metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sparse-encoder
  - sparse
  - splade
  - generated_from_trainer
  - dataset_size:90000
  - loss:SpladeLoss
  - loss:SparseMarginMSELoss
  - loss:FlopsLoss
base_model: Luyu/co-condenser-marco
widget:
  - text: up to what age can a child get autism
  - text: food temperature danger zone
  - text: >-
      Small and medium size poly tanks are relatively inexpensive. They are also
      easy to handle, so poly tanks are used in many smaller wineries. New and
      used poly. drums are available in 20, 30, 40 and 55 gallon sizes, and they
      make excellent wine storage containers. for home winemakers. Just like
      glass, wine storage containers made of polyethylene advantages and
      disadvantages. They are lightweight, and polyethylene drums can be handled
      and stored easily.
  - text: what county is louin ms
  - text: >-
      Map of the Old City of Shanghai. By the early 1400s, Shanghai had become
      important enough for Ming dynasty engineers to begin dredging the Huangpu
      River (also known as Shen). In 1553, a city wall was built around the Old
      Town (Nanshi) as a defense against the depredations of the Wokou (Japanese
      pirates).
datasets:
  - sentence-transformers/msmarco
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: 84.77861327949611
  energy_consumed: 0.21810696440845714
  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: 0.618
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: CoCondenser 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: 0.46
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.64
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.72
            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: 0.21333333333333335
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.14400000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08199999999999999
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.46
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.64
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.72
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.82
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.6288613269928542
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5688571428571428
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.5779425698484522
            name: Dot Map@100
          - type: query_active_dims
            value: 56.099998474121094
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9981619815715183
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 192.40869140625
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9936960654149056
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNFCorpus
          type: NanoNFCorpus
        metrics:
          - type: dot_accuracy@1
            value: 0.38
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.58
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.62
            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: 0.36
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.316
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.26999999999999996
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.039663209420347775
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.07520387221675563
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.09363263999248954
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.14669853217549625
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.3303519560816792
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.49576984126984125
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.14778057031019226
            name: Dot Map@100
          - type: query_active_dims
            value: 53.68000030517578
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9982412685831473
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 367.5431823730469
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9879580898246167
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: dot_accuracy@1
            value: 0.5
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.76
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.8
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.88
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.5
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.25999999999999995
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.16799999999999998
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.09599999999999997
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.48
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.71
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.75
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.85
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.677150216479017
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.6328888888888887
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.6167275355591967
            name: Dot Map@100
          - type: query_active_dims
            value: 55.939998626708984
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9981672236869567
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 228.83615112304688
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9925025833456834
            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: 0.4466666666666667
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.66
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.7133333333333333
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.8133333333333334
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.4466666666666667
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.27777777777777773
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.20933333333333334
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.14933333333333332
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.3265544031401159
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.47506795740558516
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.5212108799974965
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.605566177391832
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5454544998511834
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5658386243386242
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.44748355857261374
            name: Dot Map@100
          - type: query_active_dims
            value: 55.23999913533529
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9981901579472073
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 246.17159613336406
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9919346177795241
            name: Corpus Sparsity Ratio

CoCondenser trained on Natural-Questions tuples

This is a SPLADE Sparse Encoder model finetuned from Luyu/co-condenser-marco on the msmarco 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: SPLADE Sparse Encoder
Base model: Luyu/co-condenser-marco
Maximum Sequence Length: 512 tokens
Output Dimensionality: 30522 dimensions
Similarity Function: Dot Product
Training Dataset:
- msmarco
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): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
  (1): 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/splade-cocondenser-msmarco-margin-mse")
# Run inference
queries = [
    "when did shanghai disneyland open",
]
documents = [
    "Shanghai Disney officially opens: A peek inside. June 17, 2016, 6 p.m. After five years of construction, $5.5 billion in spending and a month of testing to work out the kinks, Shanghai Disney Resort opened to the public just before noon, Shanghai time, on Thursday, June 16 (which was 9 p.m. Wednesday in Anaheim, home of the original Disney park). Shanghai Disneyland features six themed areas, and the resort contains two hotels, a shopping district and 99 acres of gardens, lakes and parkland. We'll keep you updated throughout the week with new details and peeks inside the resort.",
    'Map of the Old City of Shanghai. By the early 1400s, Shanghai had become important enough for Ming dynasty engineers to begin dredging the Huangpu River (also known as Shen). In 1553, a city wall was built around the Old Town (Nanshi) as a defense against the depredations of the Wokou (Japanese pirates).',
    'The conflict is referred to in China as the War of Resistance against Japanese Aggression (1937-45) and the Anti-Fascist War. Japanâ\x80\x99s expansionist policy of the 1930s, driven by the military, was to set up what it called the Greater East Asia Co-Prosperity Sphere. Marco Polo Bridge, Beijing.A sphere.e are marking the anniversary of Germany and Japanâ\x80\x99s surrender in 1945, but it is legitimate to suggest that the incident that sparked the conflict that became WWII occurred not in Poland in 1939 but in China, near this eleven-arched bridge on the outskirts of Beijing, in July 1937. Letâ\x80\x99s look at the undisputed facts.',
]
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([[31.8057, 19.5344, 12.4372]])

Evaluation

Metrics

Sparse Information Retrieval

Datasets: NanoMSMARCO, NanoNFCorpus and NanoNQ
Evaluated with SparseInformationRetrievalEvaluator

Metric	NanoMSMARCO	NanoNFCorpus	NanoNQ
dot_accuracy@1	0.46	0.38	0.5
dot_accuracy@3	0.64	0.58	0.76
dot_accuracy@5	0.72	0.62	0.8
dot_accuracy@10	0.82	0.74	0.88
dot_precision@1	0.46	0.38	0.5
dot_precision@3	0.2133	0.36	0.26
dot_precision@5	0.144	0.316	0.168
dot_precision@10	0.082	0.27	0.096
dot_recall@1	0.46	0.0397	0.48
dot_recall@3	0.64	0.0752	0.71
dot_recall@5	0.72	0.0936	0.75
dot_recall@10	0.82	0.1467	0.85
dot_ndcg@10	0.6289	0.3304	0.6772
dot_mrr@10	0.5689	0.4958	0.6329
dot_map@100	0.5779	0.1478	0.6167
query_active_dims	56.1	53.68	55.94
query_sparsity_ratio	0.9982	0.9982	0.9982
corpus_active_dims	192.4087	367.5432	228.8362
corpus_sparsity_ratio	0.9937	0.988	0.9925

Sparse Nano BEIR

Dataset: NanoBEIR_mean

Evaluated with SparseNanoBEIREvaluator with these parameters:

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

Metric	Value
dot_accuracy@1	0.4467
dot_accuracy@3	0.66
dot_accuracy@5	0.7133
dot_accuracy@10	0.8133
dot_precision@1	0.4467
dot_precision@3	0.2778
dot_precision@5	0.2093
dot_precision@10	0.1493
dot_recall@1	0.3266
dot_recall@3	0.4751
dot_recall@5	0.5212
dot_recall@10	0.6056
dot_ndcg@10	0.5455
dot_mrr@10	0.5658
dot_map@100	0.4475
query_active_dims	55.24
query_sparsity_ratio	0.9982
corpus_active_dims	246.1716
corpus_sparsity_ratio	0.9919

Training Details

Training Dataset

msmarco

Dataset: msmarco at 9e329ed
Size: 90,000 training samples
Columns: score, query, positive, and negative

Approximate statistics based on the first 1000 samples:

	score	query	positive	negative
type	float	string	string	string
details	min: -2.22 mean: 13.59 max: 22.53	min: 4 tokens mean: 9.05 tokens max: 40 tokens	min: 19 tokens mean: 81.18 tokens max: 203 tokens	min: 15 tokens mean: 77.08 tokens max: 249 tokens

Samples:

score	query	positive	negative
`4.470368494590124`	`where does the bile duct carry its secretions`	`The function of the common bile duct is to carry bile from the liver and the gallbladder into the duodenum, the top of the small intestine directly after the stomach. The bile it carries interacts with ingested fats and fat-soluble vitamins to enable them to be absorbed by the intestine.`	`The gall bladder is a pouch-shaped organ that stores the bile produced by the liver. The gall bladder shares a vessel, called the common bile duct, with the liver. When bile is needed, it moves through the common bile duct into the first part of the small intestine, the duodenum. It is here that the bile breaks down fat.`
`9.550037781397503`	`definition of reverse auction`	`Reverse auction. A reverse auction is a type of auction in which the roles of buyer and seller are reversed. In an ordinary auction (also known as a 'forward auction'), buyers compete to obtain goods or services by offering increasingly higher prices. In a reverse auction, the sellers compete to obtain business from the buyer and prices will typically decrease as the sellers underbid each other.`	`No-reserve auction. A No-reserve auction (NR), also known as an absolute auction, is an auction in which the item for sale will be sold regardless of price. From the seller's perspective, advertising an auction as having no reserve price can be desirable because it potentially attracts a greater number of bidders due to the possibility of a bargain.`
`19.58259622255961`	`how do i prevent diverticulitis`	`Follow Following Unfollow Pending Disabled. A , Gastroenterology, answered. The suggestion to prevent diverticulitis is to eat a diet high in fiber, and that includes high-fiber whole grains, fruits, vegetables, nuts, and seeds. Iâm aware that some gastroenterologists say to avoid all seeds and nuts, so some of you are nuts enough to wash tomato seeds from slices and pick free poppy seeds from buns.`	`The test is fast and easy especially with the newer CT scanners. But does it provide the information needed? CT KUBs are used to screen for a variety of intra-abdominal conditions, including appendicitis, kidney stones, diverticulitis, and others.`

Loss: SpladeLoss with these parameters:

{
    "loss": "SparseMarginMSELoss",
    "lambda_corpus": 0.08,
    "lambda_query": 0.1
}

Evaluation Dataset

msmarco

Dataset: msmarco at 9e329ed
Size: 10,000 evaluation samples
Columns: score, query, positive, and negative

Approximate statistics based on the first 1000 samples:

	score	query	positive	negative
type	float	string	string	string
details	min: -1.34 mean: 13.49 max: 22.2	min: 4 tokens mean: 8.85 tokens max: 27 tokens	min: 14 tokens mean: 80.48 tokens max: 211 tokens	min: 20 tokens mean: 77.44 tokens max: 209 tokens

Samples:

score	query	positive	negative
`15.64028427998225`	`what is a protected seedbed`	`A seedbed is a plot of garden set aside to grow vegetables seeds, which can later be transplanted. seedbed is a plot of garden set aside to grow vegetables seeds, which can later be transplanted.`	`Several articles within the Confederate Statesâ Constitution specifically protected slavery within the Confederacy, but some articles of the U.S. Constitution also protected slaveryâthe Emancipation Proclamation drew a clearer distinction between the two.`
`6.375148057937622`	`who founded ecuador`	`The first Spanish settlement in Ecuador was established in 1534 at Quito on the site of an important Incan town of the same name. Another settlement was established four years later near the river Guayas in Guayaquil.`	`Zuleta is a colonial working farm of 4,000 acres (2,000 hectares) that belongs to the family of Mr. Galo Plaza lasso, a former president of Ecuador, for more than 100 years. It was chosen as one of the worldâs âTop Ten Findsâ by Outside magazine and named as one of the best Ecuador Hotel by National Geographic Traveler.`
`8.436618288358051`	`what is aol problem`	`AOL problems. Lots of people are reporting ongoing (RTR:GE) messages from AOL today. This indicates the AOL mail servers are having problems and canât accept mail. This has nothing to do with spam, filtering or malicious email. This is simply their servers arenât functioning as well as they should be and so AOL canât accept all the mail thrown at them. These types of blocks resolve themselves. Update Feb 8, 2016: AOL users are having problems logging in.`	`Executive Director. I have read these complaints of poor service and agree 110%. I'm a college professor and give extra credit to all AOL users and over the 100% highest grade. I thought I phoned AOL and get some chap in India who is a proven scam man and I'm the poor American SOB who gets whacked.`

Loss: SpladeLoss with these parameters:

{
    "loss": "SparseMarginMSELoss",
    "lambda_corpus": 0.08,
    "lambda_query": 0.1
}

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

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: {}
learning_rate_mapping: {}

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
0.0178	100	805201.68	-	-	-	-	-
0.0356	200	11999.3975	-	-	-	-	-
0.0533	300	124.0031	-	-	-	-	-
0.0711	400	62.6813	-	-	-	-	-
0.0889	500	46.0329	49.7658	0.4890	0.2543	0.5131	0.4188
0.1067	600	41.2877	-	-	-	-	-
0.1244	700	35.3636	-	-	-	-	-
0.1422	800	33.3727	-	-	-	-	-
0.16	900	29.389	-	-	-	-	-
0.1778	1000	31.2482	28.1527	0.5652	0.2875	0.5423	0.4650
0.1956	1100	31.43	-	-	-	-	-
0.2133	1200	27.9919	-	-	-	-	-
0.2311	1300	26.9214	-	-	-	-	-
0.2489	1400	27.5533	-	-	-	-	-
0.2667	1500	25.7473	26.8466	0.5837	0.3265	0.6268	0.5123
0.2844	1600	26.7899	-	-	-	-	-
0.3022	1700	24.0652	-	-	-	-	-
0.32	1800	23.5837	-	-	-	-	-
0.3378	1900	24.1051	-	-	-	-	-
0.3556	2000	24.6901	22.0851	0.6018	0.3325	0.6359	0.5234
0.3733	2100	21.5136	-	-	-	-	-
0.3911	2200	22.066	-	-	-	-	-
0.4089	2300	20.8234	-	-	-	-	-
0.4267	2400	20.1988	-	-	-	-	-
0.4444	2500	20.0342	20.3437	0.5901	0.3222	0.6010	0.5044
0.4622	2600	18.8835	-	-	-	-	-
0.48	2700	19.4797	-	-	-	-	-
0.4978	2800	19.6199	-	-	-	-	-
0.5156	2900	16.6963	-	-	-	-	-
0.5333	3000	19.9204	18.0851	0.5915	0.3111	0.6323	0.5116
0.5511	3100	18.7849	-	-	-	-	-
0.5689	3200	18.3169	-	-	-	-	-
0.5867	3300	17.1938	-	-	-	-	-
0.6044	3400	18.0807	-	-	-	-	-
0.6222	3500	16.7721	20.1195	0.6012	0.3119	0.6337	0.5156
0.64	3600	16.7909	-	-	-	-	-
0.6578	3700	16.4954	-	-	-	-	-
0.6756	3800	16.3734	-	-	-	-	-
0.6933	3900	17.2231	-	-	-	-	-
0.7111	4000	16.8486	17.5785	0.6228	0.3423	0.6553	0.5401
0.7289	4100	18.2939	-	-	-	-	-
0.7467	4200	16.1108	-	-	-	-	-
0.7644	4300	16.878	-	-	-	-	-
0.7822	4400	15.6163	-	-	-	-	-
0.8	4500	15.8337	16.1847	0.6286	0.3376	0.6639	0.5434
0.8178	4600	15.5014	-	-	-	-	-
0.8356	4700	15.7579	-	-	-	-	-
0.8533	4800	15.9361	-	-	-	-	-
0.8711	4900	16.3308	-	-	-	-	-
0.8889	5000	14.8395	17.4054	0.6221	0.3280	0.6853	0.5451
0.9067	5100	14.8655	-	-	-	-	-
0.9244	5200	14.6498	-	-	-	-	-
0.9422	5300	15.5189	-	-	-	-	-
0.96	5400	14.608	-	-	-	-	-
0.9778	5500	15.6019	16.4883	0.6298	0.3317	0.6831	0.5482
0.9956	5600	14.6263	-	-	-	-	-
-1	-1	-	-	0.6289	0.3304	0.6772	0.5455

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.218 kWh
Carbon Emitted: 0.085 kg of CO2
Hours Used: 0.618 hours

Training Hardware

On Cloud: No
GPU Model: 1 x NVIDIA GeForce RTX 3090
CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
RAM Size: 31.78 GB

Framework Versions

Python: 3.11.6
Sentence Transformers: 4.2.0.dev0
Transformers: 4.52.4
PyTorch: 2.6.0+cu124
Accelerate: 1.5.1
Datasets: 2.21.0
Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

@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},
}

SparseMarginMSELoss

@misc{hofstätter2021improving,
    title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation},
    author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury},
    year={2021},
    eprint={2010.02666},
    archivePrefix={arXiv},
    primaryClass={cs.IR}
}

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}
    }