README.md

---
base_model: DeepPavlov/rubert-base-cased-sentence
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:29127
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Медицинское освидетельствование на состояние опьянения

    (алкогольное, наркотическое и иное токсическое согласно приказу МЗ РФ № 933н от

    18.12.2015г.)'
  sentences:
  - Патолого-анатомическое исследование биопсийного (операционного) материала матки,
    придатков, стенки кишки
  - Медицинское освидетельствование на состояние опьянения (алкогольного, наркотического
    или иного токсического)
  - Определение содержания антител к эндомизию в крови
- source_sentence: УЗИ придаточных (верхнечелюстных) пазух
  sentences:
  - Рентгенография позвоночника, вертикальная
  - Прием (осмотр, консультация) врача-офтальмолога первичный
  - Ультразвуковое исследование околоносовых пазух
- source_sentence: Прием (осмотр, консультация) врача-челюстно-лицевого хирурга повторный
  sentences:
  - Магнитно-резонансная томография шеи
  - Тимпанометрия
  - Прием (осмотр, консультация) врача-челюстно-лицевого хирурга повторный
- source_sentence: (200) АЛТ (аланинаминотрансфераза)
  sentences:
  - Определение активности аланинаминотрансферазы в крови
  - Рентгенография грудного и поясничного отдела позвоночника
  - Анализ спектра органических кислот мочи методом газовой хроматографии с масс-спектрометрией
- source_sentence: Витамин 25(OH)D2 и 25(OH)D3, раздельное определение (ВЭЖХ - МС/МС)
  sentences:
  - Исследование уровня 25-OH витамина Д в крови
  - Определение содержания антител к париетальным клеткам желудка
  - Прием (осмотр, консультация) врача-детского хирурга повторный
---

# SentenceTransformer based on DeepPavlov/rubert-base-cased-sentence

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [DeepPavlov/rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence). 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:** [DeepPavlov/rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence) <!-- at revision 78b5122d6365337dd4114281b0d08cd1edbb3bc8 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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:

```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 = [
    'Витамин 25(OH)D2 и 25(OH)D3, раздельное определение (ВЭЖХ - МС/МС)',
    'Исследование уровня 25-OH витамина Д в крови',
    'Определение содержания антител к париетальным клеткам желудка',
]
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]
```

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 29,127 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                         | sentence_1                                                                       |
  |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                           |
  | details | <ul><li>min: 4 tokens</li><li>mean: 19.98 tokens</li><li>max: 110 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.0 tokens</li><li>max: 60 tokens</li></ul> |
* Samples:
  | sentence_0                                                                     | sentence_1                                                                          |
  |:-------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | <code>Ультразвуковое исследование органов малого таза <br>(комплексное)</code> | <code>Ультразвуковое исследование органов малого таза</code>                        |
  | <code>МРТ головного мозга (исследование структуры головного мозга)</code>      | <code>Магнитно-резонансная томография головного мозга с контрастированием</code>    |
  | <code>Антитела к лямблиям (Lamblia intestinalis), суммарные</code>             | <code>Определение антител классов A, M, G (IgM, IgA, IgG) к лямблиям в крови</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 11
- `multi_dataset_batch_sampler`: round_robin

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `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
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 11
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: False
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch   | Step  | Training Loss |
|:-------:|:-----:|:-------------:|
| 0.5488  | 500   | 0.8526        |
| 1.0977  | 1000  | 0.3415        |
| 1.6465  | 1500  | 0.2691        |
| 2.1954  | 2000  | 0.218         |
| 2.7442  | 2500  | 0.188         |
| 3.2931  | 3000  | 0.1725        |
| 3.8419  | 3500  | 0.1533        |
| 4.3908  | 4000  | 0.1508        |
| 4.9396  | 4500  | 0.1391        |
| 5.4885  | 5000  | 0.1311        |
| 6.0373  | 5500  | 0.1284        |
| 6.5862  | 6000  | 0.122         |
| 7.1350  | 6500  | 0.1163        |
| 7.6839  | 7000  | 0.1102        |
| 8.2327  | 7500  | 0.1068        |
| 8.7816  | 8000  | 0.1046        |
| 9.3304  | 8500  | 0.1018        |
| 9.8793  | 9000  | 0.0987        |
| 10.4281 | 9500  | 0.0983        |
| 10.9769 | 10000 | 0.0971        |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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