This is a sentence-transformers model finetuned from distilbert/distilbert-base-uncased on the sentence-transformers/stsb dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: distilbert/distilbert-base-uncased
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- sentence-transformers/stsb
Language: en

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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 SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/distilbert-base-uncased-sts-matryoshka")
# Run inference
sentences = [
    'A woman is dancing.',
    'A woman is dancing in railway station.',
    'The flag was moving in the air.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

Dataset: sts-dev-768
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8648
spearman_cosine	0.8748
pearson_manhattan	0.8628
spearman_manhattan	0.8658
pearson_euclidean	0.8627
spearman_euclidean	0.8658
pearson_dot	0.7443
spearman_dot	0.7514
pearson_max	0.8648
spearman_max	0.8748

Semantic Similarity

Dataset: sts-dev-512
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8628
spearman_cosine	0.8741
pearson_manhattan	0.862
spearman_manhattan	0.8651
pearson_euclidean	0.8623
spearman_euclidean	0.8653
pearson_dot	0.7464
spearman_dot	0.7541
pearson_max	0.8628
spearman_max	0.8741

Semantic Similarity

Dataset: sts-dev-256
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8589
spearman_cosine	0.8714
pearson_manhattan	0.8591
spearman_manhattan	0.8634
pearson_euclidean	0.8592
spearman_euclidean	0.8629
pearson_dot	0.7186
spearman_dot	0.7289
pearson_max	0.8592
spearman_max	0.8714

Semantic Similarity

Dataset: sts-dev-128
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8529
spearman_cosine	0.8688
pearson_manhattan	0.8509
spearman_manhattan	0.8576
pearson_euclidean	0.8532
spearman_euclidean	0.8581
pearson_dot	0.697
spearman_dot	0.7059
pearson_max	0.8532
spearman_max	0.8688

Semantic Similarity

Dataset: sts-dev-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.834
spearman_cosine	0.8587
pearson_manhattan	0.8352
spearman_manhattan	0.8446
pearson_euclidean	0.8387
spearman_euclidean	0.8461
pearson_dot	0.6579
spearman_dot	0.6713
pearson_max	0.8387
spearman_max	0.8587

Semantic Similarity

Dataset: sts-test-768
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8337
spearman_cosine	0.847
pearson_manhattan	0.8485
spearman_manhattan	0.847
pearson_euclidean	0.8493
spearman_euclidean	0.8475
pearson_dot	0.6702
spearman_dot	0.6526
pearson_max	0.8493
spearman_max	0.8475

Semantic Similarity

Dataset: sts-test-512
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8326
spearman_cosine	0.8468
pearson_manhattan	0.8474
spearman_manhattan	0.8463
pearson_euclidean	0.8482
spearman_euclidean	0.8466
pearson_dot	0.6737
spearman_dot	0.6572
pearson_max	0.8482
spearman_max	0.8468

Semantic Similarity

Dataset: sts-test-256
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8226
spearman_cosine	0.8403
pearson_manhattan	0.8421
spearman_manhattan	0.842
pearson_euclidean	0.8435
spearman_euclidean	0.8429
pearson_dot	0.623
spearman_dot	0.6062
pearson_max	0.8435
spearman_max	0.8429

Semantic Similarity

Dataset: sts-test-128
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.815
spearman_cosine	0.835
pearson_manhattan	0.8352
spearman_manhattan	0.8361
pearson_euclidean	0.8376
spearman_euclidean	0.8376
pearson_dot	0.5958
spearman_dot	0.5793
pearson_max	0.8376
spearman_max	0.8376

Semantic Similarity

Dataset: sts-test-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7981
spearman_cosine	0.827
pearson_manhattan	0.8239
spearman_manhattan	0.8289
pearson_euclidean	0.8279
spearman_euclidean	0.8315
pearson_dot	0.5206
spearman_dot	0.5067
pearson_max	0.8279
spearman_max	0.8315

Training Details

Training Dataset

sentence-transformers/stsb

Dataset: sentence-transformers/stsb at ab7a5ac
Size: 5,749 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: 10.0 tokens
max: 28 tokens
min: 5 tokens
mean: 9.95 tokens
max: 25 tokens
min: 0.0
mean: 0.54
max: 1.0

Samples:

sentence1	sentence2	score
`A plane is taking off.`	`An air plane is taking off.`	`1.0`
`A man is playing a large flute.`	`A man is playing a flute.`	`0.76`
`A man is spreading shreded cheese on a pizza.`	`A man is spreading shredded cheese on an uncooked pizza.`	`0.76`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "CoSENTLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Evaluation Dataset

sentence-transformers/stsb

Dataset: sentence-transformers/stsb at ab7a5ac
Size: 1,500 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 5 tokens
mean: 15.1 tokens
max: 45 tokens
min: 6 tokens
mean: 15.11 tokens
max: 53 tokens
min: 0.0
mean: 0.47
max: 1.0

Samples:

sentence1	sentence2	score
`A man with a hard hat is dancing.`	`A man wearing a hard hat is dancing.`	`1.0`
`A young child is riding a horse.`	`A child is riding a horse.`	`0.95`
`A man is feeding a mouse to a snake.`	`The man is feeding a mouse to the snake.`	`1.0`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "CoSENTLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 4
warmup_ratio: 0.1
fp16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: False
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
learning_rate: 5e-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: 4
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
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: None
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	sts-dev-128_spearman_cosine	sts-dev-256_spearman_cosine	sts-dev-512_spearman_cosine	sts-dev-64_spearman_cosine	sts-dev-768_spearman_cosine	sts-test-128_spearman_cosine	sts-test-256_spearman_cosine	sts-test-512_spearman_cosine	sts-test-64_spearman_cosine	sts-test-768_spearman_cosine
0.2778	100	23.266	21.5517	0.8305	0.8355	0.8361	0.8157	0.8366	-	-	-	-	-
0.5556	200	21.8736	21.6172	0.8327	0.8388	0.8446	0.8206	0.8453	-	-	-	-	-
0.8333	300	21.6241	22.0565	0.8475	0.8538	0.8556	0.8345	0.8565	-	-	-	-	-
1.1111	400	21.075	23.6719	0.8545	0.8581	0.8634	0.8435	0.8644	-	-	-	-	-
1.3889	500	20.4122	22.5926	0.8592	0.8624	0.8650	0.8436	0.8656	-	-	-	-	-
1.6667	600	20.6586	22.5999	0.8514	0.8563	0.8595	0.8389	0.8597	-	-	-	-	-
1.9444	700	20.3262	22.2965	0.8582	0.8631	0.8666	0.8465	0.8667	-	-	-	-	-
2.2222	800	19.7948	23.1844	0.8621	0.8659	0.8688	0.8499	0.8694	-	-	-	-	-
2.5	900	19.2826	23.1351	0.8653	0.8687	0.8703	0.8547	0.8710	-	-	-	-	-
2.7778	1000	19.1063	23.7141	0.8641	0.8672	0.8691	0.8531	0.8695	-	-	-	-	-
3.0556	1100	19.4575	23.0055	0.8673	0.8702	0.8726	0.8574	0.8728	-	-	-	-	-
3.3333	1200	18.0727	24.9288	0.8659	0.8692	0.8715	0.8565	0.8722	-	-	-	-	-
3.6111	1300	18.1698	25.3114	0.8675	0.8701	0.8728	0.8576	0.8734	-	-	-	-	-
3.8889	1400	18.2321	25.3777	0.8688	0.8714	0.8741	0.8587	0.8748	-	-	-	-	-
4.0	1440	-	-	-	-	-	-	-	0.8350	0.8403	0.8468	0.8270	0.8470

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.020 kWh
Carbon Emitted: 0.008 kg of CO2
Hours Used: 0.112 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: 3.0.0.dev0
Transformers: 4.41.0.dev0
PyTorch: 2.3.0+cu121
Accelerate: 0.26.1
Datasets: 2.18.0
Tokenizers: 0.19.1

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",
}

MatryoshkaLoss

bibtex

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

CoSENTLoss

bibtex

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}

	sentence1	sentence2	score
type	string	string	float
details	min: 6 tokens mean: 10.0 tokens max: 28 tokens	min: 5 tokens mean: 9.95 tokens max: 25 tokens	min: 0.0 mean: 0.54 max: 1.0