sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:11113 loss:CosineSimilarityLoss arxiv:1908.10084 base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:11113
- loss:CosineSimilarityLoss
widget:
- source_sentence: Фэнүүддээ сайхан мэдээг дуулгажээ
  sentences:
  - Фэнүүддээ муу мэдээг дуулгажээ
  - Жүжгийг 14.00 болон 16.00 цагаас тоглоно.
  - Киноны дараа хэлэлцүүлэг болно.
- source_sentence: Фрида 22 насандаа Диего Риверагийн эхнэр болжээ
  sentences:
  - Хүрэл металлын найрлагад олон төрлийн элементүүд ордог бөгөөд цэвэр хүрлийг гарган
    авдаг
  - “Жонон” хамтлаг олон улсын хэмжээнд тоглолт хийхээр төлөвлөж байна.
  - Тэдний гэр бүлийн амьдрал буцалж байв.
- source_sentence: Тоглолтыг ССАЖЯ-ны дэмжлэгтэй зохион байгуулжээ
  sentences:
  - Тоглолт аравдугаар сарын 26-нд болно.
  - Цомогт мал аж ахуйн сэдэвтэй дуунууд багтсан
  - Тоглолт өвөрмөц тайз, онцгой хөтөлбөртэй
- source_sentence: '"TJ" энтертайнменттэй хамтран ажиллаж байна'
  sentences:
  - Тодорхой хэмжээгээр урлаг­тайгаа л байна
  - “Алтан хуур” наадмын зохион байгуулагчид мэдээлэл хийлээ
  - Тэд хамтран podcast хийж байна
- source_sentence: дөнгөж арван настайдаа олгойны хагалгаанд орж байсан
  sentences:
  - Түүнээс би монгол эрчүүд ийм, тийм гэж боддог учраас хань, нөхрөөрөө сонгохгүй
    байгаа юм биш
  - '"Домог" чуулгын тоглолт Монгол иргэдэд зориулагджээ'
  - Энэ мэтчилэн болсон болоогүй өвчин тусдаг нэг тийм л хүүхэд байсан юм шиг байгаа
    юм.
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.9672191293060537
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9652101071464687
      name: Spearman Cosine
---

# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the csv dataset. It maps sentences & paragraphs to a 384-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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 8d6b950845285729817bf8e1af1861502c2fed0c -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - csv
<!-- - **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': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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("gmunkhtur/paraphrase-multilingual-minilm-l12-v2-mn")
# Run inference
sentences = [
    'дөнгөж арван настайдаа олгойны хагалгаанд орж байсан',
    'Энэ мэтчилэн болсон болоогүй өвчин тусдаг нэг тийм л хүүхэд байсан юм шиг байгаа юм.',
    '"Домог" чуулгын тоглолт Монгол иргэдэд зориулагджээ',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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## Evaluation

### Metrics

#### Semantic Similarity

* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.9672     |
| **spearman_cosine** | **0.9652** |

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## Training Details

### Training Dataset

#### csv

* Dataset: csv
* Size: 11,113 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                          | sentence2                                                                          | score                                                             |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | float                                                             |
  | details | <ul><li>min: 5 tokens</li><li>mean: 19.59 tokens</li><li>max: 116 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 19.86 tokens</li><li>max: 104 tokens</li></ul> | <ul><li>min: -0.07</li><li>mean: 0.49</li><li>max: 0.98</li></ul> |
* Samples:
  | sentence1                                                                                                                                      | sentence2                                                                                                                                                                                                                | score                           |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------|
  | <code>"Гамлет" жүжиг УДЭТ-д тоглогдоно</code>                                                                                                  | <code>"Скапений дамшиглал" жүжиг УДЭТ-д тоглогдоно.</code>                                                                                                                                                               | <code>0.7848628163337708</code> |
  | <code>Киноны эхэнд нөхөртэйгээ дөнгөж танилцаж байх үедээ М.Тетчэр “Би нөхрийнхөө сүүдэр дор амьдарч, аяга угаахын төлөө төрсөн хүн биш</code> | <code>Харин киноны төгсгөлд нас барсан нөхрийгөө амьд мэтээр төсөөлж, түүнтэй үргэлж ярилцан ганцаардмал байдлаасаа ангижрахыг оролддог настай эмэгтэй цайны аягаа өөрөө угаачихаад цааш явж байгааг харуулсан юм</code> | <code>0.5108565092086792</code> |
  | <code>Арга хэмжээний нээлтээр тоглолт болно</code>                                                                                             | <code>Нээлтийн арга хэмжээ нь тоглолт юм</code>                                                                                                                                                                          | <code>0.8344829082489014</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Evaluation Dataset

#### csv

* Dataset: csv
* Size: 11,113 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                          | sentence2                                                                          | score                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | float                                                            |
  | details | <ul><li>min: 5 tokens</li><li>mean: 20.22 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 20.11 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: -0.11</li><li>mean: 0.49</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                                                  | sentence2                                                                                                                                                                     | score                           |
  |:-----------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------|
  | <code>Гиннессийн амжилтад бүртгүүлсэн байна</code>                                                         | <code>Швед улсад очиж тоглох гэнэ.</code>                                                                                                                                     | <code>0.3108136057853699</code> |
  | <code>PLAYTIME 2014 наадам нь Улаанбаатар хотын орчин үеийн хөгжмийн соёлыг хөгжүүлэхэд чиглэгдэнэ.</code> | <code>PLAYTIME 2014 наадам нь залууст амралт чөлөөт цагаа цэвэр агаарт өнгөрүүлэх боломжийг олгоно.</code>                                                                    | <code>0.577198326587677</code>  |
  | <code>Альфа артист-аар тодорсон дуучин олон шагналын эзэн болно</code>                                     | <code>Альфа артист-аар тодорсон нэг дуучин ирэх гуравдугаар сард Хонконгод болох Бруно Марсын тоглолтыг үзэх клип хийлгэх гэх зэрэг олон шагналын эзэн болох юм байна.</code> | <code>0.6577209830284119</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates

#### 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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 1.0
- `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`: 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}
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:-----------------------:|
| 0      | 0    | -             | -               | 1.0000                  |
| 0.1799 | 100  | 0.0045        | -               | -                       |
| 0.3597 | 200  | 0.006         | -               | -                       |
| 0.5396 | 300  | 0.006         | -               | -                       |
| 0.7194 | 400  | 0.005         | -               | -                       |
| 0.8993 | 500  | 0.0047        | 0.0030          | 0.9652                  |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- 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",
}
```

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