README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sparse-encoder
  - sparse
  - asymmetric
  - inference-free
  - splade
  - generated_from_trainer
  - dataset_size:99000
  - loss:SpladeLoss
widget:
  - source_sentence: where is the tiber river located in italy
    sentences:
      - >-
        Sales taxes in British Columbia On 1 July 2010, the PST and GST were
        combined into the Harmonized Sales Tax (HST) levied according to the
        provisions of the GST. The conversion to HST was controversial. Popular
        opposition led to a referendum on the tax system, the first such
        referendum in the Commonwealth of Nations, resulting in the province
        reverting to the former PST/GST model on 1 April 2013.
      - "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."
      - >-
        Water in California California's limited water supply comes from two
        main sources: surface water, or water that travels or gathers on the
        ground, like rivers, streams, and lakes; and groundwater, which is water
        that is pumped out from the ground. California has also begun producing
        a small amount of desalinated water, water that was once sea water, but
        has been purified.
  - source_sentence: what kind of car does jay gatsby drive
    sentences:
      - >-
        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.
      - "Panama Canal The Panama Canal (Spanish: Canal de Panamá) is an artificial 77\_km (48\_mi) waterway in Panama that connects the Atlantic Ocean with the Pacific Ocean. The canal cuts across the Isthmus of Panama and is a conduit for maritime trade. Canal locks are at each end to lift ships up to Gatun Lake, an artificial lake created to reduce the amount of excavation work required for the canal, 26 m (85 ft) above sea level, and then lower the ships at the other end. The original locks are 34 m (110 ft) wide. A third, wider lane of locks was constructed between September 2007 and May 2016. The expanded canal began commercial operation on June 26, 2016. The new locks allow transit of larger, post-Panamax ships, capable of handling more cargo.[1]"
      - >-
        Solar maximum Predictions of a future maximum's timing and strength are
        very difficult; predictions vary widely. There was a solar maximum in
        2000. In 2006 NASA initially expected a solar maximum in 2010 or 2011,
        and thought that it could be the strongest since 1958.[3] However, the
        solar maximum was not declared to have occurred until 2014, and even
        then was ranked among the weakest on record.[4]
  - source_sentence: who sings if i can dream about you
    sentences:
      - >-
        Wesley Jonathan Wesley Jonathan Waples (born October 18, 1978), known
        professionally as Wesley Jonathan, is an American actor. He is best
        known for his starring role as Jamal Grant on the NBC Saturday morning
        comedy-drama series City Guys, Sweetness in the 2005 film Roll Bounce,
        as well as Burrell "Stamps" Ballentine on TV Land's The Soul Man.
      - >-
        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]
      - >-
        Blood is thicker than water In modern society, the proverb "blood is
        thicker than water" is used to imply that family relationships are
        always more important than friends.
  - source_sentence: who did jesse palmer end up with on the bachelor
    sentences:
      - >-
        Jesse Palmer In 2004, Palmer was the first professional athlete to
        appear on The Bachelor television program and the first non-American
        bachelor, in which he was given his choice of eligible single women. He
        eventually selected Jessica Bowlin, but their courtship lasted for only
        a few months after the end of the show.[19][20]
      - >-
        Wave base In seawater, the water particles are moved in a circular
        orbital motion when a wave passes. The radius of the circle of motion
        for any given water molecule decreases exponentially with increasing
        depth. The wave base, which is the depth of influence of a water wave,
        is about half the wavelength.
      - >-
        Do You Remember the First Time? (The Vampire Diaries) Elena, after
        everyone continues to convince her that she had once loved damon decides
        to run through the magic free, mystic falls border. So she does, and she
        gets glimpses of her and Damon but never fully remembers yet that she
        loves him. Damon pulls her back across the line and she asks about a
        kiss in the rain. He continues to try to get her to remember.
  - source_sentence: when did the american civil rights movement end
    sentences:
      - >-
        A Sunday Afternoon on the Island of La Grande Jatte A Sunday Afternoon
        on the Island of La Grande Jatte (French: Un dimanche après-midi à l'Île
        de la Grande Jatte) painted in 1884, is one of Georges Seurat's most
        famous works. It is a leading example of pointillist technique, executed
        on a large canvas. Seurat's composition includes a number of Parisians
        at a park on the banks of the River Seine.
      - "Paleolithic Paleolithic humans made tools of stone, bone, and wood.[23] The early paleolithic hominins, Australopithecus, were the first users of stone tools. Excavations in Gona, Ethiopia have produced thousands of artifacts, and through radioisotopic dating and magnetostratigraphy, the sites can be firmly dated to 2.6Â\_million years ago. Evidence shows these early hominins intentionally selected raw materials with good flaking qualities and chose appropriate sized stones for their needs to produce sharp-edged tools for cutting.[29]"
      - >-
        African-American civil rights movement (1954–1968) The Civil Rights
        Movement (also known as the American civil rights movement,
        African-American civil rights movement, and other terms,[b]) was a human
        rights movement from 1954–1968 that encompassed strategies, groups,
        and social movements to accomplish its goal of ending legalized racial
        segregation and discrimination laws in the United States. The movement
        secured the legal recognition and federal protection of black Americans
        in the United States Constitution and federal law.
datasets:
  - sentence-transformers/natural-questions
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
co2_eq_emissions:
  emissions: 11.486068711625794
  energy_consumed: 0.029549806050974257
  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: 0.087
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: Inference-free SPLADE BERT-tiny trained on Natural-Questions tuples
    results:
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: dot_accuracy@1
            value: 0.3
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.46
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.52
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.66
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.3
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.15333333333333332
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.10400000000000001
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.066
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.3
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.46
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.52
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.66
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4613583823584531
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.3998809523809524
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4174283897822019
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNFCorpus
          type: NanoNFCorpus
        metrics:
          - type: dot_accuracy@1
            value: 0.46
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.56
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.6
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.66
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.46
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.36
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.31200000000000006
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.248
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.0437532639932766
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.07287376477005543
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.109733905482588
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.13321789504016796
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.32305198562670556
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5245
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.13942100043534664
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: dot_accuracy@1
            value: 0.24
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.5
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.64
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.74
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.24
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.16666666666666663
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.128
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07400000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.24
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.48
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.61
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.7
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4672527583679101
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.40066666666666656
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.3953139370042203
            name: Dot Map@100
      - task:
          type: sparse-nano-beir
          name: Sparse Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: dot_accuracy@1
            value: 0.3333333333333333
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.5066666666666667
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.5866666666666668
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.6866666666666666
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.3333333333333333
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.22666666666666666
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.18133333333333335
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.12933333333333333
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.1945844213310922
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.3376245882566851
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.4132446351608627
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.49773929834672265
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.41722104211768957
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4416825396825396
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.31738777574058963
            name: Dot Map@100

Inference-free SPLADE BERT-tiny trained on Natural-Questions tuples

This is a Asymmetric Inference-free SPLADE Sparse Encoder model trained on the natural-questions dataset using the sentence-transformers 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: Asymmetric Inference-free SPLADE Sparse Encoder
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 30522 dimensions
• Similarity Function: Dot Product
• Training Dataset:
  • natural-questions
• 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): Asym(
    (query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast)
    (corpus_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
    (corpus_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
  )
)

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/inference-free-splade-bert-tiny-nq-3e-3-lambda-corpus")
# Run inference
sentences = [
    'when did the american civil rights movement end',
    'African-American civil rights movement (1954–1968) The Civil Rights Movement (also known as the American civil rights movement, African-American civil rights movement, and other terms,[b]) was a human rights movement from 1954–1968 that encompassed strategies, groups, and social movements to accomplish its goal of ending legalized racial segregation and discrimination laws in the United States. The movement secured the legal recognition and federal protection of black Americans in the United States Constitution and federal law.',
    'Paleolithic Paleolithic humans made tools of stone, bone, and wood.[23] The early paleolithic hominins, Australopithecus, were the first users of stone tools. Excavations in Gona, Ethiopia have produced thousands of artifacts, and through radioisotopic dating and magnetostratigraphy, the sites can be firmly dated to 2.6Â\xa0million years ago. Evidence shows these early hominins intentionally selected raw materials with good flaking qualities and chose appropriate sized stones for their needs to produce sharp-edged tools for cutting.[29]',
]
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]

Evaluation

Metrics

Sparse Information Retrieval

• Datasets: NanoMSMARCO, NanoNFCorpus and NanoNQ
• Evaluated with SparseInformationRetrievalEvaluator

Metric	NanoMSMARCO	NanoNFCorpus	NanoNQ
dot_accuracy@1	0.3	0.46	0.24
dot_accuracy@3	0.46	0.56	0.5
dot_accuracy@5	0.52	0.6	0.64
dot_accuracy@10	0.66	0.66	0.74
dot_precision@1	0.3	0.46	0.24
dot_precision@3	0.1533	0.36	0.1667
dot_precision@5	0.104	0.312	0.128
dot_precision@10	0.066	0.248	0.074
dot_recall@1	0.3	0.0438	0.24
dot_recall@3	0.46	0.0729	0.48
dot_recall@5	0.52	0.1097	0.61
dot_recall@10	0.66	0.1332	0.7
dot_ndcg@10	0.4614	0.3231	0.4673
dot_mrr@10	0.3999	0.5245	0.4007
dot_map@100	0.4174	0.1394	0.3953

Sparse Nano BEIR

• Dataset: NanoBEIR_mean
• Evaluated with SparseNanoBEIREvaluator with these parameters:

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

Metric	Value
dot_accuracy@1	0.3333
dot_accuracy@3	0.5067
dot_accuracy@5	0.5867
dot_accuracy@10	0.6867
dot_precision@1	0.3333
dot_precision@3	0.2267
dot_precision@5	0.1813
dot_precision@10	0.1293
dot_recall@1	0.1946
dot_recall@3	0.3376
dot_recall@5	0.4132
dot_recall@10	0.4977
dot_ndcg@10	0.4172
dot_mrr@10	0.4417
dot_map@100	0.3174

Training Details

Training Dataset

natural-questions

• Dataset: natural-questions at f9e894e
• Size: 99,000 training samples
• Columns: query and corpus
• Approximate statistics based on the first 1000 samples:

  	query	corpus
  type	dict	dict
  details

• Samples:

  query	corpus
  {'query': "who played the father in papa don't preach"}	{'corpus': 'Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.'}
  {'query': 'where was the location of the battle of hastings'}	{'corpus': '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.'}
  {'query': 'how many puppies can a dog give birth to'}	{'corpus': '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]'}

• Loss: SpladeLoss with these parameters:

  {'loss': SparseMultipleNegativesRankingLoss(
    (model): SparseEncoder(
      (0): Asym(
        (query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast)
        (corpus_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
        (corpus_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
      )
    )
    (cross_entropy_loss): CrossEntropyLoss()
  ), 'lambda_corpus': 0.003, 'lambda_query': 0, 'corpus_regularizer': FlopsLoss(
    (model): SparseEncoder(
      (0): Asym(
        (query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast)
        (corpus_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
        (corpus_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
      )
    )
  ), 'query_regularizer': None}
  

Evaluation Dataset

natural-questions

• Dataset: natural-questions at f9e894e
• Size: 1,000 evaluation samples
• Columns: query and corpus
• Approximate statistics based on the first 1000 samples:

  	query	corpus
  type	dict	dict
  details

• Samples:

  query	corpus
  {'query': 'where is the tiber river located in italy'}	{'corpus': '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\xa0mi) 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\xa0sq\xa0mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.'}
  {'query': 'what kind of car does jay gatsby drive'}	{'corpus': '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.'}
  {'query': 'who sings if i can dream about you'}	{'corpus': '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]'}

• Loss: SpladeLoss with these parameters:

  {'loss': SparseMultipleNegativesRankingLoss(
    (model): SparseEncoder(
      (0): Asym(
        (query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast)
        (corpus_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
        (corpus_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
      )
    )
    (cross_entropy_loss): CrossEntropyLoss()
  ), 'lambda_corpus': 0.003, 'lambda_query': 0, 'corpus_regularizer': FlopsLoss(
    (model): SparseEncoder(
      (0): Asym(
        (query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast)
        (corpus_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
        (corpus_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
      )
    )
  ), 'query_regularizer': None}
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• fp16: 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: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: True
• 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
• 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

Epoch	Step	Training Loss	Validation Loss	NanoMSMARCO_dot_ndcg@10	NanoNFCorpus_dot_ndcg@10	NanoNQ_dot_ndcg@10	NanoBEIR_mean_dot_ndcg@10
0.0129	20	1.5874	-	-	-	-	-
0.0259	40	1.7845	-	-	-	-	-
0.0388	60	1.924	-	-	-	-	-
0.0517	80	1.6441	-	-	-	-	-
0.0646	100	1.1842	-	-	-	-	-
0.0776	120	0.9556	-	-	-	-	-
0.0905	140	0.8623	-	-	-	-	-
0.1034	160	0.7888	-	-	-	-	-
0.1164	180	0.7923	-	-	-	-	-
0.1293	200	0.7464	0.6235	0.4629	0.3201	0.4511	0.4113
0.1422	220	0.7121	-	-	-	-	-
0.1551	240	0.6795	-	-	-	-	-
0.1681	260	0.7187	-	-	-	-	-
0.1810	280	0.6786	-	-	-	-	-
0.1939	300	0.6608	-	-	-	-	-
0.2069	320	0.6625	-	-	-	-	-
0.2198	340	0.651	-	-	-	-	-
0.2327	360	0.6671	-	-	-	-	-
0.2456	380	0.6732	-	-	-	-	-
0.2586	400	0.6301	0.5740	0.4243	0.3278	0.4273	0.3931
0.2715	420	0.6375	-	-	-	-	-
0.2844	440	0.6651	-	-	-	-	-
0.2973	460	0.6378	-	-	-	-	-
0.3103	480	0.6592	-	-	-	-	-
0.3232	500	0.6404	-	-	-	-	-
0.3361	520	0.6216	-	-	-	-	-
0.3491	540	0.6072	-	-	-	-	-
0.3620	560	0.6508	-	-	-	-	-
0.3749	580	0.5645	-	-	-	-	-
0.3878	600	0.6275	0.4993	0.4352	0.3241	0.4227	0.3940
0.4008	620	0.566	-	-	-	-	-
0.4137	640	0.5063	-	-	-	-	-
0.4266	660	0.5297	-	-	-	-	-
0.4396	680	0.5448	-	-	-	-	-
0.4525	700	0.5436	-	-	-	-	-
0.4654	720	0.4771	-	-	-	-	-
0.4783	740	0.5035	-	-	-	-	-
0.4913	760	0.5005	-	-	-	-	-
0.5042	780	0.4509	-	-	-	-	-
0.5171	800	0.4956	0.4341	0.4596	0.3280	0.4357	0.4078
0.5301	820	0.4876	-	-	-	-	-
0.5430	840	0.4622	-	-	-	-	-
0.5559	860	0.4791	-	-	-	-	-
0.5688	880	0.4608	-	-	-	-	-
0.5818	900	0.451	-	-	-	-	-
0.5947	920	0.4537	-	-	-	-	-
0.6076	940	0.4233	-	-	-	-	-
0.6206	960	0.4534	-	-	-	-	-
0.6335	980	0.4701	-	-	-	-	-
0.6464	1000	0.4017	0.4052	0.4692	0.3271	0.4452	0.4138
0.6593	1020	0.4518	-	-	-	-	-
0.6723	1040	0.4173	-	-	-	-	-
0.6852	1060	0.4369	-	-	-	-	-
0.6981	1080	0.456	-	-	-	-	-
0.7111	1100	0.448	-	-	-	-	-
0.7240	1120	0.4369	-	-	-	-	-
0.7369	1140	0.4394	-	-	-	-	-
0.7498	1160	0.437	-	-	-	-	-
0.7628	1180	0.4402	-	-	-	-	-
0.7757	1200	0.4382	0.3901	0.4623	0.3238	0.4664	0.4175
0.7886	1220	0.4111	-	-	-	-	-
0.8016	1240	0.4386	-	-	-	-	-
0.8145	1260	0.4136	-	-	-	-	-
0.8274	1280	0.4439	-	-	-	-	-
0.8403	1300	0.4423	-	-	-	-	-
0.8533	1320	0.4339	-	-	-	-	-
0.8662	1340	0.4124	-	-	-	-	-
0.8791	1360	0.417	-	-	-	-	-
0.8920	1380	0.4067	-	-	-	-	-
0.9050	1400	0.414	0.3854	0.4591	0.3234	0.4660	0.4162
0.9179	1420	0.4153	-	-	-	-	-
0.9308	1440	0.3889	-	-	-	-	-
0.9438	1460	0.4368	-	-	-	-	-
0.9567	1480	0.4241	-	-	-	-	-
0.9696	1500	0.423	-	-	-	-	-
0.9825	1520	0.4287	-	-	-	-	-
0.9955	1540	0.4282	-	-	-	-	-
-1	-1	-	-	0.4614	0.3231	0.4673	0.4172

Environmental Impact

Carbon emissions were measured using CodeCarbon.

• Energy Consumed: 0.030 kWh
• Carbon Emitted: 0.011 kg of CO2
• Hours Used: 0.087 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",
}

SpladeLoss

@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},
}