Add new SparseEncoder model

baa6396 verified 3 days ago

48.8 kB

	---
	language:
	- en
	license: apache-2.0
	tags:
	- sentence-transformers
	- sparse-encoder
	- sparse
	- csr
	- generated_from_trainer
	- dataset_size:99000
	- loss:CSRLoss
	- loss:SparseMultipleNegativesRankingLoss
	base_model: mixedbread-ai/mxbai-embed-large-v1
	widget:
	- text: Saudi Arabia–United Arab Emirates relations However, the UAE and Saudi Arabia
	continue to take somewhat differing stances on regional conflicts such the Yemeni
	Civil War, where the UAE opposes Al-Islah, and supports the Southern Movement,
	which has fought against Saudi-backed forces, and the Syrian Civil War, where
	the UAE has disagreed with Saudi support for Islamist movements.[4]
	- text: Economy of New Zealand New Zealand's diverse market economy has a sizable
	service sector, accounting for 63% of all GDP activity in 2013.[17] Large scale
	manufacturing industries include aluminium production, food processing, metal
	fabrication, wood and paper products. Mining, manufacturing, electricity, gas,
	water, and waste services accounted for 16.5% of GDP in 2013.[17] The primary
	sector continues to dominate New Zealand's exports, despite accounting for 6.5%
	of GDP in 2013.[17]
	- text: who was the first president of indian science congress meeting held in kolkata
	in 1914
	- text: Get Over It (Eagles song) "Get Over It" is a song by the Eagles released as
	a single after a fourteen-year breakup. It was also the first song written by
	bandmates Don Henley and Glenn Frey when the band reunited. "Get Over It" was
	played live for the first time during their Hell Freezes Over tour in 1994. It
	returned the band to the U.S. Top 40 after a fourteen-year absence, peaking at
	No. 31 on the Billboard Hot 100 chart. It also hit No. 4 on the Billboard Mainstream
	Rock Tracks chart. The song was not played live by the Eagles after the "Hell
	Freezes Over" tour in 1994. It remains the group's last Top 40 hit in the U.S.
	- text: 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion
	who is considered by Christians to be one of the first Gentiles to convert to
	the faith, as related in Acts of the Apostles.'
	datasets:
	- sentence-transformers/natural-questions
	pipeline_tag: feature-extraction
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy@1
	- cosine_accuracy@3
	- cosine_accuracy@5
	- cosine_accuracy@10
	- cosine_precision@1
	- cosine_precision@3
	- cosine_precision@5
	- cosine_precision@10
	- cosine_recall@1
	- cosine_recall@3
	- cosine_recall@5
	- cosine_recall@10
	- cosine_ndcg@10
	- cosine_mrr@10
	- cosine_map@100
	- query_active_dims
	- query_sparsity_ratio
	- corpus_active_dims
	- corpus_sparsity_ratio
	co2_eq_emissions:
	emissions: 42.81821457704325
	energy_consumed: 0.11015691860871116
	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: 0.274
	hardware_used: 1 x NVIDIA GeForce RTX 3090
	model-index:
	- name: Sparse CSR model trained on Natural Questions
	results:
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: nq eval 4
	type: nq_eval_4
	metrics:
	- type: cosine_accuracy@1
	value: 0.341
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.53
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.616
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.71
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.341
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.1766666666666667
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.12319999999999999
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.071
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.341
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.53
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.616
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.71
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.5177559532868556
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.4569571428571428
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.46808238304226085
	name: Cosine Map@100
	- type: query_active_dims
	value: 4.0
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9990234375
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 4.0
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9990234375
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: nq eval 8
	type: nq_eval_8
	metrics:
	- type: cosine_accuracy@1
	value: 0.479
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.683
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.743
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.827
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.479
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.22766666666666666
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.14859999999999998
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.08270000000000001
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.479
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.683
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.743
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.827
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.6514732993360963
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.5954253968253969
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.602459158736598
	name: Cosine Map@100
	- type: query_active_dims
	value: 8.0
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.998046875
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 8.0
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.998046875
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: nq eval 16
	type: nq_eval_16
	metrics:
	- type: cosine_accuracy@1
	value: 0.61
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.792
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.843
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.61
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.264
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.16860000000000003
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.61
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.792
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.843
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.7573375805688765
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7114896825396828
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.7159603693257915
	name: Cosine Map@100
	- type: query_active_dims
	value: 16.0
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.99609375
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 16.0
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.99609375
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: nq eval 32
	type: nq_eval_32
	metrics:
	- type: cosine_accuracy@1
	value: 0.739
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.871
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.899
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.936
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.739
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.2903333333333333
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.17980000000000002
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.0936
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.739
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.871
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.899
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.936
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.8407099394827843
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.8098075396825399
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.8124255549328265
	name: Cosine Map@100
	- type: query_active_dims
	value: 32.0
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9921875
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 32.0
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9921875
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: nq eval 64
	type: nq_eval_64
	metrics:
	- type: cosine_accuracy@1
	value: 0.775
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.895
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.925
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.951
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.775
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.2983333333333333
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.18500000000000003
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.0951
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.775
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.895
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.925
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.951
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.8672657281787072
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.8399420634920639
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.8417827624389276
	name: Cosine Map@100
	- type: query_active_dims
	value: 63.992000579833984
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.984376952983439
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 64.0
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.984375
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: nq eval 128
	type: nq_eval_128
	metrics:
	- type: cosine_accuracy@1
	value: 0.797
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.901
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.933
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.951
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.797
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.30033333333333334
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.18660000000000002
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.0951
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.797
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.901
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.933
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.951
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.8780719613731008
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.8541857142857148
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.8561013158199787
	name: Cosine Map@100
	- type: query_active_dims
	value: 119.21700286865234
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9708942864090204
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 119.6520004272461
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9707880858331919
	name: Corpus Sparsity Ratio
	- task:
	type: sparse-information-retrieval
	name: Sparse Information Retrieval
	dataset:
	name: nq eval 256
	type: nq_eval_256
	metrics:
	- type: cosine_accuracy@1
	value: 0.8
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.901
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.933
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.951
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.8
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.30033333333333334
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.18660000000000002
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.0951
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.8
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.901
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.933
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.951
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.8788975201919854
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.8553369047619053
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.8573055135070745
	name: Cosine Map@100
	- type: query_active_dims
	value: 133.42999267578125
	name: Query Active Dims
	- type: query_sparsity_ratio
	value: 0.9674243181943893
	name: Query Sparsity Ratio
	- type: corpus_active_dims
	value: 129.16900634765625
	name: Corpus Active Dims
	- type: corpus_sparsity_ratio
	value: 0.9684645980596542
	name: Corpus Sparsity Ratio
	---

	# Sparse CSR model trained on Natural Questions

	This is a [CSR Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 4096-dimensional sparse vector space with 256 maximum active dimensions and can be used for semantic search and sparse retrieval.
	## Model Details

	### Model Description
	- Model Type: CSR Sparse Encoder
	- Base model: [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) <!-- at revision db9d1fe0f31addb4978201b2bf3e577f3f8900d2 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 4096 dimensions (trained with 256 maximum active dimensions)
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
	- 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): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	(2): CSRSparsity({'input_dim': 1024, 'hidden_dim': 4096, 'k': 256, 'k_aux': 512, 'normalize': False, 'dead_threshold': 30})
	)
	```

	## 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/csr-mxbai-embed-large-v1-nq-cos-sim-scale-5-gamma-1-detach-2")
	# Run inference
	queries = [
	"who is cornelius in the book of acts",
	]
	documents = [
	'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.',
	"Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their head of story.[1] There he worked on all of their films produced up to 2006; this included Toy Story (for which he received an Academy Award nomination) and A Bug's Life, as the co-story writer and others as story supervisor. His final film was Cars. He also voiced characters in many of the films, including Heimlich the caterpillar in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in Finding Nemo.[1]",
	'Wonderful Tonight "Wonderful Tonight" is a ballad written by Eric Clapton. It was included on Clapton\'s 1977 album Slowhand. Clapton wrote the song about Pattie Boyd.[1] The female vocal harmonies on the song are provided by Marcella Detroit (then Marcy Levy) and Yvonne Elliman.',
	]
	query_embeddings = model.encode_query(queries)
	document_embeddings = model.encode_document(documents)
	print(query_embeddings.shape, document_embeddings.shape)
	# [1, 4096] [3, 4096]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(query_embeddings, document_embeddings)
	print(similarities)
	# tensor([[0.8907, 0.0410, 0.0237]])
	```

	<!--
	### 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

	* Dataset: `nq_eval_4`
	* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
	```json
	{
	"max_active_dims": 4
	}
	```

	\| Metric \| Value \|
	\|:----------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.341 \|
	\| cosine_accuracy@3 \| 0.53 \|
	\| cosine_accuracy@5 \| 0.616 \|
	\| cosine_accuracy@10 \| 0.71 \|
	\| cosine_precision@1 \| 0.341 \|
	\| cosine_precision@3 \| 0.1767 \|
	\| cosine_precision@5 \| 0.1232 \|
	\| cosine_precision@10 \| 0.071 \|
	\| cosine_recall@1 \| 0.341 \|
	\| cosine_recall@3 \| 0.53 \|
	\| cosine_recall@5 \| 0.616 \|
	\| cosine_recall@10 \| 0.71 \|
	\| cosine_ndcg@10 \| 0.5178 \|
	\| cosine_mrr@10 \| 0.457 \|
	\| cosine_map@100 \| 0.4681 \|
	\| query_active_dims \| 4.0 \|
	\| query_sparsity_ratio \| 0.999 \|
	\| corpus_active_dims \| 4.0 \|
	\| corpus_sparsity_ratio \| 0.999 \|

	#### Sparse Information Retrieval

	* Dataset: `nq_eval_8`
	* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
	```json
	{
	"max_active_dims": 8
	}
	```

	\| Metric \| Value \|
	\|:----------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.479 \|
	\| cosine_accuracy@3 \| 0.683 \|
	\| cosine_accuracy@5 \| 0.743 \|
	\| cosine_accuracy@10 \| 0.827 \|
	\| cosine_precision@1 \| 0.479 \|
	\| cosine_precision@3 \| 0.2277 \|
	\| cosine_precision@5 \| 0.1486 \|
	\| cosine_precision@10 \| 0.0827 \|
	\| cosine_recall@1 \| 0.479 \|
	\| cosine_recall@3 \| 0.683 \|
	\| cosine_recall@5 \| 0.743 \|
	\| cosine_recall@10 \| 0.827 \|
	\| cosine_ndcg@10 \| 0.6515 \|
	\| cosine_mrr@10 \| 0.5954 \|
	\| cosine_map@100 \| 0.6025 \|
	\| query_active_dims \| 8.0 \|
	\| query_sparsity_ratio \| 0.998 \|
	\| corpus_active_dims \| 8.0 \|
	\| corpus_sparsity_ratio \| 0.998 \|

	#### Sparse Information Retrieval

	* Dataset: `nq_eval_16`
	* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
	```json
	{
	"max_active_dims": 16
	}
	```

	\| Metric \| Value \|
	\|:----------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.61 \|
	\| cosine_accuracy@3 \| 0.792 \|
	\| cosine_accuracy@5 \| 0.843 \|
	\| cosine_accuracy@10 \| 0.9 \|
	\| cosine_precision@1 \| 0.61 \|
	\| cosine_precision@3 \| 0.264 \|
	\| cosine_precision@5 \| 0.1686 \|
	\| cosine_precision@10 \| 0.09 \|
	\| cosine_recall@1 \| 0.61 \|
	\| cosine_recall@3 \| 0.792 \|
	\| cosine_recall@5 \| 0.843 \|
	\| cosine_recall@10 \| 0.9 \|
	\| cosine_ndcg@10 \| 0.7573 \|
	\| cosine_mrr@10 \| 0.7115 \|
	\| cosine_map@100 \| 0.716 \|
	\| query_active_dims \| 16.0 \|
	\| query_sparsity_ratio \| 0.9961 \|
	\| corpus_active_dims \| 16.0 \|
	\| corpus_sparsity_ratio \| 0.9961 \|

	#### Sparse Information Retrieval

	* Dataset: `nq_eval_32`
	* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
	```json
	{
	"max_active_dims": 32
	}
	```

	\| Metric \| Value \|
	\|:----------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.739 \|
	\| cosine_accuracy@3 \| 0.871 \|
	\| cosine_accuracy@5 \| 0.899 \|
	\| cosine_accuracy@10 \| 0.936 \|
	\| cosine_precision@1 \| 0.739 \|
	\| cosine_precision@3 \| 0.2903 \|
	\| cosine_precision@5 \| 0.1798 \|
	\| cosine_precision@10 \| 0.0936 \|
	\| cosine_recall@1 \| 0.739 \|
	\| cosine_recall@3 \| 0.871 \|
	\| cosine_recall@5 \| 0.899 \|
	\| cosine_recall@10 \| 0.936 \|
	\| cosine_ndcg@10 \| 0.8407 \|
	\| cosine_mrr@10 \| 0.8098 \|
	\| cosine_map@100 \| 0.8124 \|
	\| query_active_dims \| 32.0 \|
	\| query_sparsity_ratio \| 0.9922 \|
	\| corpus_active_dims \| 32.0 \|
	\| corpus_sparsity_ratio \| 0.9922 \|

	#### Sparse Information Retrieval

	* Dataset: `nq_eval_64`
	* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
	```json
	{
	"max_active_dims": 64
	}
	```

	\| Metric \| Value \|
	\|:----------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.775 \|
	\| cosine_accuracy@3 \| 0.895 \|
	\| cosine_accuracy@5 \| 0.925 \|
	\| cosine_accuracy@10 \| 0.951 \|
	\| cosine_precision@1 \| 0.775 \|
	\| cosine_precision@3 \| 0.2983 \|
	\| cosine_precision@5 \| 0.185 \|
	\| cosine_precision@10 \| 0.0951 \|
	\| cosine_recall@1 \| 0.775 \|
	\| cosine_recall@3 \| 0.895 \|
	\| cosine_recall@5 \| 0.925 \|
	\| cosine_recall@10 \| 0.951 \|
	\| cosine_ndcg@10 \| 0.8673 \|
	\| cosine_mrr@10 \| 0.8399 \|
	\| cosine_map@100 \| 0.8418 \|
	\| query_active_dims \| 63.992 \|
	\| query_sparsity_ratio \| 0.9844 \|
	\| corpus_active_dims \| 64.0 \|
	\| corpus_sparsity_ratio \| 0.9844 \|

	#### Sparse Information Retrieval

	* Dataset: `nq_eval_128`
	* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
	```json
	{
	"max_active_dims": 128
	}
	```

	\| Metric \| Value \|
	\|:----------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.797 \|
	\| cosine_accuracy@3 \| 0.901 \|
	\| cosine_accuracy@5 \| 0.933 \|
	\| cosine_accuracy@10 \| 0.951 \|
	\| cosine_precision@1 \| 0.797 \|
	\| cosine_precision@3 \| 0.3003 \|
	\| cosine_precision@5 \| 0.1866 \|
	\| cosine_precision@10 \| 0.0951 \|
	\| cosine_recall@1 \| 0.797 \|
	\| cosine_recall@3 \| 0.901 \|
	\| cosine_recall@5 \| 0.933 \|
	\| cosine_recall@10 \| 0.951 \|
	\| cosine_ndcg@10 \| 0.8781 \|
	\| cosine_mrr@10 \| 0.8542 \|
	\| cosine_map@100 \| 0.8561 \|
	\| query_active_dims \| 119.217 \|
	\| query_sparsity_ratio \| 0.9709 \|
	\| corpus_active_dims \| 119.652 \|
	\| corpus_sparsity_ratio \| 0.9708 \|

	#### Sparse Information Retrieval

	* Dataset: `nq_eval_256`
	* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
	```json
	{
	"max_active_dims": 256
	}
	```

	\| Metric \| Value \|
	\|:----------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.8 \|
	\| cosine_accuracy@3 \| 0.901 \|
	\| cosine_accuracy@5 \| 0.933 \|
	\| cosine_accuracy@10 \| 0.951 \|
	\| cosine_precision@1 \| 0.8 \|
	\| cosine_precision@3 \| 0.3003 \|
	\| cosine_precision@5 \| 0.1866 \|
	\| cosine_precision@10 \| 0.0951 \|
	\| cosine_recall@1 \| 0.8 \|
	\| cosine_recall@3 \| 0.901 \|
	\| cosine_recall@5 \| 0.933 \|
	\| cosine_recall@10 \| 0.951 \|
	\| cosine_ndcg@10 \| 0.8789 \|
	\| cosine_mrr@10 \| 0.8553 \|
	\| cosine_map@100 \| 0.8573 \|
	\| query_active_dims \| 133.43 \|
	\| query_sparsity_ratio \| 0.9674 \|
	\| corpus_active_dims \| 129.169 \|
	\| corpus_sparsity_ratio \| 0.9685 \|

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	## Training Details

	### Training Dataset

	#### natural-questions

	* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
	* Size: 99,000 training samples
	* Columns: <code>query</code> and <code>answer</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| answer \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</li></ul> \|
	* Samples:
	\| query \| answer \|
	\|:--------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>who played the father in papa don't preach</code> \| <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code> \|
	\| <code>where was the location of the battle of hastings</code> \| <code>Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.</code> \|
	\| <code>how many puppies can a dog give birth to</code> \| <code>Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]</code> \|
	* Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters:
	```json
	{
	"beta": 0.1,
	"gamma": 1.0,
	"loss": "SparseMultipleNegativesRankingLoss(scale=5.0, similarity_fct='cos_sim')"
	}
	```

	### Evaluation Dataset

	#### natural-questions

	* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
	* Size: 1,000 evaluation samples
	* Columns: <code>query</code> and <code>answer</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| answer \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 23 tokens</li></ul> \| <ul><li>min: 15 tokens</li><li>mean: 134.01 tokens</li><li>max: 512 tokens</li></ul> \|
	* Samples:
	\| query \| answer \|
	\|:-------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>where is the tiber river located in italy</code> \| <code>Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.</code> \|
	\| <code>what kind of car does jay gatsby drive</code> \| <code>Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.</code> \|
	\| <code>who sings if i can dream about you</code> \| <code>I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]</code> \|
	* Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters:
	```json
	{
	"beta": 0.1,
	"gamma": 1.0,
	"loss": "SparseMultipleNegativesRankingLoss(scale=5.0, similarity_fct='cos_sim')"
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `learning_rate`: 4e-05
	- `num_train_epochs`: 1
	- `bf16`: True
	- `batch_sampler`: no_duplicates

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `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`: 4e-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`: False
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_min_num_params`: 0
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `fsdp_transformer_layer_cls_to_wrap`: None
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch
	- `optim_args`: None
	- `adafactor`: False
	- `group_by_length`: False
	- `length_column_name`: length
	- `ddp_find_unused_parameters`: None
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `skip_memory_metrics`: True
	- `use_legacy_prediction_loop`: False
	- `push_to_hub`: False
	- `resume_from_checkpoint`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_private_repo`: 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 \| nq_eval_4_cosine_ndcg@10 \| nq_eval_8_cosine_ndcg@10 \| nq_eval_16_cosine_ndcg@10 \| nq_eval_32_cosine_ndcg@10 \| nq_eval_64_cosine_ndcg@10 \| nq_eval_128_cosine_ndcg@10 \| nq_eval_256_cosine_ndcg@10 \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|:------------------------:\|:------------------------:\|:-------------------------:\|:-------------------------:\|:-------------------------:\|:--------------------------:\|:--------------------------:\|
	\| -1 \| -1 \| - \| - \| 0.2566 \| 0.4513 \| 0.6853 \| 0.8617 \| 0.9369 \| 0.9685 \| 0.9757 \|
	\| 0.0646 \| 100 \| 2.9836 \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1293 \| 200 \| 2.7758 \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1939 \| 300 \| 2.6386 \| 2.3891 \| 0.4003 \| 0.5884 \| 0.7387 \| 0.8220 \| 0.8695 \| 0.9164 \| 0.9372 \|
	\| 0.2586 \| 400 \| 2.5466 \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3232 \| 500 \| 2.4711 \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3878 \| 600 \| 2.3918 \| 2.1817 \| 0.4580 \| 0.6189 \| 0.7230 \| 0.7986 \| 0.8554 \| 0.8939 \| 0.9146 \|
	\| 0.4525 \| 700 \| 2.2802 \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5171 \| 800 \| 2.1309 \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5818 \| 900 \| 2.0585 \| 1.8844 \| 0.4932 \| 0.6402 \| 0.7482 \| 0.8361 \| 0.8665 \| 0.8857 \| 0.8895 \|
	\| 0.6464 \| 1000 \| 2.0203 \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7111 \| 1100 \| 1.9934 \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7757 \| 1200 \| 1.9734 \| 1.8208 \| 0.5168 \| 0.6452 \| 0.7592 \| 0.8371 \| 0.8690 \| 0.8775 \| 0.8804 \|
	\| 0.8403 \| 1300 \| 1.9583 \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9050 \| 1400 \| 1.9496 \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9696 \| 1500 \| 1.9499 \| 1.8020 \| 0.5159 \| 0.6536 \| 0.7568 \| 0.8399 \| 0.8670 \| 0.8785 \| 0.8778 \|
	\| -1 \| -1 \| - \| - \| 0.5178 \| 0.6515 \| 0.7573 \| 0.8407 \| 0.8673 \| 0.8781 \| 0.8789 \|


	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Energy Consumed: 0.110 kWh
	- Carbon Emitted: 0.043 kg of CO2
	- Hours Used: 0.274 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: 2.6.0+cu124
	- 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",
	}
	```

	#### CSRLoss
	```bibtex
	@misc{wen2025matryoshkarevisitingsparsecoding,
	title={Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation},
	author={Tiansheng Wen and Yifei Wang and Zequn Zeng and Zhong Peng and Yudi Su and Xinyang Liu and Bo Chen and Hongwei Liu and Stefanie Jegelka and Chenyu You},
	year={2025},
	eprint={2503.01776},
	archivePrefix={arXiv},
	primaryClass={cs.LG},
	url={https://arxiv.org/abs/2503.01776},
	}
	```

	#### 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}
	}
	```

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