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inference-free-splade-bert-base-uncased-nq-3e-3-lambda-corpus-1e-3-idf-lr-2e-5-lr

Feature Extraction
sentence-transformers
Safetensors
English
sparse-encoder
sparse
asymmetric
inference-free
splade
Generated from Trainer
dataset_size:99000
loss:SpladeLoss
loss:SparseMultipleNegativesRankingLoss
loss:FlopsLoss
Eval Results
Model card Files
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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
  - loss:SparseMultipleNegativesRankingLoss
  - loss:FlopsLoss
widget:
  - text: >-
      Rollin' (Limp Bizkit song) The music video was filmed atop the South Tower
      of the former World Trade Center in New York City. The introduction
      features Ben Stiller and Stephen Dorff mistaking Fred Durst for the valet
      and giving him the keys to their Bentley Azure. Also making a cameo is
      break dancer Mr. Wiggles. The rest of the video has several cuts to Durst
      and his bandmates hanging out of the Bentley as they drive about
      Manhattan. The song Ben Stiller is playing at the beginning is "My
      Generation" from the same album. The video also features scenes of Fred
      Durst with five girls dancing in a room. The video was filmed around the
      same time as the film Zoolander, which explains Stiller and Dorff's
      appearance. Fred Durst has a small cameo in that film.
  - text: document
  - text: who played the dj in the movie the warriors
  - text: >-
      Lionel Messi Born and raised in central Argentina, Messi was diagnosed
      with a growth hormone deficiency as a child. At age 13, he relocated to
      Spain to join Barcelona, who agreed to pay for his medical treatment.
      After a fast progression through Barcelona's youth academy, Messi made his
      competitive debut aged 17 in October 2004. Despite being injury-prone
      during his early career, he established himself as an integral player for
      the club within the next three years, finishing 2007 as a finalist for
      both the Ballon d'Or and FIFA World Player of the Year award, a feat he
      repeated the following year. His first uninterrupted campaign came in the
      2008–09 season, during which he helped Barcelona achieve the first
      treble in Spanish football. At 22 years old, Messi won the Ballon d'Or and
      FIFA World Player of the Year award by record voting margins.
  - text: >-
      Send In the Clowns "Send In the Clowns" is a song written by Stephen
      Sondheim for the 1973 musical A Little Night Music, an adaptation of
      Ingmar Bergman's film Smiles of a Summer Night. It is a ballad from Act
      Two, in which the character Desirée reflects on the ironies and
      disappointments of her life. Among other things, she looks back on an
      affair years earlier with the lawyer Fredrik, who was deeply in love with
      her but whose marriage proposals she had rejected. Meeting him after so
      long, she realizes she is in love with him and finally ready to marry him,
      but now it is he who rejects her: he is in an unconsummated marriage with
      a much younger woman. Desirée proposes marriage to rescue him from this
      situation, but he declines, citing his dedication to his bride. Reacting
      to his rejection, Desirée sings this song. The song is later reprised as a
      coda after Fredrik's young wife runs away with his son, and Fredrik is
      finally free to accept Desirée's offer.[1]
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
  - query_active_dims
  - query_sparsity_ratio
  - corpus_active_dims
  - corpus_sparsity_ratio
co2_eq_emissions:
  emissions: 68.29458484042254
  energy_consumed: 0.17569908269168294
  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: 0.483
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: >-
      Inference-free SPLADE bert-base-uncased 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: 0.26
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.62
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.68
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.78
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.26
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.20666666666666667
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.136
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.078
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.26
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.62
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.68
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.78
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5307390793273258
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.450047619047619
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4590024318818437
            name: Dot Map@100
          - type: query_active_dims
            value: 7.21999979019165
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.999763449322122
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 106.36942291259766
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9965149917137607
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNFCorpus
          type: NanoNFCorpus
        metrics:
          - type: dot_accuracy@1
            value: 0.44
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.54
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.58
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.62
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.44
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.38
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.34
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.274
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.043133464872628924
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.07664632573379433
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.09608957617217664
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.121568983205876
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.33893766293410243
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5020238095238095
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.1460630924219036
            name: Dot Map@100
          - type: query_active_dims
            value: 5.659999847412109
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9998145599945151
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 178.6857452392578
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9941456737684536
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: dot_accuracy@1
            value: 0.44
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.6
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.74
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.84
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.44
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.14800000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08599999999999998
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.43
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.57
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.68
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.78
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.59207822376941
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5457777777777778
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.5296003788208009
            name: Dot Map@100
          - type: query_active_dims
            value: 10.319999694824219
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9996618832417657
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 96.61132049560547
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9968346988894697
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-nano-beir
          name: Sparse Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: dot_accuracy@1
            value: 0.37999999999999995
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.5866666666666668
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.6666666666666666
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.7466666666666666
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.37999999999999995
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2622222222222222
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.20800000000000005
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.146
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.24437782162420962
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.42221544191126475
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.4853631920573922
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.5605229944019586
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.48725165534361276
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4992830687830687
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.37822196770818267
            name: Dot Map@100
          - type: query_active_dims
            value: 7.733333110809326
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.999746630852801
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 118.98687776342045
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9961016028516014
            name: Corpus Sparsity Ratio

Inference-free SPLADE bert-base-uncased 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:
  • Language: en
  • License: apache-2.0

Model Sources

Full Model Architecture 

SparseEncoder(
  (0): Router(
    (query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast)
    (document_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
    (document_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-base-uncased-nq-3e-3-lambda-corpus-1e-3-idf-lr-2e-5-lr")
# Run inference
sentences = [
    'is send in the clowns from a musical',
    'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman\'s film Smiles of a Summer Night. It is a ballad from Act Two, in which the character Desirée reflects on the ironies and disappointments of her life. Among other things, she looks back on an affair years earlier with the lawyer Fredrik, who was deeply in love with her but whose marriage proposals she had rejected. Meeting him after so long, she realizes she is in love with him and finally ready to marry him, but now it is he who rejects her: he is in an unconsummated marriage with a much younger woman. Desirée proposes marriage to rescue him from this situation, but he declines, citing his dedication to his bride. Reacting to his rejection, Desirée sings this song. The song is later reprised as a coda after Fredrik\'s young wife runs away with his son, and Fredrik is finally free to accept Desirée\'s offer.[1]',
    'query',
]
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

Metric NanoMSMARCO NanoNFCorpus NanoNQ
dot_accuracy@1 0.26 0.44 0.44
dot_accuracy@3 0.62 0.54 0.6
dot_accuracy@5 0.68 0.58 0.74
dot_accuracy@10 0.78 0.62 0.84
dot_precision@1 0.26 0.44 0.44
dot_precision@3 0.2067 0.38 0.2
dot_precision@5 0.136 0.34 0.148
dot_precision@10 0.078 0.274 0.086
dot_recall@1 0.26 0.0431 0.43
dot_recall@3 0.62 0.0766 0.57
dot_recall@5 0.68 0.0961 0.68
dot_recall@10 0.78 0.1216 0.78
dot_ndcg@10 0.5307 0.3389 0.5921
dot_mrr@10 0.45 0.502 0.5458
dot_map@100 0.459 0.1461 0.5296
query_active_dims 7.22 5.66 10.32
query_sparsity_ratio 0.9998 0.9998 0.9997
corpus_active_dims 106.3694 178.6857 96.6113
corpus_sparsity_ratio 0.9965 0.9941 0.9968

Sparse Nano BEIR

  • Dataset: NanoBEIR_mean
  • Evaluated with SparseNanoBEIREvaluator with these parameters:
    {
    "dataset_names": [
        "msmarco",
        "nfcorpus",
        "nq"
    ]
}
Metric Value
dot_accuracy@1 0.38
dot_accuracy@3 0.5867
dot_accuracy@5 0.6667
dot_accuracy@10 0.7467
dot_precision@1 0.38
dot_precision@3 0.2622
dot_precision@5 0.208
dot_precision@10 0.146
dot_recall@1 0.2444
dot_recall@3 0.4222
dot_recall@5 0.4854
dot_recall@10 0.5605
dot_ndcg@10 0.4873
dot_mrr@10 0.4993
dot_map@100 0.3782
query_active_dims 7.7333
query_sparsity_ratio 0.9997
corpus_active_dims 118.9869
corpus_sparsity_ratio 0.9961

Training Details

Training Dataset

natural-questions

  • Dataset: natural-questions at f9e894e
  • Size: 99,000 training samples
  • Columns: query and answer
  • Approximate statistics based on the first 1000 samples:
    query answer
    type string string
    details
    • min: 10 tokens
    • mean: 11.71 tokens
    • max: 26 tokens
    • min: 4 tokens
    • mean: 131.81 tokens
    • max: 450 tokens
  • Samples:
    query answer
    who played the father in papa don't preach Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.
    where was the location of the battle of hastings 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.
    how many puppies can a dog give birth to 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(scale=1.0, similarity_fct='dot_score')",
    "lambda_corpus": 0.003,
    "lambda_query": 0
}

Evaluation Dataset

natural-questions

  • Dataset: natural-questions at f9e894e
  • Size: 1,000 evaluation samples
  • Columns: query and answer
  • Approximate statistics based on the first 1000 samples:
    query answer
    type string string
    details
    • min: 10 tokens
    • mean: 11.69 tokens
    • max: 23 tokens
    • min: 15 tokens
    • mean: 134.01 tokens
    • max: 512 tokens
  • Samples:
    query answer
    where is the tiber river located in italy 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.
    what kind of car does jay gatsby drive 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.
    who sings if i can dream about you 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(scale=1.0, similarity_fct='dot_score')",
    "lambda_corpus": 0.003,
    "lambda_query": 0
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • learning_rate: 2e-05
  • num_train_epochs: 1
  • warmup_ratio: 0.1
  • fp16: True
  • batch_sampler: no_duplicates
  • router_mapping: ['query', 'document']
  • learning_rate_mapping: {'IDF\.weight': 0.001}

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 1
  • eval_accumulation_steps: None
  • torch_empty_cache_steps: None
  • learning_rate: 2e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 1
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.1
  • warmup_steps: 0
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • save_safetensors: True
  • save_on_each_node: False
  • save_only_model: False
  • restore_callback_states_from_checkpoint: False
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: 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
  • 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: ['query', 'document']
  • learning_rate_mapping: {'IDF\.weight': 0.001}

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
0.0323 200 0.3722 - - - - -
0.0646 400 0.1417 0.1266 0.5266 0.3095 0.4259 0.4207
0.0970 600 0.1152 - - - - -
0.1293 800 0.1099 0.1030 0.5202 0.3405 0.5552 0.4719
0.1616 1000 0.0956 - - - - -
0.1939 1200 0.0832 0.0920 0.4988 0.3432 0.5157 0.4526
0.2262 1400 0.0872 - - - - -
0.2586 1600 0.0915 0.0986 0.4900 0.3412 0.5269 0.4527
0.2909 1800 0.0999 - - - - -
0.3232 2000 0.0993 0.1024 0.5253 0.3405 0.5190 0.4616
0.3555 2200 0.1011 - - - - -
0.3878 2400 0.1046 0.0979 0.5241 0.3397 0.5518 0.4719
0.4202 2600 0.0935 - - - - -
0.4525 2800 0.0953 0.0914 0.5284 0.3362 0.5523 0.4723
0.4848 3000 0.09 - - - - -
0.5171 3200 0.0857 0.0910 0.5166 0.3358 0.5555 0.4693
0.5495 3400 0.086 - - - - -
0.5818 3600 0.0857 0.0861 0.5058 0.3353 0.5657 0.4689
0.6141 3800 0.0857 - - - - -
0.6464 4000 0.0816 0.0879 0.5228 0.3283 0.5576 0.4696
0.6787 4200 0.0835 - - - - -
0.7111 4400 0.0816 0.0859 0.5458 0.3395 0.5666 0.4840
0.7434 4600 0.0778 - - - - -
0.7757 4800 0.0815 0.0761 0.5514 0.3379 0.5966 0.4953
0.8080 5000 0.0758 - - - - -
0.8403 5200 0.0714 0.0770 0.5335 0.3388 0.5828 0.4850
0.8727 5400 0.077 - - - - -
0.9050 5600 0.0741 0.0772 0.5277 0.3398 0.5927 0.4867
0.9373 5800 0.0743 - - - - -
0.9696 6000 0.0787 0.0773 0.5307 0.3393 0.5921 0.4874
-1 -1 - - 0.5307 0.3389 0.5921 0.4873

Environmental Impact

Carbon emissions were measured using CodeCarbon.

  • Energy Consumed: 0.176 kWh
  • Carbon Emitted: 0.068 kg of CO2
  • Hours Used: 0.483 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: 4.52.3
  • 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},
}

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}
}

FlopsLoss 

@article{paria2020minimizing,
    title={Minimizing flops to learn efficient sparse representations},
    author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
    journal={arXiv preprint arXiv:2004.05665},
    year={2020}
    }