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nanalysenko
/
panacea_v2.2

Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
Generated from Trainer
dataset_size:19383
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Inference Endpoints
Model card Files Community
panacea_v2.2
File size: 19,082 Bytes 
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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]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## 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.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## 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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