metadata

base_model: cointegrated/rubert-tiny2
library_name: sentence-transformers
metrics:
  - cosine_accuracy
  - cosine_accuracy_threshold
  - cosine_f1
  - cosine_f1_threshold
  - cosine_precision
  - cosine_recall
  - cosine_ap
  - dot_accuracy
  - dot_accuracy_threshold
  - dot_f1
  - dot_f1_threshold
  - dot_precision
  - dot_recall
  - dot_ap
  - manhattan_accuracy
  - manhattan_accuracy_threshold
  - manhattan_f1
  - manhattan_f1_threshold
  - manhattan_precision
  - manhattan_recall
  - manhattan_ap
  - euclidean_accuracy
  - euclidean_accuracy_threshold
  - euclidean_f1
  - euclidean_f1_threshold
  - euclidean_precision
  - euclidean_recall
  - euclidean_ap
  - max_accuracy
  - max_accuracy_threshold
  - max_f1
  - max_f1_threshold
  - max_precision
  - max_recall
  - max_ap
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:13690
  - loss:ContrastiveLoss
widget:
  - source_sentence: >-
      Грузоблочный тренажер Bronze Gym D-015 - жим ногами в Москве Силовые
      тренажеры Грузоблочные Bronze Gym D-015 - жим ногами
    sentences:
      - >-
        Трицепс-машина Matrix G3-S45 Главная Силовые тренажеры Трицепс-машина
        Matrix G3-S45
      - >-
        Верхняя тяга Iron Bull IR-TE08 nan Силовые тренажеры Грузоблочные
        тренажеры
      - >-
        Горизонтальный велоэргометр Matrix Lifestyle с консолью LED nan
        Велотренажеры Matrix
  - source_sentence: Эллиптический тренажер Precor EFX 731 nan Эллиптические тренажеры Precor
    sentences:
      - >-
        Беговая дорожка коммерческая AeroFit X3-T 10″LCD в Москве
        Кардиотренажеры Беговые дорожки AeroFit X3-T 10″LCD
      - >-
        Машина Смита Matrix G1-FW161 Главная Силовые тренажеры Машина Смита
        Matrix G1-FW161
      - >-
        Эллиптический тренажер CardioPower X75 Главная Эллиптические тренажеры
        Бренды
  - source_sentence: Велотренажер Clear Fit Envy CFB 45 Ego Главная Велотренажеры Бренды
    sentences:
      - >-
        Велотренажер Spirit Fitness MU100 реабилитационный в Москве
        Кардиотренажеры Велотренажеры Spirit Fitness MU100 реабилитационный
      - >-
        Многофункциональная блочная станция Teca SP785C Две Гребных тяги nan
        Силовые тренажеры Мультистанции
      - Беговая дорожка Sports Art T670 Главная Беговые дорожки Бренды
  - source_sentence: Горизонтальный велотренажер TRUE C400 Главная Велотренажеры Бренды
    sentences:
      - Велотренажер UltraGym UG-B002 nan Велотренажеры UltraGym
      - >-
        Грузоблочный тренажер Precor DSL505 - задние дельты/баттерфляй в Москве
        Силовые тренажеры Грузоблочные Precor DSL505 - задние дельты/баттерфляй
      - >-
        Беговая дорожка Hasttings LCT80 Беговые дорожки Hasttings Hasttings
        LCT80
  - source_sentence: >-
      Беговая дорожка Hasttings CT100 Главная Беговые дорожки Беговая дорожка
      Hasttings CT100
    sentences:
      - >-
        Вертикальная тяга RangeMax CST-018 nan Силовые тренажеры Грузоблочные
        тренажеры
      - Беговая дорожка ProForm 910 Беговые дорожки ProForm ProForm 910
      - Беговая дорожка AMMITY SPACE ATM 5000 Главная Беговые дорожки Бренды
model-index:
  - name: SentenceTransformer based on cointegrated/rubert-tiny2
    results:
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: cv
          type: cv
        metrics:
          - type: cosine_accuracy
            value: 1
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.7240798473358154
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 1
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.7240798473358154
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 1
            name: Cosine Precision
          - type: cosine_recall
            value: 1
            name: Cosine Recall
          - type: cosine_ap
            value: 1
            name: Cosine Ap
          - type: dot_accuracy
            value: 1
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 0.7240797877311707
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 1
            name: Dot F1
          - type: dot_f1_threshold
            value: 0.7240797877311707
            name: Dot F1 Threshold
          - type: dot_precision
            value: 1
            name: Dot Precision
          - type: dot_recall
            value: 1
            name: Dot Recall
          - type: dot_ap
            value: 1
            name: Dot Ap
          - type: manhattan_accuracy
            value: 1
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 9.055404663085938
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 1
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 9.055404663085938
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 1
            name: Manhattan Precision
          - type: manhattan_recall
            value: 1
            name: Manhattan Recall
          - type: manhattan_ap
            value: 1
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 1
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 0.6519391536712646
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 1
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 0.6519391536712646
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 1
            name: Euclidean Precision
          - type: euclidean_recall
            value: 1
            name: Euclidean Recall
          - type: euclidean_ap
            value: 1
            name: Euclidean Ap
          - type: max_accuracy
            value: 1
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 9.055404663085938
            name: Max Accuracy Threshold
          - type: max_f1
            value: 1
            name: Max F1
          - type: max_f1_threshold
            value: 9.055404663085938
            name: Max F1 Threshold
          - type: max_precision
            value: 1
            name: Max Precision
          - type: max_recall
            value: 1
            name: Max Recall
          - type: max_ap
            value: 1
            name: Max Ap

SentenceTransformer based on cointegrated/rubert-tiny2

This is a sentence-transformers model finetuned from cointegrated/rubert-tiny2. It maps sentences & paragraphs to a 312-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: cointegrated/rubert-tiny2
Maximum Sequence Length: 2048 tokens
Output Dimensionality: 312 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': 2048, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 312, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

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 = [
    'Беговая дорожка Hasttings CT100 Главная Беговые дорожки Беговая дорожка Hasttings CT100',
    'Беговая дорожка AMMITY SPACE ATM 5000 Главная Беговые дорожки Бренды',
    'Беговая дорожка ProForm 910 Беговые дорожки ProForm ProForm 910',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 312]

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

Evaluation

Metrics

Binary Classification

Dataset: cv
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	1.0
cosine_accuracy_threshold	0.7241
cosine_f1	1.0
cosine_f1_threshold	0.7241
cosine_precision	1.0
cosine_recall	1.0
cosine_ap	1.0
dot_accuracy	1.0
dot_accuracy_threshold	0.7241
dot_f1	1.0
dot_f1_threshold	0.7241
dot_precision	1.0
dot_recall	1.0
dot_ap	1.0
manhattan_accuracy	1.0
manhattan_accuracy_threshold	9.0554
manhattan_f1	1.0
manhattan_f1_threshold	9.0554
manhattan_precision	1.0
manhattan_recall	1.0
manhattan_ap	1.0
euclidean_accuracy	1.0
euclidean_accuracy_threshold	0.6519
euclidean_f1	1.0
euclidean_f1_threshold	0.6519
euclidean_precision	1.0
euclidean_recall	1.0
euclidean_ap	1.0
max_accuracy	1.0
max_accuracy_threshold	9.0554
max_f1	1.0
max_f1_threshold	9.0554
max_precision	1.0
max_recall	1.0
max_ap	1.0

Training Details

Training Dataset

Unnamed Dataset

Size: 13,690 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 14 tokens
mean: 29.13 tokens
max: 66 tokens

min: 13 tokens
mean: 29.18 tokens
max: 63 tokens

min: 0.0
mean: 0.51
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 14 tokens mean: 29.13 tokens max: 66 tokens	min: 13 tokens mean: 29.18 tokens max: 63 tokens	min: 0.0 mean: 0.51 max: 1.0

Samples:

sentence1	sentence2	score
`Велотренажер аэродинамический Spirit Fitness AB900+ Air Bike в Москве Кардиотренажеры Велотренажеры Spirit Fitness AB900+ Air Bike`	`Велотренажер IZHIMIO СL 1500 Главная Велотренажеры Бренды`	`1.0`
`Эллиптический тренажер Sports Art E835 Главная Эллиптические тренажеры Бренды`	`Степпер Matrix C7XI в Москве Кардиотренажеры Степперы Matrix C7XI`	`0.0`
`Мультистанция Nohrd SlimBeam nan Силовые тренажеры Мультистанции`	`Эллиптический тренажер Koenigsmann JX-170EF в Москве Кардиотренажеры Эллиптические тренажеры Koenigsmann JX-170EF`	`0.0`

Loss: ContrastiveLoss with these parameters:

{
    "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
    "margin": 0.5,
    "size_average": true
}

Evaluation Dataset

Unnamed Dataset

Size: 28 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 28 samples:
sentence1 sentence2 score
type string string float
details
min: 15 tokens
mean: 27.18 tokens
max: 45 tokens

min: 16 tokens
mean: 28.0 tokens
max: 47 tokens

min: 0.0
mean: 0.61
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 15 tokens mean: 27.18 tokens max: 45 tokens	min: 16 tokens mean: 28.0 tokens max: 47 tokens	min: 0.0 mean: 0.61 max: 1.0

Samples:

sentence1	sentence2	score
`Беговая дорожка Carbon Yukon Беговые дорожки Carbon Carbon Yukon`	`Беговая дорожка Hasttings LCT80 Беговые дорожки Hasttings Hasttings LCT80`	`1.0`
`Беговая дорожка Беговая дорожка DFC BOSS I T-B1 для реабилитации Беговые дорожки DFC Беговая дорожка DFC BOSS I T-B1 для реабилитации`	`Беговая дорожка EVO FITNESS Cosmo 5 Главная Беговые дорожки Бренды`	`1.0`
`Грузоблочный тренажер Precor C010ES - жим ногами/икроножные в Москве Силовые тренажеры Грузоблочные Precor C010ES - жим ногами/икроножные`	`Кроссовер Bronze Gym D-005 Главная Силовые тренажеры Кроссовер Bronze Gym D-005`	`1.0`

Loss: ContrastiveLoss with these parameters:

{
    "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
    "margin": 0.5,
    "size_average": true
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 10
warmup_ratio: 0.1
fp16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_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: 10
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
eval_on_start: False
eval_use_gather_object: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	cv_max_ap
0	0	-	-	0.7655
1.0	428	-	0.0056	1.0
1.1682	500	0.0078	-	-
2.0	856	-	0.0015	1.0
2.3364	1000	0.0019	-	-
3.0	1284	-	0.0011	1.0
3.5047	1500	0.0013	-	-
4.0	1712	-	0.0007	1.0
4.6729	2000	0.001	-	-
5.0	2140	-	0.0004	1.0
5.8411	2500	0.0008	-	-
6.0	2568	-	0.0002	1.0
7.0	2996	-	0.0002	1.0
7.0093	3000	0.0007	-	-
8.0	3424	-	0.0001	1.0
8.1776	3500	0.0006	-	-
9.0	3852	-	0.0001	1.0
9.3458	4000	0.0005	-	-
10.0	4280	-	0.0001	1.0

Framework Versions

Python: 3.11.8
Sentence Transformers: 3.1.0
Transformers: 4.44.2
PyTorch: 2.4.1+cu118
Accelerate: 0.34.2
Datasets: 3.0.0
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",
}

ContrastiveLoss

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}