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distilbert-base-matryoshka-sts

Sentence Similarity
sentence-transformers
Safetensors
English
distilbert
feature-extraction
dataset_size:1K<n<10K
loss:MatryoshkaLoss
loss:CoSENTLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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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 Sources

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

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

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

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

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

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

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

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

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

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

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

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

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
  • 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
  • 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},
}
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