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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3072899
- loss:MSELoss
widget:
- source_sentence: That means you can see that disc 80 feet down.
sentences:
- >-
Он также сказал, что наводнение, идущее вниз по течению в приходе Ассеншен,
является угрозой, так как эти вздувшиеся реки будут медленно стекать в озеро
Морпа. «В киберфутбол играют десятки миллионов людей по всему континенту, и
мы рады дать шанс участникам состязания из наших национальных ассоциаций
представлять свою страну на самом высоком уровне», – заявил директор по
маркетингу УЕФА Ги-Лоран Эпстейн.
- >-
Компания Нортэма также заменяет замки в домах и машинах на совместимые с
чипом по цене в 300 фунтов за один замок.
- Это значит, что диск можно увидеть на глубине 80 футов.
- source_sentence: >-
There, you can also take baths in wine, pearls, iodine-bromine, selenium,
and sage-liquorice, depending on what the doctor prescribes for you.
sentences:
- >-
Организация даже учредила первый и единственный заповедник летучих мышей в
поместье Трив в Дамфрис-энд-Галловей, который является домом для восьми из
десяти видов летучих мышей в Шотландии.
- >-
Вместе мы гораздо сильнее, чем по отдельности. Экспертный звуковой анализ
всех записей установит частоту криков летучих мышей, а также какой вид что
делает.
- >-
Там можно принимать также ванны винные, жемчужные, йодобромные, селеновые,
шалфейно-лакричные, в зависимости от того, что вам назначит врача.
- source_sentence: But on Pine Ridge, I will always be what is called "wasichu."
sentences:
- >-
И я много думал о том, как это может быть применимо к разным уровням
реальности, скажем, в плане экологии.
- я всегда буду тем, кого называют ващичу,
- >-
Так что если мы можем сделать это, то мы можем высвободить ресурсы для
закупки лекарств, которые действительно нужны для лечения СПИДа, и ВИЧ, и
малярии, и для предотвращения птичьего гриппа. Спасибо.
- source_sentence: And Bertie County is no exception to this.
sentences:
- И округ Берти - не исключение.
- >-
Кажется, в природе существует закон о том, что подходить слишком близко к
месту, откуда ты произошел, опасно.
- Они устали от договоренностей. Они устали от священных холмов.
- source_sentence: Transparency is absolutely critical to this.
sentences:
- >-
Первая: непреклонность местных лидеров к установлению чего-либо меньшего,
чем их максимальные требования.
- Прозрачность - абсолютно критична в этом процессе.
- Мы покупаем его нашим детям.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- negative_mse
- src2trg_accuracy
- trg2src_accuracy
- mean_accuracy
model-index:
- name: SentenceTransformer
results:
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: small content
type: small_content
metrics:
- type: negative_mse
value: -4.356895923614502
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: small content
type: small_content
metrics:
- type: src2trg_accuracy
value: 0.7375
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.665
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.70125
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: big content
type: big_content
metrics:
- type: negative_mse
value: -3.541424036026001
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: big content
type: big_content
metrics:
- type: src2trg_accuracy
value: 0.8285
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.668
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.7482500000000001
name: Mean Accuracy
license: apache-2.0
language:
- en
- ru
base_model:
- answerdotai/ModernBERT-base
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained on the corpus dataset. 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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- corpus
<!-- - **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: ModernBertModel
(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("whitemouse84/ModernBERT-base-en-ru-v1")
# Run inference
sentences = [
'Transparency is absolutely critical to this.',
'Прозрачность - абсолютно критична в этом процессе.',
'Мы покупаем его нашим детям.',
]
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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</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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## Evaluation
### Metrics
#### Knowledge Distillation
* Datasets: `small_content` and `big_content`
* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | small_content | big_content |
|:-----------------|:--------------|:------------|
| **negative_mse** | **-4.3569** | **-3.5414** |
#### Translation
* Datasets: `small_content` and `big_content`
* Evaluated with [<code>TranslationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | small_content | big_content |
|:------------------|:--------------|:------------|
| src2trg_accuracy | 0.7375 | 0.8285 |
| trg2src_accuracy | 0.665 | 0.668 |
| **mean_accuracy** | **0.7013** | **0.7483** |
#### Encodechka
| Model | STS | PI | NLI | SA | TI | IA | IC | ICX |
|:--------------------------|:--------------|:------------|:--------------|:------------|:--------------|:------------|:--------------|:------------|
| ModernBERT-base-en-ru-v1 | 0.602 | **0.521** | 0.355 | 0.722 | 0.892 | 0.704 | **0.747** | **0.591** |
| ModernBERT-base | 0.498 | 0.239 | 0.358 | 0.643 | 0.786 | 0.623 | 0.593 | 0.104 |
| EuroBERT-210m | **0.619** | 0.452 | **0.369** | 0.702 | 0.875 | 0.703 | 0.647 | 0.192 |
| xlm-roberta-base | 0.552 | 0.439 | 0.362 | **0.752** | **0.940** | **0.768** | 0.695 | 0.520 |
<!--
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### corpus
* Dataset: corpus
* Size: 2,000,000 training samples
* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | english | non_english | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | <ul><li>min: 4 tokens</li><li>mean: 29.26 tokens</li><li>max: 133 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 71.46 tokens</li><li>max: 285 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
* Samples:
| english | non_english | label |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|
| <code>Hence it can be said that Voit is a well-satisfied customer, and completely convinced of the potential offered by Voortman machines for his firm.</code> | <code>В конечном итоге можно утверждать, что компания Voit довольна своим выбором, ведь она имела возможность убедиться в качественных характеристиках оборудования Voortman.</code> | <code>[0.1702279895544052, -0.6711388826370239, -0.5062062740325928, 0.14078450202941895, 0.15188495814800262, ...]</code> |
| <code>We want to feel good, we want to be happy, in fact happiness is our birthright.</code> | <code>Мы хотим чувствовать себя хорошо, хотим быть счастливы.</code> | <code>[0.556108295917511, -0.42819586396217346, -0.25372204184532166, 0.099883534014225, 0.7299532294273376, ...]</code> |
| <code>In Germany, Arcandor - a major holding company in the mail order, retail and tourism industries that reported €21 billion in 2007 sales - threatens to become the first victim of tighter credit terms.</code> | <code>В Германии Arcandor - ключевая холдинговая компания в сфере посылочной и розничной торговли, а также индустрии туризма, в финансовых отчетах которой за 2007 год значился торговый оборот в размере €21 миллиардов - грозит стать первой жертвой ужесточения условий кредитования.</code> | <code>[-0.27140647172927856, -0.5173773169517517, -0.6571329236030579, 0.21765929460525513, -0.01978394016623497, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Evaluation Datasets
#### small_content
* Dataset: small_content
* Size: 2,000 evaluation samples
* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | english | non_english | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | <ul><li>min: 4 tokens</li><li>mean: 24.13 tokens</li><li>max: 252 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 53.83 tokens</li><li>max: 406 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
* Samples:
| english | non_english | label |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------|
| <code>Thank you so much, Chris.</code> | <code>Спасибо, Крис.</code> | <code>[1.0408389568328857, 0.3253674805164337, -0.12651680409908295, 0.45153331756591797, 0.4052223563194275, ...]</code> |
| <code>And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful.</code> | <code>Это огромная честь, получить возможность выйти на эту сцену дважды. Я неимоверно благодарен.</code> | <code>[0.6990637183189392, -0.4462655782699585, -0.5292129516601562, 0.23709823191165924, 0.32307693362236023, ...]</code> |
| <code>I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night.</code> | <code>Я в восторге от этой конференции, и я хочу поблагодарить вас всех за благожелательные отзывы о моем позавчерашнем выступлении.</code> | <code>[0.8470447063446045, -0.17461800575256348, -0.7178670167922974, 0.6488378047943115, 0.6101466417312622, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### big_content
* Dataset: big_content
* Size: 2,000 evaluation samples
* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | english | non_english | label |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | <ul><li>min: 6 tokens</li><li>mean: 43.84 tokens</li><li>max: 141 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 107.9 tokens</li><li>max: 411 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
* Samples:
| english | non_english | label |
|:------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|
| <code>India has recorded a surge in COVID-19 cases in the past weeks, with over 45,000 new cases detected every day since July 23.</code> | <code>Индия зафиксировала резкий всплеск случаев заражения COVID-19 за последние недели, с 23 июля каждый день выявляется более 45 000 новых случаев.</code> | <code>[-0.12528948485851288, -0.49428656697273254, -0.07556094229221344, 0.8069225549697876, 0.20946118235588074, ...]</code> |
| <code>A bloom the Red Tide extends approximately 130 miles of coastline from northern Pinellas to southern Lee counties.</code> | <code>Цветение Красного Прилива простирается примерно на 130 миль дволь береговой линии от Пинеллас на севере до округа Ли на юге.</code> | <code>[0.027262285351753235, -0.4401558041572571, -0.3353440463542938, 0.11166133731603622, -0.2294958084821701, ...]</code> |
| <code>Among those affected by the new rules is Transport Secretary Grant Shapps, who began his holiday in Spain on Saturday.</code> | <code>Среди тех, кого затронули новые правила, оказался министр транспорта Грант Шэппс, у которого в субботу начался отпуск в Испании.</code> | <code>[0.1868007630109787, -0.18781621754169464, -0.48890581727027893, 0.328614205121994, 0.36041054129600525, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: 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`: 4
- `per_device_eval_batch_size`: 4
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `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`: 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`: True
- `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`: 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>
### Framework Versions
- Python: 3.13.2
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu126
- 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",
}
```
#### MSELoss
```bibtex
@inproceedings{reimers-2020-multilingual-sentence-bert,
title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2004.09813",
}
```
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