metadata

language: []
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dataset_size:100K<n<1M
  - loss:CoSENTLoss
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
base_model: distilbert/distilbert-base-uncased
widget:
  - source_sentence: T L 2 DUMMY CHEST LAT WIDEBAND 90 Deg Front 2020 CX482 G-S
    sentences:
      - T L F DUMMY CHEST LAT WIDEBAND 90 Deg Front 2020.5 U625 G-S
      - T L F DUMMY HEAD CG LAT WIDEBAND Static Airbag OOP Test 2025 CX430 G-S
      - >-
        T R F DUMMY PELVIS LAT WIDEBAND 90 Deg Frontal Impact Simulation 2026
        P800 G-S
  - source_sentence: T L F DUMMY CHEST LONG WIDEBAND 90 Deg Front 2022 U553 G-S
    sentences:
      - T R F TORSO BELT AT D RING LOAD WIDEBAND 90 Deg Front 2022 U553 LBF
      - T L F DUMMY L UP TIBIA MY LOAD WIDEBAND 90 Deg Front 2015 P552 IN-LBS
      - >-
        T R F DUMMY R UP TIBIA FX LOAD WIDEBAND 30 Deg Front Angular Left 2022
        U554 LBF
  - source_sentence: T R F DUMMY PELVIS LAT WIDEBAND 90 Deg Front 2019 D544 G-S
    sentences:
      - T L F DUMMY PELVIS LAT WIDEBAND 90 Deg Front 2015 P552 G-S
      - T L LOWER CONTROL ARM VERT WIDEBAND Left Side Drop Test 2024.5 P702 G-S
      - F BARRIER PLATE 11030 SZ D FX LOAD WIDEBAND 90 Deg Front 2015 P552 LBF
  - source_sentence: T ENGINE ENGINE TOP LAT WIDEBAND 90 Deg Front 2015 P552 G-S
    sentences:
      - T R ENGINE TRANS BOTTOM LAT WIDEBAND 90 Deg Front 2015 P552 G-S
      - F BARRIER PLATE 09030 SZ D FX LOAD WIDEBAND 90 Deg Front 2015 P552 LBF
      - T R F DUMMY NECK UPPER MX LOAD WIDEBAND 90 Deg Front 2022 U554 IN-LBS
  - source_sentence: T L F DUMMY CHEST LAT WIDEBAND 90 Deg Front 2020 CX482 G-S
    sentences:
      - T R F DUMMY CHEST LAT WIDEBAND 90 Deg Front 2025 V363N G-S
      - T R F DUMMY HEAD CG VERT WIDEBAND VIA Linear Impact Test 2021 C727 G-S
      - >-
        T L F DUMMY T1 VERT WIDEBAND 75 Deg Oblique Left Side 10 in. Pole 2026
        P800 G-S
pipeline_tag: sentence-similarity
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: 0.27051173706186693
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.2798593637893599
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.228702027931258
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.25353345676390787
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.23018017587211453
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.2550481010151111
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.2125353301405465
            name: Pearson Dot
          - type: spearman_dot
            value: 0.1902748420981738
            name: Spearman Dot
          - type: pearson_max
            value: 0.27051173706186693
            name: Pearson Max
          - type: spearman_max
            value: 0.2798593637893599
            name: Spearman Max
          - type: pearson_cosine
            value: 0.26319176781258086
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.2721909587247752
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.21766215319708615
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.2439514548051345
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.2195389492634635
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.24629153092425862
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.21073878591545503
            name: Pearson Dot
          - type: spearman_dot
            value: 0.1864889259868287
            name: Spearman Dot
          - type: pearson_max
            value: 0.26319176781258086
            name: Pearson Max
          - type: spearman_max
            value: 0.2721909587247752
            name: Spearman Max

SentenceTransformer based on distilbert/distilbert-base-uncased

This is a sentence-transformers model finetuned from distilbert/distilbert-base-uncased. 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

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'T L F DUMMY CHEST LAT WIDEBAND 90 Deg Front 2020 CX482 G-S',
    'T R F DUMMY CHEST LAT WIDEBAND 90 Deg Front 2025 V363N G-S',
    'T R F DUMMY HEAD CG VERT WIDEBAND VIA Linear Impact Test 2021 C727 G-S',
]
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
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.2705
spearman_cosine	0.2799
pearson_manhattan	0.2287
spearman_manhattan	0.2535
pearson_euclidean	0.2302
spearman_euclidean	0.255
pearson_dot	0.2125
spearman_dot	0.1903
pearson_max	0.2705
spearman_max	0.2799

Semantic Similarity

Dataset: sts-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.2632
spearman_cosine	0.2722
pearson_manhattan	0.2177
spearman_manhattan	0.244
pearson_euclidean	0.2195
spearman_euclidean	0.2463
pearson_dot	0.2107
spearman_dot	0.1865
pearson_max	0.2632
spearman_max	0.2722

Training Details

Training Dataset

Unnamed Dataset

Size: 481,114 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 16 tokens
mean: 32.14 tokens
max: 57 tokens

min: 17 tokens
mean: 32.62 tokens
max: 58 tokens

min: 0.0
mean: 0.45
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 16 tokens mean: 32.14 tokens max: 57 tokens	min: 17 tokens mean: 32.62 tokens max: 58 tokens	min: 0.0 mean: 0.45 max: 1.0

Samples:

sentence1	sentence2	score
`T L C PLR SM SCS L2 HY REF 053 LAT WIDEBAND 75 Deg Oblique Left Side 10 in. Pole 2018 P558 G-S`	`T PCM PWR POWER TO PCM VOLT 2 SEC WIDEBAND 75 Deg Oblique Left Side 10 in. Pole 2020 V363N VOLTS`	`0.5198143220305642`
`T L F DUMMY L_FEMUR MX LOAD WIDEBAND 90 Deg Frontal Impact Simulation MY2025 U717 IN-LBS`	`B L FRAME AT No 1 X MEM LAT WIDEBAND Inline 25% Left Front Offset Vehicle to Vehicle 2021 P702 G-S`	`0.5214072221695696`
`T R F DOOR REAR OF SEAT H PT LAT WIDEBAND 75 Deg Oblique Right Side 10 in. Pole 2015 P552 G-S`	`T SCS R2 HY BOS A12 008 TAP RIGHT C PILLAR VOLT WIDEBAND 30 Deg Front Angular Right 2021 CX727 VOLTS`	`0.322173496575591`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Evaluation Dataset

Unnamed Dataset

Size: 103,097 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 17 tokens
mean: 31.98 tokens
max: 56 tokens

min: 15 tokens
mean: 31.96 tokens
max: 58 tokens

min: 0.0
mean: 0.45
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 17 tokens mean: 31.98 tokens max: 56 tokens	min: 15 tokens mean: 31.96 tokens max: 58 tokens	min: 0.0 mean: 0.45 max: 1.0

Samples:

sentence1	sentence2	score
`T R F DUMMY NECK UPPER MZ LOAD WIDEBAND 90 Deg Frontal Impact Simulation 2026 GENERIC IN-LBS`	`T R ROCKER AT C PILLAR LAT WIDEBAND 90 Deg Front 2021 P702 G-S`	`0.5234504780172093`
`T L ROCKER AT B_PILLAR VERT WIDEBAND 90 Deg Front 2024.5 P702 G-S`	`T RCM BTWN SEATS LOW G Z RCM C1 LZ ALV RC7 003 VOLT WIDEBAND 75 Deg Oblique Left Side 10 in. Pole 2018 P558 VOLTS`	`0.36805699821563936`
`T R FRAME AT C_PILLAR LONG WIDEBAND 90 Deg Left Side IIHS MDB to Vehicle 2024.5 P702 G-S`	`T L F LAP BELT AT ANCHOR LOAD WIDEBAND 90 DEG / LEFT SIDE DECEL-3G 2021 P702 LBF`	`0.5309750606095435`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 64
per_device_eval_batch_size: 64
num_train_epochs: 32
warmup_ratio: 0.1
fp16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
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: 32
max_steps: -1
lr_scheduler_type: linear
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
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: 7
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: True
dataloader_num_workers: 0
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_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: 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
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
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: False
include_tokens_per_second: False
neftune_noise_alpha: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	sts-dev_spearman_cosine
1.0650	1000	7.6111	7.5503	0.4087
2.1299	2000	7.5359	7.5420	0.4448
3.1949	3000	7.5232	7.5292	0.4622
4.2599	4000	7.5146	7.5218	0.4779
5.3248	5000	7.5045	7.5200	0.4880
6.3898	6000	7.4956	7.5191	0.4934
7.4547	7000	7.4873	7.5170	0.4967
8.5197	8000	7.4781	7.5218	0.4931
9.5847	9000	7.4686	7.5257	0.4961
10.6496	10000	7.4596	7.5327	0.4884
11.7146	11000	7.4498	7.5403	0.4860
12.7796	12000	7.4386	7.5507	0.4735
13.8445	13000	7.4253	7.5651	0.4660
14.9095	14000	7.4124	7.5927	0.4467
15.9744	15000	7.3989	7.6054	0.4314
17.0394	16000	7.3833	7.6654	0.4163
18.1044	17000	7.3669	7.7186	0.3967
19.1693	18000	7.3519	7.7653	0.3779
20.2343	19000	7.3349	7.8356	0.3651
21.2993	20000	7.3191	7.8772	0.3495
22.3642	21000	7.3032	7.9346	0.3412
23.4292	22000	7.2873	7.9624	0.3231
24.4941	23000	7.2718	8.0169	0.3161
25.5591	24000	7.2556	8.0633	0.3050
26.6241	25000	7.2425	8.1021	0.2958
27.6890	26000	7.2278	8.1563	0.2954
28.7540	27000	7.2124	8.1955	0.2882
29.8190	28000	7.2014	8.2234	0.2821
30.8839	29000	7.1938	8.2447	0.2792
31.9489	30000	7.1811	8.2609	0.2799
32.0	30048	-	-	0.2722

Framework Versions

Python: 3.10.6
Sentence Transformers: 3.0.0
Transformers: 4.35.0
PyTorch: 2.1.0a0+4136153
Accelerate: 0.30.1
Datasets: 2.14.1
Tokenizers: 0.14.1

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",
}

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},
}