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
base_model: distilbert/distilbert-base-uncased
widget:
  - text: >-
      Saudi Arabia–United Arab Emirates relations However, the UAE and Saudi
      Arabia continue to take somewhat differing stances on regional conflicts
      such the Yemeni Civil War, where the UAE opposes Al-Islah, and supports
      the Southern Movement, which has fought against Saudi-backed forces, and
      the Syrian Civil War, where the UAE has disagreed with Saudi support for
      Islamist movements.[4]
  - text: >-
      Economy of New Zealand New Zealand's diverse market economy has a sizable
      service sector, accounting for 63% of all GDP activity in 2013.[17] Large
      scale manufacturing industries include aluminium production, food
      processing, metal fabrication, wood and paper products. Mining,
      manufacturing, electricity, gas, water, and waste services accounted for
      16.5% of GDP in 2013.[17] The primary sector continues to dominate New
      Zealand's exports, despite accounting for 6.5% of GDP in 2013.[17]
  - text: >-
      who was the first president of indian science congress meeting held in
      kolkata in 1914
  - text: >-
      Get Over It (Eagles song) "Get Over It" is a song by the Eagles released
      as a single after a fourteen-year breakup. It was also the first song
      written by bandmates Don Henley and Glenn Frey when the band reunited.
      "Get Over It" was played live for the first time during their Hell Freezes
      Over tour in 1994. It returned the band to the U.S. Top 40 after a
      fourteen-year absence, peaking at No. 31 on the Billboard Hot 100 chart.
      It also hit No. 4 on the Billboard Mainstream Rock Tracks chart. The song
      was not played live by the Eagles after the "Hell Freezes Over" tour in
      1994. It remains the group's last Top 40 hit in the U.S.
  - text: >-
      Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion
      who is considered by Christians to be one of the first Gentiles to convert
      to the faith, as related in Acts of the Apostles.
datasets:
  - sentence-transformers/natural-questions
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
  - 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
model-index:
  - name: >-
      Inference-free SPLADE distilbert-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: 0.32
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.52
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.6
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.8
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.32
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.1733333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.12000000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.32
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.52
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.6
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.8
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5294275268594165
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.44701587301587303
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4547435439455525
            name: Dot Map@100
          - type: query_active_dims
            value: 6.380000114440918
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9997909704437966
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 56.05611801147461
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9981634192382061
            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: 0.48
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.54
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.58
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.44
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.3533333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.32800000000000007
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.24600000000000002
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.04296449405849682
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.07246863989183633
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.09285358111876901
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.11634922767333658
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.31292844524261265
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.47585714285714287
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.13754623990324893
            name: Dot Map@100
          - type: query_active_dims
            value: 4.760000228881836
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.999844046909479
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 69.88655853271484
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9977102890199622
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: dot_accuracy@1
            value: 0.38
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.62
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.68
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.74
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.38
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.20666666666666664
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.136
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07600000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.37
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.58
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.64
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.71
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5476944409397304
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5072222222222222
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4973273986984246
            name: Dot Map@100
          - type: query_active_dims
            value: 9.4399995803833
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9996907149079227
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 51.11539077758789
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.998325293533268
            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: 0.38000000000000006
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.54
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.6066666666666668
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.7066666666666667
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.38000000000000006
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.24444444444444444
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.19466666666666668
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.134
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.24432149801949896
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.39082287996394544
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.4442845270395897
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.5421164092244455
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.46335013768058647
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4766984126984127
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.36320572751574204
            name: Dot Map@100
          - type: query_active_dims
            value: 6.859999974568685
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9997752440870661
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 57.281252631734205
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9981232798430071
            name: Corpus Sparsity Ratio

Inference-free SPLADE distilbert-base-uncased trained on Natural-Questions tuples

This is a Asymmetric Inference-free SPLADE Sparse Encoder model finetuned from distilbert/distilbert-base-uncased 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
Base model: distilbert/distilbert-base-uncased
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): Router(
    (query_0_SparseStaticEmbedding): SparseStaticEmbedding({'frozen': False}, dim=30522, tokenizer=DistilBertTokenizerFast)
    (document_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'DistilBertForMaskedLM'})
    (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("seank333111/inference-free-splade-distilbert-base-uncased-nq")
# Run inference
queries = [
    "who is cornelius in the book of acts",
]
documents = [
    'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.',
    "Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their head of story.[1] There he worked on all of their films produced up to 2006; this included Toy Story (for which he received an Academy Award nomination) and A Bug's Life, as the co-story writer and others as story supervisor. His final film was Cars. He also voiced characters in many of the films, including Heimlich the caterpillar in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in Finding Nemo.[1]",
    'Wonderful Tonight "Wonderful Tonight" is a ballad written by Eric Clapton. It was included on Clapton\'s 1977 album Slowhand. Clapton wrote the song about Pattie Boyd.[1] The female vocal harmonies on the song are provided by Marcella Detroit (then Marcy Levy) and Yvonne Elliman.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 30522] [3, 30522]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[5.9751, 0.2390, 0.0000]])

Evaluation

Metrics

Sparse Information Retrieval

Datasets: NanoMSMARCO, NanoNFCorpus and NanoNQ
Evaluated with SparseInformationRetrievalEvaluator

Metric	NanoMSMARCO	NanoNFCorpus	NanoNQ
dot_accuracy@1	0.32	0.44	0.38
dot_accuracy@3	0.52	0.48	0.62
dot_accuracy@5	0.6	0.54	0.68
dot_accuracy@10	0.8	0.58	0.74
dot_precision@1	0.32	0.44	0.38
dot_precision@3	0.1733	0.3533	0.2067
dot_precision@5	0.12	0.328	0.136
dot_precision@10	0.08	0.246	0.076
dot_recall@1	0.32	0.043	0.37
dot_recall@3	0.52	0.0725	0.58
dot_recall@5	0.6	0.0929	0.64
dot_recall@10	0.8	0.1163	0.71
dot_ndcg@10	0.5294	0.3129	0.5477
dot_mrr@10	0.447	0.4759	0.5072
dot_map@100	0.4547	0.1375	0.4973
query_active_dims	6.38	4.76	9.44
query_sparsity_ratio	0.9998	0.9998	0.9997
corpus_active_dims	56.0561	69.8866	51.1154
corpus_sparsity_ratio	0.9982	0.9977	0.9983

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	0.54
dot_accuracy@5	0.6067
dot_accuracy@10	0.7067
dot_precision@1	0.38
dot_precision@3	0.2444
dot_precision@5	0.1947
dot_precision@10	0.134
dot_recall@1	0.2443
dot_recall@3	0.3908
dot_recall@5	0.4443
dot_recall@10	0.5421
dot_ndcg@10	0.4634
dot_mrr@10	0.4767
dot_map@100	0.3632
query_active_dims	6.86
query_sparsity_ratio	0.9998
corpus_active_dims	57.2813
corpus_sparsity_ratio	0.9981

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

	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')",
    "document_regularizer_weight": 0.003,
    "query_regularizer_weight": 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

	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')",
    "document_regularizer_weight": 0.003,
    "query_regularizer_weight": 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': 'query', 'answer': 'document'}
learning_rate_mapping: {'SparseStaticEmbedding\.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
hub_revision: None
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {'query': 'query', 'answer': 'document'}
learning_rate_mapping: {'SparseStaticEmbedding\.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	0.2418	-	-	-	-	-
0.0646	400	0.0857	-	-	-	-	-
0.0970	600	0.072	-	-	-	-	-
0.1293	800	0.062	-	-	-	-	-
0.1616	1000	0.0624	0.0867	0.5213	0.3326	0.5296	0.4612
0.1939	1200	0.0684	-	-	-	-	-
0.2262	1400	0.0776	-	-	-	-	-
0.2586	1600	0.0824	-	-	-	-	-
0.2909	1800	0.0826	-	-	-	-	-
0.3232	2000	0.082	0.1028	0.5108	0.3230	0.5169	0.4502
0.3555	2200	0.0869	-	-	-	-	-
0.3878	2400	0.0866	-	-	-	-	-
0.4202	2600	0.0848	-	-	-	-	-
0.4525	2800	0.0816	-	-	-	-	-
0.4848	3000	0.0769	0.0914	0.5667	0.3149	0.5786	0.4867
0.5171	3200	0.0745	-	-	-	-	-
0.5495	3400	0.0831	-	-	-	-	-
0.5818	3600	0.0764	-	-	-	-	-
0.6141	3800	0.0806	-	-	-	-	-
0.6464	4000	0.0742	0.0885	0.5512	0.3221	0.5262	0.4665
0.6787	4200	0.0739	-	-	-	-	-
0.7111	4400	0.0674	-	-	-	-	-
0.7434	4600	0.0675	-	-	-	-	-
0.7757	4800	0.0741	-	-	-	-	-
0.8080	5000	0.0686	0.0827	0.5514	0.3146	0.5632	0.4764
0.8403	5200	0.0745	-	-	-	-	-
0.8727	5400	0.0687	-	-	-	-	-
0.9050	5600	0.0637	-	-	-	-	-
0.9373	5800	0.0637	-	-	-	-	-
0.9696	6000	0.0648	0.0785	0.5292	0.3117	0.5480	0.4630
-1	-1	-	-	0.5294	0.3129	0.5477	0.4634

Framework Versions

Python: 3.11.13
Sentence Transformers: 5.0.0
Transformers: 4.53.0
PyTorch: 2.6.0+cu124
Accelerate: 1.8.1
Datasets: 3.6.0
Tokenizers: 0.21.2

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}
}