metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sparse-encoder
  - sparse
  - csr
  - generated_from_trainer
  - dataset_size:3011496
  - loss:CSRLoss
  - loss:SparseMultipleNegativesRankingLoss
base_model: mixedbread-ai/mxbai-embed-large-v1
widget:
  - source_sentence: how much is a car title transfer in minnesota?
    sentences:
      - >-
        This complex is a larger molecule than the original crystal violet stain
        and iodine and is insoluble in water. ... Conversely, the the outer
        membrane of Gram negative bacteria is degraded and the thinner
        peptidoglycan layer of Gram negative cells is unable to retain the
        crystal violet-iodine complex and the color is lost.
      - >-
        Get insurance on the car and provide proof. Bring this information
        (including the title) to the Minnesota DVS office, as well as $10 for
        the filing fee and $7.25 for the titling fee. There is also a $10
        transfer tax, as well as a 6.5% sales tax on the purchase price.
      - >-
        One of the risks of DNP is that it accelerates the metabolism to a
        dangerously fast level. Our metabolic system operates at the rate it
        does for a reason – it is safe. Speeding up the metabolism may help burn
        off fat, but it can also trigger a number of potentially dangerous side
        effects, such as: fever.
  - source_sentence: what is the difference between 18 and 20 inch tires?
    sentences:
      - >-
        The only real difference is a 20" rim would be more likely to be
        damaged, as you pointed out. Beyond looks, there is zero benefit for the
        20" rim. Also, just the availability of tires will likely be much more
        limited for the larger rim. ... Tire selection is better for 18" wheels
        than 20" wheels.
      - >-
        ['Open your Outlook app on your mobile device and click on the Settings
        gear icon.', 'Under Settings, click on the Signature option.', 'Enter
        either a generic signature that could be used for all email accounts
        tied to your Outlook app, or a specific signature, Per Account
        Signature, for each email account.']
      - >-
        The average normal body temperature is around 98.6 degrees Fahrenheit,
        or 37 degrees Celsius. If your body temperature drops to just a few
        degrees lower than this, your blood vessels in your hands, feet, arms,
        and legs start to get narrower.
  - source_sentence: whom the bell tolls meaning?
    sentences:
      - >-
        Answer: Humans are depicted in Hindu art often in sensuous and erotic
        postures.
      - >-
        The phrase "For whom the bell tolls" refers to the church bells that are
        rung when a person dies. Hence, the author is suggesting that we should
        not be curious as to for whom the church bell is tolling for. It is for
        all of us.
      - '[''Automatically.'', ''When connected to car Bluetooth and,'', ''Manually.'']'
  - source_sentence: how long before chlamydia symptoms appear?
    sentences:
      - >-
        Most people who have chlamydia don't notice any symptoms. If you do get
        symptoms, these usually appear between 1 and 3 weeks after having
        unprotected sex with an infected person. For some people they don't
        develop until many months later. Sometimes the symptoms can disappear
        after a few days.
      - >-
        ['Open the My Verizon app . ... ', 'Tap the Menu icon. ... ', 'Tap
        Manage device for the appropriate mobile number. ... ', 'Tap Transfer
        content between phones. ... ', 'Tap Start Transfer.']
      - >-
        Psychiatrist vs Psychologist A psychiatrist is classed as a medical
        doctor, they include a physical examination of symptoms in their
        assessment and are able to prescribe medicine: a psychologist is also a
        doctor by virtue of their PHD level qualification, but is not medically
        trained and cannot prescribe.
  - source_sentence: are you human korean novela?
    sentences:
      - >-
        Many cysts heal on their own, which means that conservative treatments
        like rest and anti-inflammatory painkillers can often be enough to get
        rid of them. However, in some cases, routine drainage of the sac may be
        necessary to reduce symptoms.
      - >-
        A relative of European pear varieties like Bartlett and Anjou, the Asian
        pear is great used in recipes or simply eaten out of hand. It retains a
        crispness that works well in slaws and salads, and it holds its shape
        better than European pears when baked and cooked.
      - >-
        Are You Human? (Korean: 너도 인간이니; RR: Neodo Inganini; lit. Are You Human
        Too?) is a 2018 South Korean television series starring Seo Kang-jun and
        Gong Seung-yeon. It aired on KBS2's Mondays and Tuesdays at 22:00 (KST)
        time slot, from June 4 to August 7, 2018.
datasets:
  - sentence-transformers/gooaq
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
  - dot_accuracy@1
  - dot_accuracy@3
  - dot_accuracy@5
  - dot_accuracy@10
  - dot_precision@1
  - dot_precision@3
  - dot_precision@5
  - dot_precision@10
  - dot_recall@1
  - dot_recall@3
  - dot_recall@5
  - dot_recall@10
  - dot_ndcg@10
  - dot_mrr@10
  - dot_map@100
  - row_non_zero_mean_query
  - row_sparsity_mean_query
  - row_non_zero_mean_corpus
  - row_sparsity_mean_corpus
co2_eq_emissions:
  emissions: 467.36155743833086
  energy_consumed: 1.2023646840981803
  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: 3.125
  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: NanoMSMARCO 128
          type: NanoMSMARCO_128
        metrics:
          - type: dot_accuracy@1
            value: 0.42
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.64
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.68
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.8
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.42
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.21333333333333332
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.136
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.42
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.64
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.68
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.8
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.6079185617079585
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5469047619047619
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.5546949863343481
            name: Dot Map@100
          - type: row_non_zero_mean_query
            value: 128
            name: Row Non Zero Mean Query
          - type: row_sparsity_mean_query
            value: 0.96875
            name: Row Sparsity Mean Query
          - type: row_non_zero_mean_corpus
            value: 128
            name: Row Non Zero Mean Corpus
          - type: row_sparsity_mean_corpus
            value: 0.96875
            name: Row Sparsity Mean Corpus
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNFCorpus 128
          type: NanoNFCorpus_128
        metrics:
          - type: dot_accuracy@1
            value: 0.28
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.46
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.58
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.66
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.28
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2866666666666667
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.28
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.24600000000000002
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.010077778443246685
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.04965300165842144
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.07680443441830657
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.10785346110615711
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.27112973349418856
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.3951904761904761
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.10882673834779542
            name: Dot Map@100
          - type: row_non_zero_mean_query
            value: 128
            name: Row Non Zero Mean Query
          - type: row_sparsity_mean_query
            value: 0.96875
            name: Row Sparsity Mean Query
          - type: row_non_zero_mean_corpus
            value: 128
            name: Row Non Zero Mean Corpus
          - type: row_sparsity_mean_corpus
            value: 0.96875
            name: Row Sparsity Mean Corpus
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNQ 128
          type: NanoNQ_128
        metrics:
          - type: dot_accuracy@1
            value: 0.46
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.62
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.7
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.82
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.46
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.20666666666666667
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.14
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08199999999999999
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.44
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.58
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.65
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.76
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5976862103963738
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5692222222222223
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.5513454286143362
            name: Dot Map@100
          - type: row_non_zero_mean_query
            value: 128
            name: Row Non Zero Mean Query
          - type: row_sparsity_mean_query
            value: 0.96875
            name: Row Sparsity Mean Query
          - type: row_non_zero_mean_corpus
            value: 128
            name: Row Non Zero Mean Corpus
          - type: row_sparsity_mean_corpus
            value: 0.96875
            name: Row Sparsity Mean Corpus
      - task:
          type: sparse-nano-beir
          name: Sparse Nano BEIR
        dataset:
          name: NanoBEIR mean 128
          type: NanoBEIR_mean_128
        metrics:
          - type: dot_accuracy@1
            value: 0.38666666666666666
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.5733333333333334
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.6533333333333333
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.7599999999999999
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.38666666666666666
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.23555555555555555
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.18533333333333335
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.136
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.2900259261477489
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.4232176672194738
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.4689348114727689
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.5559511537020524
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.49224483519950696
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5037724867724868
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4049557177654933
            name: Dot Map@100
          - type: row_non_zero_mean_query
            value: 128
            name: Row Non Zero Mean Query
          - type: row_sparsity_mean_query
            value: 0.96875
            name: Row Sparsity Mean Query
          - type: row_non_zero_mean_corpus
            value: 128
            name: Row Non Zero Mean Corpus
          - type: row_sparsity_mean_corpus
            value: 0.96875
            name: Row Sparsity Mean Corpus
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoMSMARCO 256
          type: NanoMSMARCO_256
        metrics:
          - type: dot_accuracy@1
            value: 0.42
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.7
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.76
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.84
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.42
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2333333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.15200000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08399999999999999
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.42
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.7
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.76
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.84
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.6326016391887893
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.566111111111111
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.5727341193854673
            name: Dot Map@100
          - type: row_non_zero_mean_query
            value: 256
            name: Row Non Zero Mean Query
          - type: row_sparsity_mean_query
            value: 0.9375
            name: Row Sparsity Mean Query
          - type: row_non_zero_mean_corpus
            value: 256
            name: Row Non Zero Mean Corpus
          - type: row_sparsity_mean_corpus
            value: 0.9375
            name: Row Sparsity Mean Corpus
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNFCorpus 256
          type: NanoNFCorpus_256
        metrics:
          - type: dot_accuracy@1
            value: 0.32
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.56
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.62
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.7
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.32
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.31999999999999995
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.316
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.262
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.030392237560226815
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.0717373009745601
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.09312218308574575
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.133341363492939
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.30709320262394824
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.45252380952380944
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.14302697817666413
            name: Dot Map@100
          - type: row_non_zero_mean_query
            value: 256
            name: Row Non Zero Mean Query
          - type: row_sparsity_mean_query
            value: 0.9375
            name: Row Sparsity Mean Query
          - type: row_non_zero_mean_corpus
            value: 256
            name: Row Non Zero Mean Corpus
          - type: row_sparsity_mean_corpus
            value: 0.9375
            name: Row Sparsity Mean Corpus
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNQ 256
          type: NanoNQ_256
        metrics:
          - type: dot_accuracy@1
            value: 0.42
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.64
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.68
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.84
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.42
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.22
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.14
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.088
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.4
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.6
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.63
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.79
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.594269599796927
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5505952380952379
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.5330295920949546
            name: Dot Map@100
          - type: row_non_zero_mean_query
            value: 256
            name: Row Non Zero Mean Query
          - type: row_sparsity_mean_query
            value: 0.9375
            name: Row Sparsity Mean Query
          - type: row_non_zero_mean_corpus
            value: 256
            name: Row Non Zero Mean Corpus
          - type: row_sparsity_mean_corpus
            value: 0.9375
            name: Row Sparsity Mean Corpus
      - task:
          type: sparse-nano-beir
          name: Sparse Nano BEIR
        dataset:
          name: NanoBEIR mean 256
          type: NanoBEIR_mean_256
        metrics:
          - type: dot_accuracy@1
            value: 0.38666666666666666
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.6333333333333333
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.6866666666666666
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.7933333333333333
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.38666666666666666
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2577777777777777
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.2026666666666667
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.14466666666666664
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.28346407918674227
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.45724576699152
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.4943740610285819
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.5877804544976463
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5113214805365548
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5230767195767194
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.41626356321902863
            name: Dot Map@100
          - type: row_non_zero_mean_query
            value: 256
            name: Row Non Zero Mean Query
          - type: row_sparsity_mean_query
            value: 0.9375
            name: Row Sparsity Mean Query
          - type: row_non_zero_mean_corpus
            value: 256
            name: Row Non Zero Mean Corpus
          - type: row_sparsity_mean_corpus
            value: 0.9375
            name: Row Sparsity Mean Corpus

Sparse CSR model trained on Natural Questions

This is a CSR Sparse Encoder model finetuned from mixedbread-ai/mxbai-embed-large-v1 on the gooaq dataset using the sentence-transformers library. It maps sentences & paragraphs to a 4096-dimensional sparse vector space 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 4096 dimensions
Similarity Function: Dot Product
Training Dataset:
- gooaq
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Documentation: Sparse Encoder Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sparse Encoders on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SparseEncoder

# Download from the 🤗 Hub
model = SparseEncoder("tomaarsen/csr-mxbai-embed-large-v1-gooaq-2e-4")
# Run inference
sentences = [
    'are you human korean novela?',
    "Are You Human? (Korean: 너도 인간이니; RR: Neodo Inganini; lit. Are You Human Too?) is a 2018 South Korean television series starring Seo Kang-jun and Gong Seung-yeon. It aired on KBS2's Mondays and Tuesdays at 22:00 (KST) time slot, from June 4 to August 7, 2018.",
    'A relative of European pear varieties like Bartlett and Anjou, the Asian pear is great used in recipes or simply eaten out of hand. It retains a crispness that works well in slaws and salads, and it holds its shape better than European pears when baked and cooked.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# (3, 4096)

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

Evaluation

Metrics

Sparse Information Retrieval

Datasets: NanoMSMARCO_128, NanoNFCorpus_128 and NanoNQ_128
Evaluated with SparseInformationRetrievalEvaluator with these parameters:
```
{
    "max_active_dims": 128
}
```

Metric	NanoMSMARCO_128	NanoNFCorpus_128	NanoNQ_128
dot_accuracy@1	0.42	0.28	0.46
dot_accuracy@3	0.64	0.46	0.62
dot_accuracy@5	0.68	0.58	0.7
dot_accuracy@10	0.8	0.66	0.82
dot_precision@1	0.42	0.28	0.46
dot_precision@3	0.2133	0.2867	0.2067
dot_precision@5	0.136	0.28	0.14
dot_precision@10	0.08	0.246	0.082
dot_recall@1	0.42	0.0101	0.44
dot_recall@3	0.64	0.0497	0.58
dot_recall@5	0.68	0.0768	0.65
dot_recall@10	0.8	0.1079	0.76
dot_ndcg@10	0.6079	0.2711	0.5977
dot_mrr@10	0.5469	0.3952	0.5692
dot_map@100	0.5547	0.1088	0.5513
row_non_zero_mean_query	128.0	128.0	128.0
row_sparsity_mean_query	0.9688	0.9688	0.9688
row_non_zero_mean_corpus	128.0	128.0	128.0
row_sparsity_mean_corpus	0.9688	0.9688	0.9688

Sparse Nano BEIR

Dataset: NanoBEIR_mean_128

Evaluated with SparseNanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "msmarco",
        "nfcorpus",
        "nq"
    ],
    "max_active_dims": 128
}

Metric	Value
dot_accuracy@1	0.3867
dot_accuracy@3	0.5733
dot_accuracy@5	0.6533
dot_accuracy@10	0.76
dot_precision@1	0.3867
dot_precision@3	0.2356
dot_precision@5	0.1853
dot_precision@10	0.136
dot_recall@1	0.29
dot_recall@3	0.4232
dot_recall@5	0.4689
dot_recall@10	0.556
dot_ndcg@10	0.4922
dot_mrr@10	0.5038
dot_map@100	0.405
row_non_zero_mean_query	128.0
row_sparsity_mean_query	0.9688
row_non_zero_mean_corpus	128.0
row_sparsity_mean_corpus	0.9688

Sparse Information Retrieval

Datasets: NanoMSMARCO_256, NanoNFCorpus_256 and NanoNQ_256
Evaluated with SparseInformationRetrievalEvaluator with these parameters:
```
{
    "max_active_dims": 256
}
```

Metric	NanoMSMARCO_256	NanoNFCorpus_256	NanoNQ_256
dot_accuracy@1	0.42	0.32	0.42
dot_accuracy@3	0.7	0.56	0.64
dot_accuracy@5	0.76	0.62	0.68
dot_accuracy@10	0.84	0.7	0.84
dot_precision@1	0.42	0.32	0.42
dot_precision@3	0.2333	0.32	0.22
dot_precision@5	0.152	0.316	0.14
dot_precision@10	0.084	0.262	0.088
dot_recall@1	0.42	0.0304	0.4
dot_recall@3	0.7	0.0717	0.6
dot_recall@5	0.76	0.0931	0.63
dot_recall@10	0.84	0.1333	0.79
dot_ndcg@10	0.6326	0.3071	0.5943
dot_mrr@10	0.5661	0.4525	0.5506
dot_map@100	0.5727	0.143	0.533
row_non_zero_mean_query	256.0	256.0	256.0
row_sparsity_mean_query	0.9375	0.9375	0.9375
row_non_zero_mean_corpus	256.0	256.0	256.0
row_sparsity_mean_corpus	0.9375	0.9375	0.9375

Sparse Nano BEIR

Dataset: NanoBEIR_mean_256

Evaluated with SparseNanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "msmarco",
        "nfcorpus",
        "nq"
    ],
    "max_active_dims": 256
}

Metric	Value
dot_accuracy@1	0.3867
dot_accuracy@3	0.6333
dot_accuracy@5	0.6867
dot_accuracy@10	0.7933
dot_precision@1	0.3867
dot_precision@3	0.2578
dot_precision@5	0.2027
dot_precision@10	0.1447
dot_recall@1	0.2835
dot_recall@3	0.4572
dot_recall@5	0.4944
dot_recall@10	0.5878
dot_ndcg@10	0.5113
dot_mrr@10	0.5231
dot_map@100	0.4163
row_non_zero_mean_query	256.0
row_sparsity_mean_query	0.9375
row_non_zero_mean_corpus	256.0
row_sparsity_mean_corpus	0.9375

Training Details

Training Dataset

gooaq

Dataset: gooaq at b089f72
Size: 3,011,496 training samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 11.87 tokens
max: 23 tokens

min: 14 tokens
mean: 60.09 tokens
max: 201 tokens

	question	answer
type	string	string
details	min: 8 tokens mean: 11.87 tokens max: 23 tokens	min: 14 tokens mean: 60.09 tokens max: 201 tokens

Samples:

question	answer
`what is the difference between clay and mud mask?`	`The main difference between the two is that mud is a skin-healing agent, while clay is a cosmetic, drying agent. Clay masks are most useful for someone who has oily skin and is prone to breakouts of acne and blemishes.`
`myki how much on card?`	`A full fare myki card costs $6 and a concession, seniors or child myki costs $3. For more information about how to use your myki, visit ptv.vic.gov.au or call 1800 800 007.`
`how to find out if someone blocked your phone number on iphone?`	`If you get a notification like "Message Not Delivered" or you get no notification at all, that's a sign of a potential block. Next, you could try calling the person. If the call goes right to voicemail or rings once (or a half ring) then goes to voicemail, that's further evidence you may have been blocked.`

Loss: CSRLoss with these parameters:

{
    "beta": 0.1,
    "gamma": 1.0,
    "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')"
}

Evaluation Dataset

gooaq

Dataset: gooaq at b089f72
Size: 1,000 evaluation samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 11.88 tokens
max: 22 tokens

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

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

Samples:

question	answer
`how do i program my directv remote with my tv?`	`['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']`
`are rodrigues fruit bats nocturnal?`	`Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.`
`why does your heart rate increase during exercise bbc bitesize?`	`During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.`

Loss: CSRLoss with these parameters:

{
    "beta": 0.1,
    "gamma": 1.0,
    "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
learning_rate: 0.0002
num_train_epochs: 1
warmup_ratio: 0.1
bf16: True
load_best_model_at_end: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 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: 0.0002
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	NanoMSMARCO_128_dot_ndcg@10	NanoNFCorpus_128_dot_ndcg@10	NanoNQ_128_dot_ndcg@10	NanoBEIR_mean_128_dot_ndcg@10	NanoMSMARCO_256_dot_ndcg@10	NanoNFCorpus_256_dot_ndcg@10	NanoNQ_256_dot_ndcg@10	NanoBEIR_mean_256_dot_ndcg@10
-1	-1	-	-	0.6175	0.2875	0.5432	0.4827	0.6158	0.3234	0.5929	0.5107
0.0064	300	0.3621	-	-	-	-	-	-	-	-	-
0.0128	600	0.3319	-	-	-	-	-	-	-	-	-
0.0191	900	0.3212	-	-	-	-	-	-	-	-	-
0.0255	1200	0.3154	-	-	-	-	-	-	-	-	-
0.0319	1500	0.3129	-	-	-	-	-	-	-	-	-
0.0383	1800	0.309	-	-	-	-	-	-	-	-	-
0.0446	2100	0.317	-	-	-	-	-	-	-	-	-
0.0510	2400	0.2997	-	-	-	-	-	-	-	-	-
0.0574	2700	0.3409	-	-	-	-	-	-	-	-	-
0.0638	3000	0.3251	0.3136	0.6049	0.2393	0.5583	0.4675	0.5950	0.2559	0.5555	0.4688
0.0701	3300	0.3291	-	-	-	-	-	-	-	-	-
0.0765	3600	0.3366	-	-	-	-	-	-	-	-	-
0.0829	3900	0.3286	-	-	-	-	-	-	-	-	-
0.0893	4200	0.3264	-	-	-	-	-	-	-	-	-
0.0956	4500	0.3413	-	-	-	-	-	-	-	-	-
0.1020	4800	0.3352	-	-	-	-	-	-	-	-	-
0.1084	5100	0.3323	-	-	-	-	-	-	-	-	-
0.1148	5400	0.3308	-	-	-	-	-	-	-	-	-
0.1211	5700	0.3127	-	-	-	-	-	-	-	-	-
0.1275	6000	0.3224	0.2949	0.5445	0.2155	0.5394	0.4331	0.5911	0.2340	0.5365	0.4539
0.1339	6300	0.3216	-	-	-	-	-	-	-	-	-
0.1403	6600	0.3202	-	-	-	-	-	-	-	-	-
0.1466	6900	0.3296	-	-	-	-	-	-	-	-	-
0.1530	7200	0.3171	-	-	-	-	-	-	-	-	-
0.1594	7500	0.3141	-	-	-	-	-	-	-	-	-
0.1658	7800	0.3202	-	-	-	-	-	-	-	-	-
0.1721	8100	0.3088	-	-	-	-	-	-	-	-	-
0.1785	8400	0.304	-	-	-	-	-	-	-	-	-
0.1849	8700	0.3105	-	-	-	-	-	-	-	-	-
0.1913	9000	0.307	0.2849	0.6038	0.2258	0.5471	0.4589	0.6241	0.2449	0.5498	0.4730
0.1976	9300	0.3043	-	-	-	-	-	-	-	-	-
0.2040	9600	0.3035	-	-	-	-	-	-	-	-	-
0.2104	9900	0.3069	-	-	-	-	-	-	-	-	-
0.2168	10200	0.3174	-	-	-	-	-	-	-	-	-
0.2231	10500	0.3111	-	-	-	-	-	-	-	-	-
0.2295	10800	0.295	-	-	-	-	-	-	-	-	-
0.2359	11100	0.2892	-	-	-	-	-	-	-	-	-
0.2423	11400	0.3012	-	-	-	-	-	-	-	-	-
0.2486	11700	0.3061	-	-	-	-	-	-	-	-	-
0.2550	12000	0.2863	0.2631	0.6190	0.2720	0.5379	0.4763	0.6056	0.2898	0.5419	0.4791
0.2614	12300	0.3008	-	-	-	-	-	-	-	-	-
0.2678	12600	0.2849	-	-	-	-	-	-	-	-	-
0.2741	12900	0.2876	-	-	-	-	-	-	-	-	-
0.2805	13200	0.2963	-	-	-	-	-	-	-	-	-
0.2869	13500	0.2926	-	-	-	-	-	-	-	-	-
0.2933	13800	0.2855	-	-	-	-	-	-	-	-	-
0.2996	14100	0.2868	-	-	-	-	-	-	-	-	-
0.3060	14400	0.294	-	-	-	-	-	-	-	-	-
0.3124	14700	0.3008	-	-	-	-	-	-	-	-	-
0.3188	15000	0.293	0.2745	0.5538	0.2847	0.5422	0.4602	0.5615	0.2976	0.5588	0.4726
0.3252	15300	0.2776	-	-	-	-	-	-	-	-	-
0.3315	15600	0.2906	-	-	-	-	-	-	-	-	-
0.3379	15900	0.2874	-	-	-	-	-	-	-	-	-
0.3443	16200	0.2834	-	-	-	-	-	-	-	-	-
0.3507	16500	0.2718	-	-	-	-	-	-	-	-	-
0.3570	16800	0.2834	-	-	-	-	-	-	-	-	-
0.3634	17100	0.2833	-	-	-	-	-	-	-	-	-
0.3698	17400	0.281	-	-	-	-	-	-	-	-	-
0.3762	17700	0.2922	-	-	-	-	-	-	-	-	-
0.3825	18000	0.279	0.2623	0.5851	0.2696	0.5097	0.4548	0.5849	0.2776	0.5570	0.4732
0.3889	18300	0.2894	-	-	-	-	-	-	-	-	-
0.3953	18600	0.283	-	-	-	-	-	-	-	-	-
0.4017	18900	0.2824	-	-	-	-	-	-	-	-	-
0.4080	19200	0.2758	-	-	-	-	-	-	-	-	-
0.4144	19500	0.2893	-	-	-	-	-	-	-	-	-
0.4208	19800	0.278	-	-	-	-	-	-	-	-	-
0.4272	20100	0.2814	-	-	-	-	-	-	-	-	-
0.4335	20400	0.278	-	-	-	-	-	-	-	-	-
0.4399	20700	0.2783	-	-	-	-	-	-	-	-	-
0.4463	21000	0.2803	0.2510	0.5880	0.2664	0.5664	0.4736	0.6115	0.2734	0.5465	0.4772
0.4527	21300	0.2668	-	-	-	-	-	-	-	-	-
0.4590	21600	0.2828	-	-	-	-	-	-	-	-	-
0.4654	21900	0.2815	-	-	-	-	-	-	-	-	-
0.4718	22200	0.2778	-	-	-	-	-	-	-	-	-
0.4782	22500	0.271	-	-	-	-	-	-	-	-	-
0.4845	22800	0.2696	-	-	-	-	-	-	-	-	-
0.4909	23100	0.2698	-	-	-	-	-	-	-	-	-
0.4973	23400	0.2768	-	-	-	-	-	-	-	-	-
0.5037	23700	0.2626	-	-	-	-	-	-	-	-	-
0.5100	24000	0.2611	0.2414	0.6078	0.2635	0.5668	0.4794	0.6231	0.2942	0.5944	0.5039
0.5164	24300	0.2736	-	-	-	-	-	-	-	-	-
0.5228	24600	0.2695	-	-	-	-	-	-	-	-	-
0.5292	24900	0.2673	-	-	-	-	-	-	-	-	-
0.5355	25200	0.2746	-	-	-	-	-	-	-	-	-
0.5419	25500	0.2681	-	-	-	-	-	-	-	-	-
0.5483	25800	0.2676	-	-	-	-	-	-	-	-	-
0.5547	26100	0.2686	-	-	-	-	-	-	-	-	-
0.5610	26400	0.2652	-	-	-	-	-	-	-	-	-
0.5674	26700	0.2596	-	-	-	-	-	-	-	-	-
0.5738	27000	0.2677	0.2494	0.6018	0.2460	0.5280	0.4586	0.6238	0.2775	0.5673	0.4895
0.5802	27300	0.2621	-	-	-	-	-	-	-	-	-
0.5865	27600	0.2558	-	-	-	-	-	-	-	-	-
0.5929	27900	0.251	-	-	-	-	-	-	-	-	-
0.5993	28200	0.2601	-	-	-	-	-	-	-	-	-
0.6057	28500	0.2612	-	-	-	-	-	-	-	-	-
0.6120	28800	0.2695	-	-	-	-	-	-	-	-	-
0.6184	29100	0.2662	-	-	-	-	-	-	-	-	-
0.6248	29400	0.2589	-	-	-	-	-	-	-	-	-
0.6312	29700	0.2602	-	-	-	-	-	-	-	-	-
0.6376	30000	0.2698	0.2507	0.5892	0.2996	0.5386	0.4758	0.6102	0.2941	0.5535	0.4860
0.6439	30300	0.2625	-	-	-	-	-	-	-	-	-
0.6503	30600	0.2598	-	-	-	-	-	-	-	-	-
0.6567	30900	0.2594	-	-	-	-	-	-	-	-	-
0.6631	31200	0.2618	-	-	-	-	-	-	-	-	-
0.6694	31500	0.2556	-	-	-	-	-	-	-	-	-
0.6758	31800	0.2591	-	-	-	-	-	-	-	-	-
0.6822	32100	0.2544	-	-	-	-	-	-	-	-	-
0.6886	32400	0.2589	-	-	-	-	-	-	-	-	-
0.6949	32700	0.2522	-	-	-	-	-	-	-	-	-
0.7013	33000	0.2521	0.2535	0.6053	0.2650	0.5329	0.4677	0.6115	0.2925	0.6057	0.5032
0.7077	33300	0.2576	-	-	-	-	-	-	-	-	-
0.7141	33600	0.2582	-	-	-	-	-	-	-	-	-
0.7204	33900	0.2567	-	-	-	-	-	-	-	-	-
0.7268	34200	0.2577	-	-	-	-	-	-	-	-	-
0.7332	34500	0.2568	-	-	-	-	-	-	-	-	-
0.7396	34800	0.254	-	-	-	-	-	-	-	-	-
0.7459	35100	0.2489	-	-	-	-	-	-	-	-	-
0.7523	35400	0.2545	-	-	-	-	-	-	-	-	-
0.7587	35700	0.2476	-	-	-	-	-	-	-	-	-
0.7651	36000	0.2637	0.2397	0.6138	0.2726	0.5627	0.4831	0.6056	0.2889	0.5745	0.4897
0.7714	36300	0.2508	-	-	-	-	-	-	-	-	-
0.7778	36600	0.2569	-	-	-	-	-	-	-	-	-
0.7842	36900	0.2419	-	-	-	-	-	-	-	-	-
0.7906	37200	0.2453	-	-	-	-	-	-	-	-	-
0.7969	37500	0.2456	-	-	-	-	-	-	-	-	-
0.8033	37800	0.2497	-	-	-	-	-	-	-	-	-
0.8097	38100	0.2556	-	-	-	-	-	-	-	-	-
0.8161	38400	0.252	-	-	-	-	-	-	-	-	-
0.8224	38700	0.2423	-	-	-	-	-	-	-	-	-
0.8288	39000	0.2545	0.2301	0.5927	0.2895	0.5553	0.4792	0.5979	0.2987	0.5587	0.4851
0.8352	39300	0.2482	-	-	-	-	-	-	-	-	-
0.8416	39600	0.2429	-	-	-	-	-	-	-	-	-
0.8479	39900	0.2463	-	-	-	-	-	-	-	-	-
0.8543	40200	0.2354	-	-	-	-	-	-	-	-	-
0.8607	40500	0.2466	-	-	-	-	-	-	-	-	-
0.8671	40800	0.2484	-	-	-	-	-	-	-	-	-
0.8734	41100	0.2448	-	-	-	-	-	-	-	-	-
0.8798	41400	0.2448	-	-	-	-	-	-	-	-	-
0.8862	41700	0.2515	-	-	-	-	-	-	-	-	-
0.8926	42000	0.2428	0.2392	0.6001	0.2826	0.5857	0.4895	0.6208	0.3019	0.6010	0.5079
0.8989	42300	0.2497	-	-	-	-	-	-	-	-	-
0.9053	42600	0.2415	-	-	-	-	-	-	-	-	-
0.9117	42900	0.2408	-	-	-	-	-	-	-	-	-
0.9181	43200	0.242	-	-	-	-	-	-	-	-	-
0.9245	43500	0.2412	-	-	-	-	-	-	-	-	-
0.9308	43800	0.2472	-	-	-	-	-	-	-	-	-
0.9372	44100	0.2408	-	-	-	-	-	-	-	-	-
0.9436	44400	0.2374	-	-	-	-	-	-	-	-	-
0.9500	44700	0.2312	-	-	-	-	-	-	-	-	-
0.9563	45000	0.2412	0.2379	0.6079	0.2711	0.5977	0.4922	0.6326	0.3071	0.5943	0.5113
0.9627	45300	0.2381	-	-	-	-	-	-	-	-	-
0.9691	45600	0.2456	-	-	-	-	-	-	-	-	-
0.9755	45900	0.2418	-	-	-	-	-	-	-	-	-
0.9818	46200	0.2355	-	-	-	-	-	-	-	-	-
0.9882	46500	0.2424	-	-	-	-	-	-	-	-	-
0.9946	46800	0.2389	-	-	-	-	-	-	-	-	-

The bold row denotes the saved checkpoint.

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 1.202 kWh
Carbon Emitted: 0.467 kg of CO2
Hours Used: 3.125 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: 4.49.0
PyTorch: 2.6.0+cu124
Accelerate: 1.5.1
Datasets: 2.21.0
Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

CSRLoss

@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

@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}
}