README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:77455
- loss:ContrastiveLoss
base_model: deepvk/USER-bge-m3
widget:
- source_sentence: Исследование антигена хеликобактера (Helicobacter pylori)
  sentences:
  - 'Токсоплазма (Toxoplasma gondii): Антитела: IgG, (количественно). Метод: ИФА'
  - 'Хеликобактер пилори (Helicobacter pylori): Антитела: IgG, (количественно). Метод:
    ИФА'
  - УЗИ молочных желез с эластографией
- source_sentence: Антитела к ХГЧ ( IgM и IgG)
  sentences:
  - Общий анализ мочи с микроскопией
  - Антитела к тканевой трансглутаминазе, IgG
  - Прием (осмотр, консультация) врача-онколога в клинике
- source_sentence: Белок общий в суточной моче
  sentences:
  - Уран в моче
  - Белок общий в сыворотке
  - Исследование уровня антигена фактора Виллебранда
- source_sentence: 'Развернутая диагностика склеродермии (иммуноблот антинуклеарных
    антител: анти-Scl-70, CENT-A, CENT-B, RP11, RP155, Fibrillarin, NOR90, Th/To,
    PM-Scl 75, Ku, PDGFR, Ro-52) и антинуклеарный фактор (АНФ), иммуноблот'
  sentences:
  - 'Токсоплазма (Toxoplasma gondii): Антитела: IgG, (количественно). Метод: ИФА'
  - 'Эпителий кролика, IgE, аллерген - e82. Метод: ImmunoCAP'
  - Антинуклеарные антитела, IgG (анти-Sm, RNP/Sm, SS-A, SS-B, Scl-70, PM-Scl, PCNA,
    dsDNA, CENT-B, Jo-1, к гистонам, к нуклеосомам, Ribo P, AMA-M2), иммуноблот
- source_sentence: Определение активности амилазы в моче
  sentences:
  - Амилаза общая в суточной моче
  - 'Микоплазма гениталиум (Mycoplasma genitalium): ДНК, (качественно). Метод: реал-тайм
    ПЦР'
  - 'Пенициллин V, IgE, аллерген - c2. Метод: ИФА'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: SentenceTransformer based on deepvk/USER-bge-m3
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: binary eval test
      type: binary-eval-test
    metrics:
    - type: cosine_accuracy
      value: 0.9442084651302907
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7781298160552979
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.8599519663764634
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7724614143371582
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.8569932685115931
      name: Cosine Precision
    - type: cosine_recall
      value: 0.8629311643319777
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9223179041268369
      name: Cosine Ap
    - type: cosine_mcc
      value: 0.824796426094371
      name: Cosine Mcc
---

# SentenceTransformer based on deepvk/USER-bge-m3

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [deepvk/USER-bge-m3](https://huggingface.co/deepvk/USER-bge-m3). It maps sentences & paragraphs to a 1024-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:** [deepvk/USER-bge-m3](https://huggingface.co/deepvk/USER-bge-m3) <!-- at revision 0cc6cfe48e260fb0474c753087a69369e88709ae -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 dimensions
- **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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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("EvgenyBondarenko/BIEncoderRanker")
# Run inference
sentences = [
    'Определение активности амилазы в моче',
    'Амилаза общая в суточной моче',
    'Пенициллин V, IgE, аллерген - c2. Метод: ИФА',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

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## Evaluation

### Metrics

#### Binary Classification

* Dataset: `binary-eval-test`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                    | Value      |
|:--------------------------|:-----------|
| cosine_accuracy           | 0.9442     |
| cosine_accuracy_threshold | 0.7781     |
| cosine_f1                 | 0.86       |
| cosine_f1_threshold       | 0.7725     |
| cosine_precision          | 0.857      |
| cosine_recall             | 0.8629     |
| **cosine_ap**             | **0.9223** |
| cosine_mcc                | 0.8248     |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 77,455 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                          | sentence2                                                                         | label                                           |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                             | string                                                                            | int                                             |
  | details | <ul><li>min: 5 tokens</li><li>mean: 22.98 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 22.68 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>0: ~80.00%</li><li>1: ~20.00%</li></ul> |
* Samples:
  | sentence1                                                                                                    | sentence2                                                                                                                                                  | label          |
  |:-------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>Посев с кожи на аэробную микрофлору и определение чувствительности к  антимикробным  препаратам</code> | <code>Посев кожи на аэробную и факультативно-анаэробную флору: Определение чувствительности к антибиотикам. Метод: культуральный</code>                    | <code>1</code> |
  | <code>Посев с кожи на аэробную микрофлору и определение чувствительности к  антимикробным  препаратам</code> | <code>Посев отделяемого с кожи/раны на аэробную и факультативно-анаэробную флору: Определение чувствительности к антибиотикам. Метод: культуральный</code> | <code>0</code> |
  | <code>Посев с кожи на аэробную микрофлору и определение чувствительности к  антимикробным  препаратам</code> | <code>Посев отделяемого из глаза на аэробную и факультативно-анаэробную флору: Определение чувствительности к антибиотикам. Метод: культуральный</code>    | <code>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: 33,195 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                          | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                             | int                                             |
  | details | <ul><li>min: 6 tokens</li><li>mean: 21.52 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 23.61 tokens</li><li>max: 140 tokens</li></ul> | <ul><li>0: ~80.00%</li><li>1: ~20.00%</li></ul> |
* Samples:
  | sentence1                                    | sentence2                                                    | label          |
  |:---------------------------------------------|:-------------------------------------------------------------|:---------------|
  | <code>транскраниальная допплерография</code> | <code>УЗИ сосудов головного мозга</code>                     | <code>1</code> |
  | <code>транскраниальная допплерография</code> | <code>УЗИ сосудов глаза (доплерография)</code>               | <code>0</code> |
  | <code>транскраниальная допплерография</code> | <code>Магнитно-резонансная томография головного мозга</code> | <code>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`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `save_only_model`: True
- `fp16`: True
- `load_best_model_at_end`: True

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `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`: 2e-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`: 1
- `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`: True
- `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`: True
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | binary-eval-test_cosine_ap |
|:------:|:----:|:-------------:|:---------------:|:--------------------------:|
| 0.2065 | 500  | 0.0125        | 0.0096          | 0.8634                     |
| 0.4131 | 1000 | 0.0094        | 0.0082          | 0.8940                     |
| 0.6196 | 1500 | 0.0086        | 0.0073          | 0.9150                     |
| 0.8261 | 2000 | 0.0078        | 0.0068          | 0.9223                     |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu118
- Accelerate: 1.4.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0

## 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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