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	---
	base_model: ai-forever/ruRoberta-large
	datasets: []
	language: []
	library_name: sentence-transformers
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:19383
	- loss:MultipleNegativesRankingLoss
	widget:
	- source_sentence: '12.02.2.17 Панель ингаляционных аллергенов № 9 (IgE): эпителий
	кошки, перхоть собаки, овсяница луговая'
	sentences:
	- Панель аллергенов плесени № 1 IgE (penicillium notatum, cladosporium herbarum,
	aspergillus fumigatus, candida albicans, alternaria tenuis),
	- Панель пищевых аллергенов № 51 IgE (помидор, картофель, морковь, чеснок, горчица),
	- Прием (осмотр, консультация) врача-психотерапевта первичный
	- source_sentence: '12.02.2.2.04 Панель пищевых аллергенов № 2 (IgG): треска, тунец,
	креветки, лосось, мидии'
	sentences:
	- Панель пищевых аллергенов № 5 IgE (яичный белок, молоко, треска, пшеничная мука,
	арахис, соевые бобы),
	- Панель пищевых аллергенов № 7 IgE (яичный белок, рис, коровье молоко, aрахис,
	пшеничная мука, соевые бобы),
	- Панель ингаляционных аллергенов № 3 IgE (клещ - дерматофаг перинный, эпителий
	кошки, эпителий собаки, плесневый гриб (Aspergillus fumigatus)),
	- source_sentence: 12.4.6.04 Аллерген f27 - говядина, IgE (ImmunoCAP)
	sentences:
	- Панель ингаляционных аллергенов № 3 IgE (клещ - дерматофаг перинный, эпителий
	кошки, эпителий собаки, плесневый гриб (Aspergillus fumigatus)),
	- Панель аллергенов животных/перья птиц/ № 71 IgE (перо гуся, перо курицы, перо
	утки, перо индюка),
	- Панель ингаляционных аллергенов № 6 IgE (плесневый гриб (Cladosporium herbarum),
	тимофеевка, плесневый гриб (Alternaria tenuis), береза, полынь обыкновенная),
	- source_sentence: Микробиологическое исследование биосубстатов на микрофлору (отделяемое
	зева, носа, глаз, ушей, гениталий, ран,мокрота) с постановкой чувствительности
	[Мартьянова]
	sentences:
	- Панель ингаляционных аллергенов № 9 IgE (эпителий кошки, перхоть собаки, овсяница
	луговая, плесневый гриб (Alternaria tenuis), подорожник),
	- Панель аллергенов плесени № 1 IgE (penicillium notatum, cladosporium herbarum,
	aspergillus fumigatus, candida albicans, alternaria tenuis),
	- Посев отделяемого верхних дыхательных путей на микрофлору, определение чувствительности
	к антимикробным препаратам (одна локализация) (Upper Respiratory Culture. Bacteria
	Identification and Antibiotic Susceptibility Testing)*
	- source_sentence: НЕТ ДО 20.04!!!!!!!! 12.01.16 Аллергокомпонент f77 - бета-лактоглобулин
	nBos d 5, IgE (ImmunoCAP)
	sentences:
	- Ультразвуковое исследование плода
	- Панель аллергенов животных № 70 IgE (эпителий морской свинки, эпителий кролика,
	хомяк, крыса, мышь),
	- Панель пищевых аллергенов № 15 IgE (апельсин, банан, яблоко, персик),
	---

	# SentenceTransformer based on ai-forever/ruRoberta-large

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [ai-forever/ruRoberta-large](https://huggingface.co/ai-forever/ruRoberta-large). 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: [ai-forever/ruRoberta-large](https://huggingface.co/ai-forever/ruRoberta-large) <!-- at revision 5192d064ca6ac67c14c40e017ce41612e010f05f -->
	- Maximum Sequence Length: 514 tokens
	- Output Dimensionality: 1024 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': 514, 'do_lower_case': False}) with Transformer model: RobertaModel
	(1): Pooling({'word_embedding_dimension': 1024, '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 = [
	'НЕТ ДО 20.04!!!!!!!! 12.01.16 Аллергокомпонент f77 - бета-лактоглобулин nBos d 5, IgE (ImmunoCAP)',
	'Панель аллергенов животных № 70 IgE (эпителий морской свинки, эпителий кролика, хомяк, крыса, мышь),',
	'Ультразвуковое исследование плода',
	]
	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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	</details>
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	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
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	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 19,383 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: 5 tokens</li><li>mean: 30.0 tokens</li><li>max: 121 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 30.73 tokens</li><li>max: 105 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \|
	\|:-------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------\|
	\| <code>Ингибитор VIII фактора</code> \| <code>Исследование уровня антигена фактора Виллебранда</code> \|
	\| <code>13.01.02 Антитела к экстрагируемому нуклеарному АГ (ЭНА/ENA-скрин), сыворотка крови</code> \| <code>Антитела к экстрагируемому ядерному антигену, кач.</code> \|
	\| <code>Нет 12.4.092 Аллерген f203 - фисташковые орехи, IgE</code> \| <code>Панель аллергенов деревьев № 2 IgE (клен ясенелистный, тополь, вяз, дуб, пекан),</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`: 4
	- `per_device_eval_batch_size`: 4
	- `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`: 4
	- `per_device_eval_batch_size`: 4
	- `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
	<details><summary>Click to expand</summary>

	\| Epoch \| Step \| Training Loss \|
	\|:-------:\|:-----:\|:-------------:\|
	\| 0.1032 \| 500 \| 0.7937 \|
	\| 0.2064 \| 1000 \| 0.5179 \|
	\| 0.3095 \| 1500 \| 0.5271 \|
	\| 0.4127 \| 2000 \| 0.5696 \|
	\| 0.5159 \| 2500 \| 0.5232 \|
	\| 0.6191 \| 3000 \| 0.6401 \|
	\| 0.7222 \| 3500 \| 0.6337 \|
	\| 0.8254 \| 4000 \| 0.9436 \|
	\| 0.9286 \| 4500 \| 1.3872 \|
	\| 1.0318 \| 5000 \| 1.3834 \|
	\| 1.1350 \| 5500 \| 0.9831 \|
	\| 1.2381 \| 6000 \| 1.0122 \|
	\| 1.3413 \| 6500 \| 1.3708 \|
	\| 1.4445 \| 7000 \| 1.3794 \|
	\| 1.5477 \| 7500 \| 1.3784 \|
	\| 1.6508 \| 8000 \| 1.3856 \|
	\| 1.7540 \| 8500 \| 1.3809 \|
	\| 1.8572 \| 9000 \| 1.3776 \|
	\| 1.9604 \| 9500 \| 1.0041 \|
	\| 2.0636 \| 10000 \| 0.8559 \|
	\| 2.1667 \| 10500 \| 0.8531 \|
	\| 2.2699 \| 11000 \| 0.8446 \|
	\| 2.3731 \| 11500 \| 0.8487 \|
	\| 2.4763 \| 12000 \| 1.0807 \|
	\| 2.5794 \| 12500 \| 1.3792 \|
	\| 2.6826 \| 13000 \| 1.3923 \|
	\| 2.7858 \| 13500 \| 1.3787 \|
	\| 2.8890 \| 14000 \| 1.3803 \|
	\| 2.9922 \| 14500 \| 1.3641 \|
	\| 3.0953 \| 15000 \| 1.3725 \|
	\| 3.1985 \| 15500 \| 1.3624 \|
	\| 3.3017 \| 16000 \| 1.3659 \|
	\| 3.4049 \| 16500 \| 1.3609 \|
	\| 3.5080 \| 17000 \| 1.3496 \|
	\| 3.6112 \| 17500 \| 1.3639 \|
	\| 3.7144 \| 18000 \| 1.3487 \|
	\| 3.8176 \| 18500 \| 1.3463 \|
	\| 3.9208 \| 19000 \| 1.336 \|
	\| 4.0239 \| 19500 \| 1.3451 \|
	\| 4.1271 \| 20000 \| 1.3363 \|
	\| 4.2303 \| 20500 \| 1.3411 \|
	\| 4.3335 \| 21000 \| 1.3376 \|
	\| 4.4366 \| 21500 \| 1.3294 \|
	\| 4.5398 \| 22000 \| 1.3281 \|
	\| 4.6430 \| 22500 \| 1.3323 \|
	\| 4.7462 \| 23000 \| 1.3411 \|
	\| 4.8494 \| 23500 \| 1.3162 \|
	\| 4.9525 \| 24000 \| 1.3204 \|
	\| 5.0557 \| 24500 \| 1.324 \|
	\| 5.1589 \| 25000 \| 1.3253 \|
	\| 5.2621 \| 25500 \| 1.3283 \|
	\| 5.3652 \| 26000 \| 1.3298 \|
	\| 5.4684 \| 26500 \| 1.3144 \|
	\| 5.5716 \| 27000 \| 1.3162 \|
	\| 5.6748 \| 27500 \| 1.3148 \|
	\| 5.7780 \| 28000 \| 1.3254 \|
	\| 5.8811 \| 28500 \| 1.319 \|
	\| 5.9843 \| 29000 \| 1.3134 \|
	\| 6.0875 \| 29500 \| 1.3184 \|
	\| 6.1907 \| 30000 \| 1.3049 \|
	\| 6.2939 \| 30500 \| 1.3167 \|
	\| 6.3970 \| 31000 \| 1.3192 \|
	\| 6.5002 \| 31500 \| 1.2926 \|
	\| 6.6034 \| 32000 \| 1.3035 \|
	\| 6.7066 \| 32500 \| 1.3117 \|
	\| 6.8097 \| 33000 \| 1.3093 \|
	\| 6.9129 \| 33500 \| 1.278 \|
	\| 7.0161 \| 34000 \| 1.3143 \|
	\| 7.1193 \| 34500 \| 1.3144 \|
	\| 7.2225 \| 35000 \| 1.304 \|
	\| 7.3256 \| 35500 \| 1.3066 \|
	\| 7.4288 \| 36000 \| 1.2916 \|
	\| 7.5320 \| 36500 \| 1.2943 \|
	\| 7.6352 \| 37000 \| 1.2883 \|
	\| 7.7383 \| 37500 \| 1.3014 \|
	\| 7.8415 \| 38000 \| 1.3005 \|
	\| 7.9447 \| 38500 \| 1.2699 \|
	\| 8.0479 \| 39000 \| 1.3042 \|
	\| 8.1511 \| 39500 \| 1.289 \|
	\| 8.2542 \| 40000 \| 1.3012 \|
	\| 8.3574 \| 40500 \| 1.3017 \|
	\| 8.4606 \| 41000 \| 1.272 \|
	\| 8.5638 \| 41500 \| 1.2939 \|
	\| 8.6669 \| 42000 \| 1.2764 \|
	\| 8.7701 \| 42500 \| 1.2908 \|
	\| 8.8733 \| 43000 \| 1.2619 \|
	\| 8.9765 \| 43500 \| 1.2791 \|
	\| 9.0797 \| 44000 \| 1.2722 \|
	\| 9.1828 \| 44500 \| 1.278 \|
	\| 9.2860 \| 45000 \| 1.2911 \|
	\| 9.3892 \| 45500 \| 1.2791 \|
	\| 9.4924 \| 46000 \| 1.2791 \|
	\| 9.5955 \| 46500 \| 1.2782 \|
	\| 9.6987 \| 47000 \| 1.2789 \|
	\| 9.8019 \| 47500 \| 1.2858 \|
	\| 9.9051 \| 48000 \| 1.2601 \|
	\| 10.0083 \| 48500 \| 1.29 \|
	\| 10.1114 \| 49000 \| 1.276 \|
	\| 10.2146 \| 49500 \| 1.2801 \|
	\| 10.3178 \| 50000 \| 1.2853 \|
	\| 10.4210 \| 50500 \| 1.2655 \|
	\| 10.5241 \| 51000 \| 1.271 \|
	\| 10.6273 \| 51500 \| 1.2633 \|
	\| 10.7305 \| 52000 \| 1.2565 \|
	\| 10.8337 \| 52500 \| 1.2755 \|
	\| 10.9369 \| 53000 \| 1.2567 \|

	</details>

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.1
	- Transformers: 4.41.2
	- PyTorch: 2.3.0+cu121
	- Accelerate: 0.31.0
	- 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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