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yosefw/
SPLADE-BERT-Tiny-BS256-domain-adapted

Name: SPLADE-BERT-Tiny-BS256-domain-adapted
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SPLADE-BERT-Tiny

This is a SPLADE Sparse Encoder model finetuned from yosefw/bert-tiny-adapted-msmarco 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: yosefw/bert-tiny-adapted-msmarco
Maximum Sequence Length: 512 tokens
Output Dimensionality: 30522 dimensions
Similarity Function: Dot Product

Language: en
License: mit

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, 'architecture': '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.

python

from sentence_transformers import SparseEncoder

# Download from the 🤗 Hub
model = SparseEncoder("yosefw/SPLADE-BERT-Tiny-BS256-domain-adapted")
# Run inference
queries = [
    "what is funded depreciation",
]
documents = [
    "Depreciation Defined. Depreciating an asset means allocating the asset's cost over its useful life. The useful life of an asset, or economic resource, is the period of time over which a company intends to use this asset in operating activities or manufacturing processes. Funded Depreciation Defined. Funded depreciation is a fixed asset management method that helps a company set aside funds to renew machinery and equipment that it uses in operating activities. For instance, a company buys a new truck valued at $100,000 and records $10,000 in annual depreciation expense over 10 years.",
    'Funding Depreciation – Make Your Business More Profitable. Charles Hall is a CPA who heads up an excellent blog by the name of CPA-Scribo. His mission is to assist small- to medium-sized CPA firms with accounting, auditing, fraud and technology issues. We here at Depreciation Guru think he does an excellent job! In a recent post he tackled the topic of funding depreciation.',
    'Ratings: 6.1 /10 from 3,128 users. Reviews: 30 user | 2 critic. Liv, a popular television star whose show has just finished its run, and Maddie, an outstanding student and school basketball star whose popularity is on the rise until Liv makes a return to their high school.John D. Beck, Ron Hart.iv and Maddie is surprisingly good. I was expecting an OK show that was somewhat entertaining, instead it is a show that has charm, great comedic timing and is pretty darn adorable. The lead actress who plays both the twins (Dove Cameron) is extremely talented and also has a beautiful voice.',
]
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([[19.0238, 13.9594,  0.0000]])

Evaluation

Metrics

Sparse Information Retrieval

Evaluated with SparseInformationRetrievalEvaluator

Metric	Value
dot_accuracy@1	0.461
dot_accuracy@3	0.7702
dot_accuracy@5	0.8676
dot_accuracy@10	0.9446
dot_precision@1	0.461
dot_precision@3	0.2636
dot_precision@5	0.1804
dot_precision@10	0.0991
dot_recall@1	0.4451
dot_recall@3	0.7559
dot_recall@5	0.8575
dot_recall@10	0.9396
dot_ndcg@10	0.7036
dot_mrr@10	0.6316
dot_map@100	0.6268
query_active_dims	19.0044
query_sparsity_ratio	0.9994
corpus_active_dims	163.8887
corpus_sparsity_ratio	0.9946
avg_flops	1.9742

Training Details

Training Dataset

Unnamed Dataset

Size: 1,056,494 training samples
Columns: query, positive, and negative
Approximate statistics based on the first 1000 samples:
query positive negative
type string string string
details
min: 4 tokens
mean: 9.09 tokens
max: 24 tokens
min: 15 tokens
mean: 79.39 tokens
max: 229 tokens
min: 13 tokens
mean: 78.0 tokens
max: 207 tokens

Samples:

query	positive	negative
`what is the arg`	`For each point on the plane, arg is the function which returns the angle φ. In mathematics, arg is a function operating on complex numbers (visualized in a complex plane). It gives the angle between the positive real axis to the line joining the point to the origin, shown as φ in figure 1, known as an argument of the point.`	`Argument (complex analysis) In mathematics, arg is a function operating on complex numbers (visualized in a complex plane). It gives the angle between the positive real axis to the line joining the point to the origin, shown as φ in figure 1, known as an argument of the point.`
`symptoms of disc herniation`	`Other symptoms of a herniated disc include severe deep muscle pain and muscle spasms. This answer should not be considered medical advice...This answer should not be considered medical advice and should not take the place of a doctor’s visit.`	`1 If the disc herniation is large enough, the disc tissue can press on the adjacent spinal nerves that exit the spine at the level of the disc herniation. The physical examination, imaging tests, and electrical tests can aid in the diagnosis of a herniated disc.`
`which of the following is found in the dorsal body city`	From Wikipedia, the free encyclopedia. Human body cavities: Dorsal body cavity is to the left. The dorsal body cavity is located along the dorsal (posterior) surface of the human body, where it is subdivided into the cranial cavity housing the brain and the spinal cavity housing the spinal cord.The two cavities are continuous with one another.rom Wikipedia, the free encyclopedia. Human body cavities: Dorsal body cavity is to the left. The dorsal body cavity is located along the dorsal (posterior) surface of the human body, where it is subdivided into the cranial cavity housing the brain and the spinal cavity housing the spinal cord.	Dorsal Cavity: The dorsal cavity is an enclosed chamber that contains a portion of the body's organs and structures.The dorsal cavity is located in the posterior area of the trunk, head and neck and includes the cranial cavity and the spinal cavity.Conditions that can afflict the dorsal cavity include infection, infarction, trauma, cancer, genetic disorders, birth defects, and syndromes.orsal Cavity: The dorsal cavity is an enclosed chamber that contains a portion of the body's organs and structures.

Loss: SpladeLoss with these parameters:
json
1{ 2 "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False)", 3 "document_regularizer_weight": 0.003, 4 "query_regularizer_weight": 0.005 5}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
gradient_accumulation_steps: 8
learning_rate: 8e-05
num_train_epochs: 8
lr_scheduler_type: cosine
warmup_ratio: 0.025
fp16: True
load_best_model_at_end: True
optim: adamw_torch_fused
push_to_hub: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 8
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 8e-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: 8
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.025
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
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: 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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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: True
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: no
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: True
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	dot_ndcg@10
1.0	4127	15.2279	0.6793
2.0	8254	0.5248	0.6913
3.0	12381	0.4857	0.6980
4.0	16508	0.4529	0.7020
5.0	20635	0.4251	0.7031
6.0	24762	0.4054	0.705
7.0	28889	0.3931	0.7044
8.0	33016	0.3875	0.7036

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.13
Sentence Transformers: 5.1.2
Transformers: 4.57.1
PyTorch: 2.6.0+cu124
Accelerate: 1.8.1
Datasets: 4.4.1
Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

bibtex

@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

bibtex

@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

bibtex

@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

bibtex

@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}
}

	query	positive	negative
type	string	string	string
details	min: 4 tokens mean: 9.09 tokens max: 24 tokens	min: 15 tokens mean: 79.39 tokens max: 229 tokens	min: 13 tokens mean: 78.0 tokens max: 207 tokens