README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:80415
- loss:MultipleNegativesRankingLoss
base_model: lingtrain/labse-buryat
widget:
- source_sentence: 'Зуг эрднь-ишин силос келһнә , нань чигн кергүднь бас дегц дарцлдад
    , тәв һарсн наста агрономд дав деерән цагнь беркдҗ бәәхнь Долдад медгднә . '
  sentences:
  - быть товарищем
  - 'Дола понимала , что агроному не так-то просто в эту страдную пору выкроить время
    . В связи с уборкой на него обрушилось множество забот . '
  - стеснение
- source_sentence: белгтə-йорта
  sentences:
  - имеющий хорошее предзнаменование
  - ' выполнение пятилетнего плана'
  - помогать в перекочёвке
- source_sentence: 'Тедн нәә-хллдәд , мадн тал өөрдәд йовцхана ; мана толһа деер көмргдн
    гиҗәх мет , усн деер улм өкәһәд йовна , цаһан дольгас мана цогциг деегшән өсргәд
    хаяд бәәнә , мана оңһц , негл һосна дор хамхрҗах яңһг мет , тачкнад йовна , би
    ода оңһцасн салҗ одвв , хад чолудин утх мет иртә , хамхрад кемтрҗ одсн хар-хар
    орасинь үзҗәнәв , бийиннь деер ик өндрт , энүнә дарунь - эн эрлгүдин хумсдин деер
    эцгәннь толһа үзҗәнәв . '
  sentences:
  - 'Качаясь , они подвигались к нам , наклонялись над водой , готовые опрокинуться
    на головы наши , - раз , раз - подкидывают белые волны наши тела , хрустит наша
    барка , точно орех под каблуком сапога , я оторван от нее , вижу изломанные черные
    ребра скал , острые , как ножи , вижу голову отца высоко надо мною , потом - над
    этими когтями дьяволов . '
  - растопыривать
  - 'Пузыревский , дымя цигаркой , ожидал обмена мнений . '
- source_sentence: 'Би бахмҗта кевәр җирһлән эдлвв . '
  sentences:
  - затруднение
  - потому что
  - '- Я славно пожил !.. '
- source_sentence: 'аврлт угаһар тәвх '
  sentences:
  - хранилище
  - ' расправляться жестоким образом'
  - 'Богу покоряйся , и он даст тебе все , что попросишь у него '
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on lingtrain/labse-buryat

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [lingtrain/labse-buryat](https://huggingface.co/lingtrain/labse-buryat). 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:** [lingtrain/labse-buryat](https://huggingface.co/lingtrain/labse-buryat) <!-- at revision 7c2b75b82da5361a7dcd3356e881e03184f780cb -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
  (3): 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("sentence_transformers_model_id")
# Run inference
sentences = [
    'аврлт угаһар тәвх ',
    ' расправляться жестоким образом',
    'Богу покоряйся , и он даст тебе все , что попросишь у него ',
]
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: 80,415 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                         | sentence_1                                                                         | label                                                         |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | float                                                         |
  | details | <ul><li>min: 3 tokens</li><li>mean: 17.25 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 11.73 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                         | sentence_1                                            | label            |
  |:---------------------------------------------------|:------------------------------------------------------|:-----------------|
  | <code>Сарин киитн герл терз деер тусҗана . </code> | <code>Луна залила неживым светом подоконник . </code> | <code>1.0</code> |
  | <code>Тер цагт-социалистнр уга болх . </code>      | <code>Тогда - не будет социалистов . </code>          | <code>1.0</code> |
  | <code>мейəркгч</code>                              | <code>завистливый</code>                              | <code>1.0</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

- `eval_strategy`: steps
- `num_train_epochs`: 1
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin

#### 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`: 8
- `per_device_eval_batch_size`: 8
- `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`: 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`: 1
- `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`: 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`: 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}
- `tp_size`: 0
- `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
- `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`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0099 | 100  | -             |
| 0.0199 | 200  | -             |
| 0.0298 | 300  | -             |
| 0.0398 | 400  | -             |
| 0.0497 | 500  | 0.7566        |
| 0.0597 | 600  | -             |
| 0.0696 | 700  | -             |
| 0.0796 | 800  | -             |
| 0.0895 | 900  | -             |
| 0.0995 | 1000 | 0.503         |
| 0.1094 | 1100 | -             |
| 0.1194 | 1200 | -             |
| 0.1293 | 1300 | -             |
| 0.1393 | 1400 | -             |
| 0.1492 | 1500 | 0.4777        |
| 0.1592 | 1600 | -             |
| 0.1691 | 1700 | -             |
| 0.1791 | 1800 | -             |
| 0.1890 | 1900 | -             |
| 0.1990 | 2000 | 0.4608        |
| 0.2089 | 2100 | -             |
| 0.2189 | 2200 | -             |
| 0.2288 | 2300 | -             |
| 0.2388 | 2400 | -             |
| 0.2487 | 2500 | 0.419         |
| 0.2587 | 2600 | -             |
| 0.2686 | 2700 | -             |


### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 2.14.4
- Tokenizers: 0.21.1

## 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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