SentenceTransformer based on distilbert/distilbert-base-uncased

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("mrm8488/distilbert-base-matryoshka-sts")
# Run inference
sentences = [
    'A baby is laughing.',
    'The baby laughed in his car seat.',
    'A brown horse in a green field.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Evaluation

Metrics

Semantic Similarity

Dataset: sts-dev-768
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8597
spearman_cosine	0.8705
pearson_manhattan	0.8577
spearman_manhattan	0.8613
pearson_euclidean	0.8574
spearman_euclidean	0.8611
pearson_dot	0.7231
spearman_dot	0.7293
pearson_max	0.8597
spearman_max	0.8705

Semantic Similarity

Dataset: sts-dev-512
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8566
spearman_cosine	0.869
pearson_manhattan	0.8561
spearman_manhattan	0.8602
pearson_euclidean	0.856
spearman_euclidean	0.8598
pearson_dot	0.7251
spearman_dot	0.7325
pearson_max	0.8566
spearman_max	0.869

Semantic Similarity

Dataset: sts-dev-256
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8509
spearman_cosine	0.8656
pearson_manhattan	0.8516
spearman_manhattan	0.8576
pearson_euclidean	0.8513
spearman_euclidean	0.8567
pearson_dot	0.6913
spearman_dot	0.6984
pearson_max	0.8516
spearman_max	0.8656

Semantic Similarity

Dataset: sts-dev-128
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8416
spearman_cosine	0.8626
pearson_manhattan	0.841
spearman_manhattan	0.8496
pearson_euclidean	0.8432
spearman_euclidean	0.8506
pearson_dot	0.6776
spearman_dot	0.6865
pearson_max	0.8432
spearman_max	0.8626

Semantic Similarity

Dataset: sts-dev-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8232
spearman_cosine	0.8523
pearson_manhattan	0.8255
spearman_manhattan	0.8358
pearson_euclidean	0.8292
spearman_euclidean	0.8385
pearson_dot	0.6416
spearman_dot	0.6564
pearson_max	0.8292
spearman_max	0.8523

Semantic Similarity

Dataset: sts-dev-32
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7903
spearman_cosine	0.8328
pearson_manhattan	0.8032
spearman_manhattan	0.8168
pearson_euclidean	0.8079
spearman_euclidean	0.8196
pearson_dot	0.5952
spearman_dot	0.5992
pearson_max	0.8079
spearman_max	0.8328

Semantic Similarity

Dataset: sts-test-768
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8259
spearman_cosine	0.842
pearson_manhattan	0.8417
spearman_manhattan	0.8394
pearson_euclidean	0.8417
spearman_euclidean	0.8393
pearson_dot	0.6531
spearman_dot	0.6396
pearson_max	0.8417
spearman_max	0.842

Semantic Similarity

Dataset: sts-test-512
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8243
spearman_cosine	0.8418
pearson_manhattan	0.8406
spearman_manhattan	0.8388
pearson_euclidean	0.8406
spearman_euclidean	0.8386
pearson_dot	0.6578
spearman_dot	0.6453
pearson_max	0.8406
spearman_max	0.8418

Semantic Similarity

Dataset: sts-test-256
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8128
spearman_cosine	0.8344
pearson_manhattan	0.835
spearman_manhattan	0.8339
pearson_euclidean	0.835
spearman_euclidean	0.8342
pearson_dot	0.6011
spearman_dot	0.5827
pearson_max	0.835
spearman_max	0.8344

Semantic Similarity

Dataset: sts-test-128
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8037
spearman_cosine	0.8297
pearson_manhattan	0.8283
spearman_manhattan	0.8293
pearson_euclidean	0.8286
spearman_euclidean	0.8295
pearson_dot	0.5793
spearman_dot	0.566
pearson_max	0.8286
spearman_max	0.8297

Semantic Similarity

Dataset: sts-test-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7862
spearman_cosine	0.8221
pearson_manhattan	0.8179
spearman_manhattan	0.8219
pearson_euclidean	0.8199
spearman_euclidean	0.8241
pearson_dot	0.5115
spearman_dot	0.5024
pearson_max	0.8199
spearman_max	0.8241

Semantic Similarity

Dataset: sts-test-32
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7616
spearman_cosine	0.8126
pearson_manhattan	0.7996
spearman_manhattan	0.8084
pearson_euclidean	0.8024
spearman_euclidean	0.8116
pearson_dot	0.4647
spearman_dot	0.451
pearson_max	0.8024
spearman_max	0.8126

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

	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:

{
    "loss": "CoSENTLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64,
        32
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

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

	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:

{
    "loss": "CoSENTLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64,
        32
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

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: 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
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
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: 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_eval_metrics: False
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-32_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-32_spearman_cosine	sts-test-512_spearman_cosine	sts-test-64_spearman_cosine	sts-test-768_spearman_cosine
0.2778	100	28.2763	26.3514	0.8250	0.8306	0.7893	0.8308	0.8094	0.8314	-	-	-	-	-	-
0.5556	200	26.3731	26.0000	0.8373	0.8412	0.8026	0.8463	0.8267	0.8467	-	-	-	-	-	-
0.8333	300	26.0243	26.5062	0.8434	0.8495	0.8073	0.8534	0.8297	0.8556	-	-	-	-	-	-
1.1111	400	25.3448	28.1742	0.8496	0.8544	0.8157	0.8593	0.8361	0.8611	-	-	-	-	-	-
1.3889	500	24.7922	27.0245	0.8488	0.8529	0.8149	0.8574	0.8352	0.8589	-	-	-	-	-	-
1.6667	600	24.7596	26.9771	0.8516	0.8558	0.8199	0.8601	0.8389	0.8619	-	-	-	-	-	-
1.9444	700	24.7165	26.2923	0.8602	0.8634	0.8277	0.8665	0.8476	0.8681	-	-	-	-	-	-
2.2222	800	23.7934	27.9207	0.8570	0.8608	0.8263	0.8640	0.8460	0.8656	-	-	-	-	-	-
2.5	900	23.4618	27.5855	0.8583	0.8618	0.8257	0.8657	0.8456	0.8675	-	-	-	-	-	-
2.7778	1000	23.1831	29.9791	0.8533	0.8557	0.8232	0.8599	0.8411	0.8612	-	-	-	-	-	-
3.0556	1100	23.1935	28.7866	0.8612	0.8636	0.8329	0.8677	0.8504	0.8689	-	-	-	-	-	-
3.3333	1200	22.1447	30.0641	0.8597	0.8630	0.8285	0.8661	0.8488	0.8676	-	-	-	-	-	-
3.6111	1300	21.9271	30.9347	0.8613	0.8648	0.8309	0.8679	0.8509	0.8697	-	-	-	-	-	-
3.8889	1400	21.973	30.9209	0.8626	0.8656	0.8328	0.8690	0.8523	0.8705	-	-	-	-	-	-
4.0	1440	-	-	-	-	-	-	-	-	0.8297	0.8344	0.8126	0.8418	0.8221	0.8420

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.0
Transformers: 4.41.1
PyTorch: 2.3.0+cu121
Accelerate: 0.30.1
Datasets: 2.19.1
Tokenizers: 0.19.1

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",
}

MatryoshkaLoss

@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

@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},
}

Downloads last month: 2

Safetensors

Model size

66.4M params

Tensor type

F32

Model tree for mrm8488/distilbert-base-matryoshka-sts

Base model

distilbert/distilbert-base-uncased

Finetuned

(10048)

this model

Evaluation results

Pearson Cosine on sts dev 768
self-reported

0.860
Spearman Cosine on sts dev 768
self-reported

0.870
Pearson Manhattan on sts dev 768
self-reported

0.858
Spearman Manhattan on sts dev 768
self-reported

0.861
Pearson Euclidean on sts dev 768
self-reported

0.857
Spearman Euclidean on sts dev 768
self-reported

0.861
Pearson Dot on sts dev 768
self-reported

0.723
Spearman Dot on sts dev 768
self-reported

0.729
Pearson Max on sts dev 768
self-reported

0.860
Spearman Max on sts dev 768
self-reported

0.870

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