metadata

language:
  - en
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - loss:Matryoshka2dLoss
  - loss:MatryoshkaLoss
  - loss:CoSENTLoss
base_model: distilbert/distilbert-base-uncased
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
widget:
  - source_sentence: A woman is reading.
    sentences:
      - A woman is taking a picture.
      - Breivik complains of 'ridicule'
      - The small dog protects its owner.
  - source_sentence: A man shoots a man.
    sentences:
      - A man is shooting off guns.
      - A tiger walks around aimlessly.
      - A cat sleeps on purple sheet.
  - source_sentence: A man is speaking.
    sentences:
      - A man is talking.
      - 19 hurt in New Orleans shooting
      - The dogs are chasing a black cat.
  - source_sentence: A man is spitting.
    sentences:
      - Breivik complains of 'ridicule'
      - The man is hiking in the woods.
      - Eurozone agrees Greece bail-out
  - source_sentence: A parrot is talking.
    sentences:
      - A parrot is talking into a microphone.
      - A monkey pratices martial arts.
      - The two men are wearing jeans.
pipeline_tag: sentence-similarity
co2_eq_emissions:
  emissions: 5.379215660466108
  energy_consumed: 0.013838919430479152
  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: 0.072
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: SentenceTransformer based on distilbert/distilbert-base-uncased
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: 0.861868947947514
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8712617743584893
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8611484157829896
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8619125760745536
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8615299857042606
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8623855766060573
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.7716399182083511
            name: Pearson Dot
          - type: spearman_dot
            value: 0.781574012832885
            name: Spearman Dot
          - type: pearson_max
            value: 0.861868947947514
            name: Pearson Max
          - type: spearman_max
            value: 0.8712617743584893
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: 0.8281542233533932
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8373087013752897
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.842468233222574
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8374178427964344
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8424571958251152
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8372826604544046
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.6750086731901399
            name: Pearson Dot
          - type: spearman_dot
            value: 0.656834541089774
            name: Spearman Dot
          - type: pearson_max
            value: 0.842468233222574
            name: Pearson Max
          - type: spearman_max
            value: 0.8374178427964344
            name: Spearman Max

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("tomaarsen/distilbert-base-uncased-sts-2d-matryoshka")
# Run inference
sentences = [
    'A parrot is talking.',
    'A parrot is talking into a microphone.',
    'A monkey pratices martial arts.',
]
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
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8619
spearman_cosine	0.8713
pearson_manhattan	0.8611
spearman_manhattan	0.8619
pearson_euclidean	0.8615
spearman_euclidean	0.8624
pearson_dot	0.7716
spearman_dot	0.7816
pearson_max	0.8619
spearman_max	0.8713

Semantic Similarity

Dataset: sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8282
spearman_cosine	0.8373
pearson_manhattan	0.8425
spearman_manhattan	0.8374
pearson_euclidean	0.8425
spearman_euclidean	0.8373
pearson_dot	0.675
spearman_dot	0.6568
pearson_max	0.8425
spearman_max	0.8374

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: Matryoshka2dLoss with these parameters:

{
    "loss": "CoSENTLoss",
    "n_layers_per_step": 1,
    "last_layer_weight": 1.0,
    "prior_layers_weight": 1.0,
    "kl_div_weight": 1.0,
    "kl_temperature": 0.3,
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        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: Matryoshka2dLoss with these parameters:

{
    "loss": "CoSENTLoss",
    "n_layers_per_step": 1,
    "last_layer_weight": 1.0,
    "prior_layers_weight": 1.0,
    "kl_div_weight": 1.0,
    "kl_temperature": 0.3,
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        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: 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_spearman_cosine	sts-test_spearman_cosine
0.2778	100	7.1781	6.6704	0.8345	-
0.5556	200	6.5316	6.7135	0.8439	-
0.8333	300	6.6267	6.8697	0.8551	-
1.1111	400	6.5709	6.7623	0.8568	-
1.3889	500	6.2898	6.4412	0.8644	-
1.6667	600	6.2021	6.7711	0.8595	-
1.9444	700	6.201	6.5252	0.8628	-
2.2222	800	6.0862	6.9795	0.8652	-
2.5	900	6.303	6.7339	0.8685	-
2.7778	1000	5.9031	6.7249	0.8694	-
3.0556	1100	6.0803	6.8350	0.8684	-
3.3333	1200	6.0564	6.9703	0.8695	-
3.6111	1300	5.8407	7.3822	0.8707	-
3.8889	1400	5.8229	7.0442	0.8713	-
4.0	1440	-	-	-	0.8373

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.014 kWh
Carbon Emitted: 0.005 kg of CO2
Hours Used: 0.072 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

@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",
}

Matryoshka2dLoss

@misc{li20242d,
    title={2D Matryoshka Sentence Embeddings}, 
    author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
    year={2024},
    eprint={2402.14776},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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},
}