metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sparse-encoder
  - sparse
  - splade
  - generated_from_trainer
  - dataset_size:10000
  - loss:SpladeLoss
  - loss:SparseMultipleNegativesRankingLoss
  - loss:FlopsLoss
base_model: naver/splade-cocondenser-ensembledistil
widget:
  - text: Two kids at a ballgame wash their hands.
  - text: Two dogs near a lake, while a person rides by on a horse.
  - text: >-
      This mother and her daughter and granddaughter are having car trouble, and
      the poor little girl looks hot out in the heat.
  - text: A young man competes in the Olympics in the pole vaulting competition.
  - text: A man is playing with the brass pots
datasets:
  - sentence-transformers/all-nli
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
  - active_dims
  - sparsity_ratio
co2_eq_emissions:
  emissions: 2.9668555526185707
  energy_consumed: 0.007632725204960537
  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: 0.033
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: >-
      splade-cocondenser-ensembledistil trained on Natural Language Inference
      (NLI)
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: 0.8541311579868741
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8470008029984434
            name: Spearman Cosine
          - type: active_dims
            value: 99.30233383178711
            name: Active Dims
          - type: sparsity_ratio
            value: 0.9967465325394211
            name: Sparsity Ratio
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: 0.8223074543214202
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8065254878130631
            name: Spearman Cosine
          - type: active_dims
            value: 95.75453186035156
            name: Active Dims
          - type: sparsity_ratio
            value: 0.9968627700720676
            name: Sparsity Ratio

splade-cocondenser-ensembledistil trained on Natural Language Inference (NLI)

This is a SPLADE Sparse Encoder model finetuned from naver/splade-cocondenser-ensembledistil on the all-nli 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: naver/splade-cocondenser-ensembledistil
Maximum Sequence Length: 256 tokens
Output Dimensionality: 30522 dimensions
Similarity Function: Dot Product
Training Dataset:
- all-nli
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': 256, '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-ensembledistil-nli")
# Run inference
sentences = [
    'A man is sitting in on the side of the street with brass pots.',
    'A man is playing with the brass pots',
    'A group of adults are swimming at the beach.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 30522]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[16.8617, 12.9505,  0.2749],
#         [12.9505, 20.8479,  0.2440],
#         [ 0.2749,  0.2440, 18.7043]])

Evaluation

Metrics

Semantic Similarity

Datasets: sts-dev and sts-test
Evaluated with SparseEmbeddingSimilarityEvaluator

Metric	sts-dev	sts-test
pearson_cosine	0.8541	0.8223
spearman_cosine	0.847	0.8065
active_dims	99.3023	95.7545
sparsity_ratio	0.9967	0.9969

Training Details

Training Dataset

all-nli

Dataset: all-nli at d482672
Size: 10,000 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 6 tokens
mean: 17.38 tokens
max: 52 tokens

min: 4 tokens
mean: 10.7 tokens
max: 31 tokens

min: 0.0
mean: 0.5
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 6 tokens mean: 17.38 tokens max: 52 tokens	min: 4 tokens mean: 10.7 tokens max: 31 tokens	min: 0.0 mean: 0.5 max: 1.0

Samples:

sentence1	sentence2	score
`A person on a horse jumps over a broken down airplane.`	`A person is training his horse for a competition.`	`0.5`
`A person on a horse jumps over a broken down airplane.`	`A person is at a diner, ordering an omelette.`	`0.0`
`A person on a horse jumps over a broken down airplane.`	`A person is outdoors, on a horse.`	`1.0`

Loss: SpladeLoss with these parameters:

{
    "loss": "SparseMultipleNegativesRankingLoss(scale=1, similarity_fct='dot_score')",
    "lambda_corpus": 0.003
}

Evaluation Dataset

all-nli

Dataset: all-nli at d482672
Size: 1,000 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 6 tokens
mean: 18.44 tokens
max: 57 tokens

min: 5 tokens
mean: 10.57 tokens
max: 25 tokens

min: 0.0
mean: 0.5
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 6 tokens mean: 18.44 tokens max: 57 tokens	min: 5 tokens mean: 10.57 tokens max: 25 tokens	min: 0.0 mean: 0.5 max: 1.0

Samples:

sentence1	sentence2	score
`Two women are embracing while holding to go packages.`	`The sisters are hugging goodbye while holding to go packages after just eating lunch.`	`0.5`
`Two women are embracing while holding to go packages.`	`Two woman are holding packages.`	`1.0`
`Two women are embracing while holding to go packages.`	`The men are fighting outside a deli.`	`0.0`

Loss: SpladeLoss with these parameters:

{
    "loss": "SparseMultipleNegativesRankingLoss(scale=1, similarity_fct='dot_score')",
    "lambda_corpus": 0.003
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 4e-06
num_train_epochs: 1
bf16: True
load_best_model_at_end: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 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: 4e-06
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: 0.0
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
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	sts-dev_spearman_cosine	sts-test_spearman_cosine
-1	-1	-	-	0.8366	-
0.032	20	0.8107	-	-	-
0.064	40	0.7854	-	-	-
0.096	60	0.7015	-	-	-
0.128	80	0.7161	-	-	-
0.16	100	0.724	-	-	-
0.192	120	0.6883	0.7255	0.8454	-
0.224	140	0.6661	-	-	-
0.256	160	0.6786	-	-	-
0.288	180	0.679	-	-	-
0.32	200	0.8013	-	-	-
0.352	220	0.6781	-	-	-
0.384	240	0.667	0.6779	0.8465	-
0.416	260	0.6691	-	-	-
0.448	280	0.7376	-	-	-
0.48	300	0.5601	-	-	-
0.512	320	0.6425	-	-	-
0.544	340	0.7406	-	-	-
0.576	360	0.6033	0.6623	0.8469	-
0.608	380	0.8166	-	-	-
0.64	400	0.5303	-	-	-
0.672	420	0.614	-	-	-
0.704	440	0.6253	-	-	-
0.736	460	0.5467	-	-	-
0.768	480	0.6804	0.6531	0.8470	-
0.8	500	0.6765	-	-	-
0.832	520	0.6522	-	-	-
0.864	540	0.5845	-	-	-
0.896	560	0.6786	-	-	-
0.928	580	0.5232	-	-	-
0.96	600	0.6077	0.6516	0.847	-
0.992	620	0.619	-	-	-
-1	-1	-	-	-	0.8065

The bold row denotes the saved checkpoint.

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.008 kWh
Carbon Emitted: 0.003 kg of CO2
Hours Used: 0.033 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},
}

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}
    }