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tiny_sent_transformer

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:13690
loss:ContrastiveLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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---
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: Тренажер на свободных весах DFC HOMEGYM HM019 в Москве
sentences:
- Независимая бицепс-машина Matrix G7-S40
- Беговая дорожка Stingrey ST-9317
- Мультикомплекс Hasttings Digger HD003-7
- source_sentence: Велотренажер Clear Fit Envy CFB 45 Ego
sentences:
- Эллиптический тренажер Svensson Body Labs Strideline EXA в Москве
- Реабилитационная рама ARMS (стек 60кг) AR082.1х60
- Вертикальная тяга двухпозиционная Vertex OPS 110
- source_sentence: Нижняя тяга Smith SH004
sentences:
- Аэробайк BH FITNESS AIRMAG
- Велотренажер Freemotion Tour De France Club
- Жим ногами под углом 45 градусов Bronze Gym BG-BGR-801
- source_sentence: Эллиптический тренажер BEST FITNESS BFE1
sentences:
- Спин-байк DFC OVICX Q200C
- Эллиптический тренажер NordicTrack E11.6 (NTEVEL99813)
- Беговая дорожка SPIRIT LW650
- source_sentence: Беговая дорожка Hasttings CT100
sentences:
- Мини велотренажер с регулируемой высотой Bradex SF 0830
- Беговая дорожка Koenigsmann ML в Москве
- Вертикальный велотренажер Sole B94 (2023)
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: 1.0
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.7653387784957886
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 1.0
name: Cosine F1
- type: cosine_f1_threshold
value: 0.7653387784957886
name: Cosine F1 Threshold
- type: cosine_precision
value: 1.0
name: Cosine Precision
- type: cosine_recall
value: 1.0
name: Cosine Recall
- type: cosine_ap
value: 1.0
name: Cosine Ap
- type: dot_accuracy
value: 1.0
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 0.7653387784957886
name: Dot Accuracy Threshold
- type: dot_f1
value: 1.0
name: Dot F1
- type: dot_f1_threshold
value: 0.7653387784957886
name: Dot F1 Threshold
- type: dot_precision
value: 1.0
name: Dot Precision
- type: dot_recall
value: 1.0
name: Dot Recall
- type: dot_ap
value: 1.0
name: Dot Ap
- type: manhattan_accuracy
value: 1.0
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 9.330949783325195
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 1.0
name: Manhattan F1
- type: manhattan_f1_threshold
value: 9.330949783325195
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 1.0
name: Manhattan Precision
- type: manhattan_recall
value: 1.0
name: Manhattan Recall
- type: manhattan_ap
value: 1.0
name: Manhattan Ap
- type: euclidean_accuracy
value: 1.0
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 0.6849288940429688
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 1.0
name: Euclidean F1
- type: euclidean_f1_threshold
value: 0.6849288940429688
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 1.0
name: Euclidean Precision
- type: euclidean_recall
value: 1.0
name: Euclidean Recall
- type: euclidean_ap
value: 1.0
name: Euclidean Ap
- type: max_accuracy
value: 1.0
name: Max Accuracy
- type: max_accuracy_threshold
value: 9.330949783325195
name: Max Accuracy Threshold
- type: max_f1
value: 1.0
name: Max F1
- type: max_f1_threshold
value: 9.330949783325195
name: Max F1 Threshold
- type: max_precision
value: 1.0
name: Max Precision
- type: max_recall
value: 1.0
name: Max Recall
- type: max_ap
value: 1.0
name: Max Ap
---
# SentenceTransformer based on cointegrated/rubert-tiny2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cointegrated/rubert-tiny2](https://huggingface.co/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](https://huggingface.co/cointegrated/rubert-tiny2) <!-- at revision dad72b8f77c5eef6995dd3e4691b758ba56b90c3 -->
- **Maximum Sequence Length:** 2048 tokens
- **Output Dimensionality:** 312 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'Беговая дорожка Hasttings CT100',
'Вертикальный велотренажер Sole B94 (2023)',
'Беговая дорожка Koenigsmann ML в Москве',
]
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]
```
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Binary Classification
* Dataset: `cv`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:--------|
| cosine_accuracy | 1.0 |
| cosine_accuracy_threshold | 0.7653 |
| cosine_f1 | 1.0 |
| cosine_f1_threshold | 0.7653 |
| cosine_precision | 1.0 |
| cosine_recall | 1.0 |
| cosine_ap | 1.0 |
| dot_accuracy | 1.0 |
| dot_accuracy_threshold | 0.7653 |
| dot_f1 | 1.0 |
| dot_f1_threshold | 0.7653 |
| dot_precision | 1.0 |
| dot_recall | 1.0 |
| dot_ap | 1.0 |
| manhattan_accuracy | 1.0 |
| manhattan_accuracy_threshold | 9.3309 |
| manhattan_f1 | 1.0 |
| manhattan_f1_threshold | 9.3309 |
| manhattan_precision | 1.0 |
| manhattan_recall | 1.0 |
| manhattan_ap | 1.0 |
| euclidean_accuracy | 1.0 |
| euclidean_accuracy_threshold | 0.6849 |
| euclidean_f1 | 1.0 |
| euclidean_f1_threshold | 0.6849 |
| euclidean_precision | 1.0 |
| euclidean_recall | 1.0 |
| euclidean_ap | 1.0 |
| max_accuracy | 1.0 |
| max_accuracy_threshold | 9.3309 |
| max_f1 | 1.0 |
| max_f1_threshold | 9.3309 |
| max_precision | 1.0 |
| max_recall | 1.0 |
| **max_ap** | **1.0** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 13,690 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 8 tokens</li><li>mean: 15.4 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.39 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------|:-----------------|
| <code>Велотренажер аэродинамический Spirit Fitness AB900+ Air Bike в Москве</code> | <code>Баттерфляй / Задняя дельта Impulse ExoForm FE9715</code> | <code>0.0</code> |
| <code>Эллиптический тренажер Sports Art E835</code> | <code>Эллиптический тренажер Clear Fit AirElliptical AE 40</code> | <code>1.0</code> |
| <code>Мультистанция Nohrd SlimBeam</code> | <code>Сведение бедра UltraGym LF-510</code> | <code>0.0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 28 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 28 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 8 tokens</li><li>mean: 14.79 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 16.21 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.57</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------|:-----------------|
| <code>Беговая дорожка Carbon Yukon</code> | <code>Кросстренер Octane Fitness Max Trainer MTX в Москве</code> | <code>0.0</code> |
| <code>Беговая дорожка Беговая дорожка DFC BOSS I T-B1 для реабилитации</code> | <code>Беговая дорожка Protrain N6J</code> | <code>1.0</code> |
| <code>Грузоблочный тренажер Precor C010ES - жим ногами/икроножные в Москве</code> | <code>Ягодичные мышцы Bronze Gym MNM-016A</code> | <code>1.0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"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
<details><summary>Click to expand</summary>
- `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
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | cv_max_ap |
|:------:|:----:|:-------------:|:------:|:---------:|
| 0 | 0 | - | - | 0.6247 |
| 1.0 | 428 | - | 0.0121 | 0.9407 |
| 1.1682 | 500 | 0.0121 | - | - |
| 2.0 | 856 | - | 0.0105 | 0.9805 |
| 2.3364 | 1000 | 0.0037 | - | - |
| 3.0 | 1284 | - | 0.0085 | 0.9821 |
| 3.5047 | 1500 | 0.0028 | - | - |
| 4.0 | 1712 | - | 0.0073 | 0.9891 |
| 4.6729 | 2000 | 0.0025 | - | - |
| 5.0 | 2140 | - | 0.0065 | 0.9924 |
| 5.8411 | 2500 | 0.0021 | - | - |
| 6.0 | 2568 | - | 0.0053 | 0.9963 |
| 7.0 | 2996 | - | 0.0055 | 0.9963 |
| 7.0093 | 3000 | 0.0018 | - | - |
| 8.0 | 3424 | - | 0.0041 | 1.0 |
| 8.1776 | 3500 | 0.0015 | - | - |
| 9.0 | 3852 | - | 0.0040 | 1.0 |
| 9.3458 | 4000 | 0.0014 | - | - |
| 10.0 | 4280 | - | 0.0036 | 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
```bibtex
@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
```bibtex
@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}
}
```
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