: Dot Map@100
	- type: query_active_dims
	value: 74.73999786376953
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9975512745605213
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 141.31478881835938
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9953700678586476
	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: 0.62
	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: 0.18
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.12400000000000003
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.06400000000000002
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.36
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.52
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.6
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.61
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.4828377104499333
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.4536666666666666
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.445384784044708
	name: Dot Map@100
	- type: query_active_dims
	value: 74.73999786376953
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9975512745605213
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 141.31478881835938
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9953700678586476
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: NanoNFCorpus
	type: NanoNFCorpus
	metrics:
	- type: dot_accuracy@1
	value: 0.34
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.5
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.54
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.58
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.34
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.30666666666666664
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.26
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.198
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.011597172822497613
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.06058581579610722
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.08260772201759854
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.09800124609193644
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.2466972614666078
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.42200000000000004
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.09401937795309984
	name: Dot Map@100
	- type: query_active_dims
	value: 79.69999694824219
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9973887688569477
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 202.17269897460938
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9933761647672298
	name: Corpus Sparsity Ratio
	- type: dot_accuracy@1
	value: 0.34
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.5
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.54
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.58
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.34
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.30666666666666664
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.26
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.198
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.011597172822497613
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.06058581579610722
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.08260772201759854
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.09800124609193644
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.2466972614666078
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.42200000000000004
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.09401937795309984
	name: Dot Map@100
	- type: query_active_dims
	value: 79.69999694824219
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9973887688569477
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 202.17269897460938
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9933761647672298
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: NanoQuoraRetrieval
	type: NanoQuoraRetrieval
	metrics:
	- type: dot_accuracy@1
	value: 0.94
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.98
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.98
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.98
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.94
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.3933333333333333
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.24799999999999997
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.13199999999999998
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.8173333333333332
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.9279999999999999
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.946
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.97
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.9467235239993945
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.96
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.9290737327188939
	name: Dot Map@100
	- type: query_active_dims
	value: 76.58000183105469
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9974909900455063
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 77.59056854248047
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9974578805929336
	name: Corpus Sparsity Ratio
	- type: dot_accuracy@1
	value: 0.94
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.98
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.98
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.98
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.94
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.3933333333333333
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.24799999999999997
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.13199999999999998
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.8173333333333332
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.9279999999999999
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.946
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.97
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.9467235239993945
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.96
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.9290737327188939
	name: Dot Map@100
	- type: query_active_dims
	value: 76.58000183105469
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9974909900455063
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 77.59056854248047
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9974578805929336
	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: 0.47
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.61
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.665
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.735
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.47
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.255
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.184
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.1175
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.3522326265389577
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.4821464539490268
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.5371519305043997
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.6095003115229841
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.5323692419236733
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.5489484126984127
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.46045673993926106
	name: Dot Map@100
	- type: query_active_dims
	value: 76.44499969482422
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9974954131546155
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 122.79780664247188
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9959767444255792
	name: Corpus Sparsity Ratio
	- type: dot_accuracy@1
	value: 0.4359811616954475
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.6088540031397174
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.6659026687598116
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.7383987441130299
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.4359811616954475
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.2725170068027211
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.2089481946624804
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.14605965463108322
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.2532746332292894
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.3813452238818861
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.4363867898661836
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.5099503000039356
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.4684519639817077
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.5328029827315542
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.39738635557561647
	name: Dot Map@100
	- type: query_active_dims
	value: 90.39137197532713
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9970384846348428
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 152.36685474307478
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9950079662295042
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: NanoClimateFEVER
	type: NanoClimateFEVER
	metrics:
	- type: dot_accuracy@1
	value: 0.18
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.32
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.4
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.48
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.18
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.10666666666666666
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.08400000000000002
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.054000000000000006
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.085
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.14666666666666667
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.17833333333333332
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.215
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.1845115403570178
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.2674126984126984
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.1475834110231865
	name: Dot Map@100
	- type: query_active_dims
	value: 89.86000061035156
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9970558940891701
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 221.75527954101562
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.992734575730915
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: NanoDBPedia
	type: NanoDBPedia
	metrics:
	- type: dot_accuracy@1
	value: 0.6
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.84
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.84
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.92
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.6
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.5266666666666666
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.456
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.4220000000000001
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.04570544957623723
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.15367137863132574
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.1908008582920462
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.293554014064817
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.5070720730882787
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.7147222222222225
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.3906658166774757
	name: Dot Map@100
	- type: query_active_dims
	value: 69.5199966430664
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.997722298779796
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 135.93350219726562
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9955463763122578
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: NanoFEVER
	type: NanoFEVER
	metrics:
	- type: dot_accuracy@1
	value: 0.58
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.76
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.8
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.86
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.58
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.26666666666666666
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.16799999999999998
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.09
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.5466666666666666
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.7466666666666667
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.7866666666666667
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.8466666666666667
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.7069849294263234
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.6765000000000001
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.6651380090497737
	name: Dot Map@100
	- type: query_active_dims
	value: 89.87999725341797
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9970552389340994
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 221.215576171875
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9927522581688004
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: NanoFiQA2018
	type: NanoFiQA2018
	metrics:
	- type: dot_accuracy@1
	value: 0.28
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.42
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.46
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.5
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.28
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.18
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.136
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.08399999999999999
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.14183333333333334
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.24288888888888888
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.27715873015873016
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.3288730158730159
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.28813286680239514
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.3561904761904763
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.2415362537997973
	name: Dot Map@100
	- type: query_active_dims
	value: 82.86000061035156
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9972852368583202
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 130.93699645996094
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9957100780925245
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: NanoHotpotQA
	type: NanoHotpotQA
	metrics:
	- type: dot_accuracy@1
	value: 0.78
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.84
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.92
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.98
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.78
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.3733333333333333
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.28400000000000003
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.16
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.39
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.56
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.71
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.8
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.7143331285788386
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.8361904761904762
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.6181181734895289
	name: Dot Map@100
	- type: query_active_dims
	value: 91.9800033569336
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9969864359033833
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 152.01571655273438
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9950194706587794
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: NanoSCIDOCS
	type: NanoSCIDOCS
	metrics:
	- type: dot_accuracy@1
	value: 0.36
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.58
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.68
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.76
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.36
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.2733333333333333
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.21199999999999997
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.15199999999999997
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.07566666666666666
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.16966666666666666
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.21766666666666665
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.31066666666666665
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.30291194083231554
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.4943888888888889
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.21666464487074008
	name: Dot Map@100
	- type: query_active_dims
	value: 94.30000305175781
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.996910425167035
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 199.64630126953125
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9934589377737524
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: NanoArguAna
	type: NanoArguAna
	metrics:
	- type: dot_accuracy@1
	value: 0.1
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.34
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.42
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.44
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.1
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.1133333333333333
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.084
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.044000000000000004
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.1
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.34
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.42
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.44
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.2781554838544819
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.22466666666666665
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.2332757160696607
	name: Dot Map@100
	- type: query_active_dims
	value: 189.10000610351562
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9938044687077021
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 164.03329467773438
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9946257357093985
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: NanoSciFact
	type: NanoSciFact
	metrics:
	- type: dot_accuracy@1
	value: 0.52
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.62
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.64
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.76
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.52
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.21333333333333332
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.14
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.08399999999999999
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.475
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.58
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.615
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.74
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.6020710919940331
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.5799047619047619
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.5551340236204781
	name: Dot Map@100
	- type: query_active_dims
	value: 82.45999908447266
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9972983422094073
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 194.24940490722656
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9936357576532591
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: NanoTouche2020
	type: NanoTouche2020
	metrics:
	- type: dot_accuracy@1
	value: 0.3877551020408163
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.7551020408163265
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.8367346938775511
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.9591836734693877
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.3877551020408163
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.4693877551020407
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.4163265306122449
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.33877551020408164
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.02376760958202688
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.08934182714819683
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.12879429112534482
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.21659229068805946
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.37622550913382224
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.5806689342403627
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.2560796141253303
	name: Dot Map@100
	- type: query_active_dims
	value: 79.12245178222656
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9974076911151881
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 135.00782775878906
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9955767044178366
	name: Corpus Sparsity Ratio
	---

	# splade-distilbert-base-uncased trained on Quora Duplicates Questions

	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 [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) 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:
	- [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
	- 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("arthurbresnu/splade-distilbert-base-uncased-quora-duplicates")
	# Run inference
	sentences = [
	'What accomplishments did Hillary Clinton achieve during her time as Secretary of State?',
	"What are Hillary Clinton's most recognized accomplishments while Secretary of State?",
	'What are Hillary Clinton’s qualifications to be President?',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# (3, 30522)

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities.shape)
	# [3, 3]
	```

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	## Evaluation

	### Metrics

	#### Sparse Binary Classification

	* Dataset: `quora_duplicates_dev`
	* Evaluated with [<code>SparseBinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseBinaryClassificationEvaluator)

	\| Metric \| Value \|
	\|:-----------------------------\|:-----------\|
	\| cosine_accuracy \| 0.758 \|
	\| cosine_accuracy_threshold \| 0.8166 \|
	\| cosine_f1 \| 0.6793 \|
	\| cosine_f1_threshold \| 0.5696 \|
	\| cosine_precision \| 0.5488 \|
	\| cosine_recall \| 0.8913 \|
	\| cosine_ap \| 0.6888 \|
	\| cosine_mcc \| 0.5082 \|
	\| dot_accuracy \| 0.765 \|
	\| dot_accuracy_threshold \| 51.6699 \|
	\| dot_f1 \| 0.6762 \|
	\| dot_f1_threshold \| 46.5249 \|
	\| dot_precision \| 0.5817 \|
	\| dot_recall \| 0.8075 \|
	\| dot_ap \| 0.6336 \|
	\| dot_mcc \| 0.4996 \|
	\| euclidean_accuracy \| 0.677 \|
	\| euclidean_accuracy_threshold \| -14.2724 \|
	\| euclidean_f1 \| 0.486 \|
	\| euclidean_f1_threshold \| -0.6445 \|
	\| euclidean_precision \| 0.3213 \|
	\| euclidean_recall \| 0.9969 \|
	\| euclidean_ap \| 0.2033 \|
	\| euclidean_mcc \| -0.0459 \|
	\| manhattan_accuracy \| 0.677 \|
	\| manhattan_accuracy_threshold \| -161.7768 \|
	\| manhattan_f1 \| 0.486 \|
	\| manhattan_f1_threshold \| -3.0495 \|
	\| manhattan_precision \| 0.3213 \|
	\| manhattan_recall \| 0.9969 \|
	\| manhattan_ap \| 0.2044 \|
	\| manhattan_mcc \| -0.0459 \|
	\| max_accuracy \| 0.765 \|
	\| max_accuracy_threshold \| 51.6699 \|
	\| max_f1 \| 0.6793 \|
	\| max_f1_threshold \| 46.5249 \|
	\| max_precision \| 0.5817 \|
	\| max_recall \| 0.9969 \|
	\| max_ap \| 0.6888 \|
	\| max_mcc \| 0.5082 \|
	\| active_dims \| 78.3228 \|
	\| sparsity_ratio \| 0.9974 \|

	#### Sparse Information Retrieval

	* Datasets: `NanoMSMARCO`, `NanoNQ`, `NanoNFCorpus`, `NanoQuoraRetrieval`, `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 \| NanoNQ \| NanoNFCorpus \| NanoQuoraRetrieval \| NanoClimateFEVER \| NanoDBPedia \| NanoFEVER \| NanoFiQA2018 \| NanoHotpotQA \| NanoSCIDOCS \| NanoArguAna \| NanoSciFact \| NanoTouche2020 \|
	\|:----------------------\|:------------\|:-----------\|:-------------\|:-------------------\|:-----------------\|:------------\|:----------\|:-------------\|:-------------\|:------------\|:------------\|:------------\|:---------------\|
	\| dot_accuracy@1 \| 0.22 \| 0.38 \| 0.34 \| 0.94 \| 0.18 \| 0.6 \| 0.58 \| 0.28 \| 0.78 \| 0.36 \| 0.1 \| 0.52 \| 0.3878 \|
	\| dot_accuracy@3 \| 0.42 \| 0.54 \| 0.5 \| 0.98 \| 0.32 \| 0.84 \| 0.76 \| 0.42 \| 0.84 \| 0.58 \| 0.34 \| 0.62 \| 0.7551 \|
	\| dot_accuracy@5 \| 0.52 \| 0.62 \| 0.54 \| 0.98 \| 0.4 \| 0.84 \| 0.8 \| 0.46 \| 0.92 \| 0.68 \| 0.42 \| 0.64 \| 0.8367 \|
	\| dot_accuracy@10 \| 0.76 \| 0.62 \| 0.58 \| 0.98 \| 0.48 \| 0.92 \| 0.86 \| 0.5 \| 0.98 \| 0.76 \| 0.44 \| 0.76 \| 0.9592 \|
	\| dot_precision@1 \| 0.22 \| 0.38 \| 0.34 \| 0.94 \| 0.18 \| 0.6 \| 0.58 \| 0.28 \| 0.78 \| 0.36 \| 0.1 \| 0.52 \| 0.3878 \|
	\| dot_precision@3 \| 0.14 \| 0.18 \| 0.3067 \| 0.3933 \| 0.1067 \| 0.5267 \| 0.2667 \| 0.18 \| 0.3733 \| 0.2733 \| 0.1133 \| 0.2133 \| 0.4694 \|
	\| dot_precision@5 \| 0.104 \| 0.124 \| 0.26 \| 0.248 \| 0.084 \| 0.456 \| 0.168 \| 0.136 \| 0.284 \| 0.212 \| 0.084 \| 0.14 \| 0.4163 \|
	\| dot_precision@10 \| 0.076 \| 0.064 \| 0.198 \| 0.132 \| 0.054 \| 0.422 \| 0.09 \| 0.084 \| 0.16 \| 0.152 \| 0.044 \| 0.084 \| 0.3388 \|
	\| dot_recall@1 \| 0.22 \| 0.36 \| 0.0116 \| 0.8173 \| 0.085 \| 0.0457 \| 0.5467 \| 0.1418 \| 0.39 \| 0.0757 \| 0.1 \| 0.475 \| 0.0238 \|
	\| dot_recall@3 \| 0.42 \| 0.52 \| 0.0606 \| 0.928 \| 0.1467 \| 0.1537 \| 0.7467 \| 0.2429 \| 0.56 \| 0.1697 \| 0.34 \| 0.58 \| 0.0893 \|
	\| dot_recall@5 \| 0.52 \| 0.6 \| 0.0826 \| 0.946 \| 0.1783 \| 0.1908 \| 0.7867 \| 0.2772 \| 0.71 \| 0.2177 \| 0.42 \| 0.615 \| 0.1288 \|
	\| dot_recall@10 \| 0.76 \| 0.61 \| 0.098 \| 0.97 \| 0.215 \| 0.2936 \| 0.8467 \| 0.3289 \| 0.8 \| 0.3107 \| 0.44 \| 0.74 \| 0.2166 \|
	\| dot_ndcg@10 \| 0.4532 \| 0.4828 \| 0.2467 \| 0.9467 \| 0.1845 \| 0.5071 \| 0.707 \| 0.2881 \| 0.7143 \| 0.3029 \| 0.2782 \| 0.6021 \| 0.3762 \|
	\| dot_mrr@10 \| 0.3601 \| 0.4537 \| 0.422 \| 0.96 \| 0.2674 \| 0.7147 \| 0.6765 \| 0.3562 \| 0.8362 \| 0.4944 \| 0.2247 \| 0.5799 \| 0.5807 \|
	\| dot_map@100 \| 0.3733 \| 0.4454 \| 0.094 \| 0.9291 \| 0.1476 \| 0.3907 \| 0.6651 \| 0.2415 \| 0.6181 \| 0.2167 \| 0.2333 \| 0.5551 \| 0.2561 \|
	\| query_active_dims \| 74.76 \| 74.74 \| 79.7 \| 76.58 \| 89.86 \| 69.52 \| 89.88 \| 82.86 \| 91.98 \| 94.3 \| 189.1 \| 82.46 \| 79.1225 \|
	\| query_sparsity_ratio \| 0.9976 \| 0.9976 \| 0.9974 \| 0.9975 \| 0.9971 \| 0.9977 \| 0.9971 \| 0.9973 \| 0.997 \| 0.9969 \| 0.9938 \| 0.9973 \| 0.9974 \|
	\| corpus_active_dims \| 103.0652 \| 141.3148 \| 202.1727 \| 77.5906 \| 221.7553 \| 135.9335 \| 221.2156 \| 130.937 \| 152.0157 \| 199.6463 \| 164.0333 \| 194.2494 \| 135.0078 \|
	\| corpus_sparsity_ratio \| 0.9966 \| 0.9954 \| 0.9934 \| 0.9975 \| 0.9927 \| 0.9955 \| 0.9928 \| 0.9957 \| 0.995 \| 0.9935 \| 0.9946 \| 0.9936 \| 0.9956 \|

	#### 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",
	"nq",
	"nfcorpus",
	"quoraretrieval"
	]
	}
	```

	\| Metric \| Value \|
	\|:----------------------\|:-----------\|
	\| dot_accuracy@1 \| 0.47 \|
	\| dot_accuracy@3 \| 0.61 \|
	\| dot_accuracy@5 \| 0.665 \|
	\| dot_accuracy@10 \| 0.735 \|
	\| dot_precision@1 \| 0.47 \|
	\| dot_precision@3 \| 0.255 \|
	\| dot_precision@5 \| 0.184 \|
	\| dot_precision@10 \| 0.1175 \|
	\| dot_recall@1 \| 0.3522 \|
	\| dot_recall@3 \| 0.4821 \|
	\| dot_recall@5 \| 0.5372 \|
	\| dot_recall@10 \| 0.6095 \|
	\| dot_ndcg@10 \| 0.5324 \|
	\| dot_mrr@10 \| 0.5489 \|
	\| dot_map@100 \| 0.4605 \|
	\| query_active_dims \| 76.445 \|
	\| query_sparsity_ratio \| 0.9975 \|
	\| corpus_active_dims \| 122.7978 \|
	\| corpus_sparsity_ratio \| 0.996 \|

	#### 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 \| 0.436 \|
	\| dot_accuracy@3 \| 0.6089 \|
	\| dot_accuracy@5 \| 0.6659 \|
	\| dot_accuracy@10 \| 0.7384 \|
	\| dot_precision@1 \| 0.436 \|
	\| dot_precision@3 \| 0.2725 \|
	\| dot_precision@5 \| 0.2089 \|
	\| dot_precision@10 \| 0.1461 \|
	\| dot_recall@1 \| 0.2533 \|
	\| dot_recall@3 \| 0.3813 \|
	\| dot_recall@5 \| 0.4364 \|
	\| dot_recall@10 \| 0.51 \|
	\| dot_ndcg@10 \| 0.4685 \|
	\| dot_mrr@10 \| 0.5328 \|
	\| dot_map@100 \| 0.3974 \|
	\| query_active_dims \| 90.3914 \|
	\| query_sparsity_ratio \| 0.997 \|
	\| corpus_active_dims \| 152.3669 \|
	\| corpus_sparsity_ratio \| 0.995 \|

	<!--
	## 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

	#### quora-duplicates

	* Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
	* Size: 99,000 training samples
	* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \|
	\|:--------\|:---------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 6 tokens</li><li>mean: 14.1 tokens</li><li>max: 39 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 13.83 tokens</li><li>max: 41 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 15.21 tokens</li><li>max: 75 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:----------------------------------------------------------------------\|:---------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>What are the best GMAT coaching institutes in Delhi NCR?</code> \| <code>Which are the best GMAT coaching institutes in Delhi/NCR?</code> \| <code>What are the best GMAT coaching institutes in Delhi-Noida Area?</code> \|
	\| <code>Is a third world war coming?</code> \| <code>Is World War 3 more imminent than expected?</code> \| <code>Since the UN is unable to control terrorism and groups like ISIS, al-Qaeda and countries that promote terrorism (even though it consumed those countries), can we assume that the world is heading towards World War III?</code> \|
	\| <code>Should I build iOS or Android apps first?</code> \| <code>Should people choose Android or iOS first to build their App?</code> \| <code>How much more effort is it to build your app on both iOS and Android?</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": 3e-05,
	"lambda_query": 5e-05
	}
	```

	### Evaluation Dataset

	#### quora-duplicates

	* Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
	* Size: 1,000 evaluation samples
	* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 6 tokens</li><li>mean: 14.05 tokens</li><li>max: 40 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 14.14 tokens</li><li>max: 44 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 14.56 tokens</li><li>max: 60 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:-------------------------------------------------------------------\|:------------------------------------------------------------\|:-----------------------------------------------------------------\|
	\| <code>What happens if we use petrol in diesel vehicles?</code> \| <code>Why can't we use petrol in diesel?</code> \| <code>Why are diesel engines noisier than petrol engines?</code> \|
	\| <code>Why is Saltwater taffy candy imported in Switzerland?</code> \| <code>Why is Saltwater taffy candy imported in Laos?</code> \| <code>Is salt a consumer product?</code> \|
	\| <code>Which is your favourite film in 2016?</code> \| <code>What movie is the best movie of 2016?</code> \| <code>What will the best movie of 2017 be?</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": 3e-05,
	"lambda_query": 5e-05
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 12
	- `per_device_eval_batch_size`: 12
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 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`: 12
	- `per_device_eval_batch_size`: 12
	- `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`: 0.0
	- `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}
	- `tp_size`: 0
	- `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
	- `dispatch_batches`: None
	- `split_batches`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: False
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `eval_on_start`: False
	- `use_liger_kernel`: False
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| Validation Loss \| quora_duplicates_dev_max_ap \| NanoMSMARCO_dot_ndcg@10 \| NanoNQ_dot_ndcg@10 \| NanoNFCorpus_dot_ndcg@10 \| NanoQuoraRetrieval_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 \| NanoSCIDOCS_dot_ndcg@10 \| NanoArguAna_dot_ndcg@10 \| NanoSciFact_dot_ndcg@10 \| NanoTouche2020_dot_ndcg@10 \|
	\|:-------:\|:--------:\|:-------------:\|:---------------:\|:---------------------------:\|:-----------------------:\|:------------------:\|:------------------------:\|:------------------------------:\|:-------------------------:\|:----------------------------:\|:-----------------------:\|:---------------------:\|:------------------------:\|:------------------------:\|:-----------------------:\|:-----------------------:\|:-----------------------:\|:--------------------------:\|
	\| 0.0242 \| 200 \| 8.3389 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0485 \| 400 \| 0.4397 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0727 \| 600 \| 0.3737 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0970 \| 800 \| 0.2666 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1212 \| 1000 \| 0.288 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1455 \| 1200 \| 0.1977 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1697 \| 1400 \| 0.2707 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1939 \| 1600 \| 0.1951 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2 \| 1650 \| - \| 0.1669 \| 0.6472 \| 0.3052 \| 0.2793 \| 0.1711 \| 0.9281 \| 0.4209 \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2182 \| 1800 \| 0.2178 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2424 \| 2000 \| 0.2174 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2667 \| 2200 \| 0.1832 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2909 \| 2400 \| 0.1879 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3152 \| 2600 \| 0.1723 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3394 \| 2800 \| 0.1543 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3636 \| 3000 \| 0.1559 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3879 \| 3200 \| 0.1575 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4 \| 3300 \| - \| 0.1149 \| 0.6749 \| 0.3894 \| 0.4467 \| 0.2360 \| 0.9292 \| 0.5003 \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4121 \| 3400 \| 0.1395 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4364 \| 3600 \| 0.1596 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4606 \| 3800 \| 0.1595 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4848 \| 4000 \| 0.1211 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5091 \| 4200 \| 0.1163 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5333 \| 4400 \| 0.1182 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5576 \| 4600 \| 0.1337 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5818 \| 4800 \| 0.1362 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6 \| 4950 \| - \| 0.1001 \| 0.6802 \| 0.4093 \| 0.4269 \| 0.2341 \| 0.9365 \| 0.5017 \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6061 \| 5000 \| 0.1112 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6303 \| 5200 \| 0.1064 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6545 \| 5400 \| 0.119 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6788 \| 5600 \| 0.1077 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7030 \| 5800 \| 0.1398 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7273 \| 6000 \| 0.09 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7515 \| 6200 \| 0.0903 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7758 \| 6400 \| 0.1082 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8 \| 6600 \| 0.1122 \| 0.0901 \| 0.6941 \| 0.4451 \| 0.4757 \| 0.2542 \| 0.9411 \| 0.5290 \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8242 \| 6800 \| 0.0708 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8485 \| 7000 \| 0.1291 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8727 \| 7200 \| 0.1165 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8970 \| 7400 \| 0.0735 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9212 \| 7600 \| 0.0775 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9455 \| 7800 \| 0.0945 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9697 \| 8000 \| 0.0912 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9939 \| 8200 \| 0.104 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.0 \| 8250 \| - \| 0.0686 \| 0.6888 \| 0.4532 \| 0.4828 \| 0.2467 \| 0.9467 \| 0.5324 \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| -1 \| -1 \| - \| - \| - \| 0.4532 \| 0.4828 \| 0.2467 \| 0.9467 \| 0.4685 \| 0.1845 \| 0.5071 \| 0.7070 \| 0.2881 \| 0.7143 \| 0.3029 \| 0.2782 \| 0.6021 \| 0.3762 \|

	* The bold row denotes the saved checkpoint.

	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Energy Consumed: 0.025 kWh
	- Carbon Emitted: 0.001 kg of CO2
	- Hours Used: 0.222 hours

	### Training Hardware
	- On Cloud: No
	- GPU Model: 1 x NVIDIA GeForce RTX 3070 Ti Laptop GPU
	- CPU Model: AMD Ryzen 9 6900HX with Radeon Graphics
	- RAM Size: 30.61 GB

	### Framework Versions
	- Python: 3.12.9
	- Sentence Transformers: 4.2.0.dev0
	- Transformers: 4.50.3
	- PyTorch: 2.6.0+cu124
	- Accelerate: 1.6.0
	- Datasets: 3.5.0
	- 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}
	}
	```

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