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: Эр цэргийн баярыг тохиолдуулан тоглолт болно
  sentences:
  - Тоглолт эрчүүдэд зориулагдсан.
  - Бие даасан тоглолт болно.
  - Уг тоглолтыг Атлантад төвтэй хүмүүнлэгийн байгууллагаас зохион байгуулж байгаа
    ажээ.
- source_sentence: Энэ ном нь МУИС-ийн оюутны бүтээл юм
  sentences:
  - Тоглолтонд дэлхийн алдартай хийлч оролцоно.
  - Дөрвөн жилийн өмнө Нью-Йоркийн Филармонийн уран бүтээлчид Пхеньян хотод тоглолт
    хийж байсан.
  - Энэ ном нь утга зохиолын шүүмжлэлийн салбарт хувь нэмэр оруулжээ.
- source_sentence: “Paprika” кино нь Японы анимэйшн, шинжлэх ухаан, танин мэдэхүйн
    бүтээл юм
  sentences:
  - “Paprika” киноны найруулагчаар Satoshi Kon ажиллажээ
  - Элчин сайд Ш.Алтангэрэл арга хэмжээг зохион байгуулсан хүмүүст талархал илэрхийллээ.
  - Уран бүтээлчдийн санаачилгаар.
- source_sentence: Пэрэнлэйн Нямлхагва бол Д.Нацагдоржийн шагналт яруу найрагч юм
  sentences:
  - ',“Ньюанс” хамтлагийн тоглолт ОХУ-ын олон хотод болсон.'
  - Зохиол нь өгүүлэмж сайтай, утгын найруулгын хувьд сонгодог
  - Пэрэнлэйн Нямлхагвагийн тоглолт удахгүй болно
- source_sentence: Тэд “Зэрлэг цэцэгсийн хүлэмж” кино хийсэн
  sentences:
  - Наадам "Давалгаа" тайзан дээр болно.
  - Францын агуу дуучин эмэгтэй Эдит Пиаф.
  - Тэдний хоёр дахь бүтээл “Зүрхээр наадагч” кино 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: 0.5175384750107511
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.48776566583188496
      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-v3-mn")
# Run inference
sentences = [
    'Тэд “Зэрлэг цэцэгсийн хүлэмж” кино хийсэн',
    'Тэдний хоёр дахь бүтээл “Зүрхээр наадагч” кино юм.',
    'Францын агуу дуучин эмэгтэй Эдит Пиаф.',
]
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]
```

<!--
### 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.*
-->

## 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      | 0.5175     |
| **spearman_cosine** | **0.4878** |

<!--
## 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

#### 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: 4 tokens</li><li>mean: 14.51 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.07 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: -0.09</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                          | sentence2                                                                                                                  | score                           |
  |:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------|:--------------------------------|
  | <code>Тэднээс 6 нь л ажиллагаатай юм</code>                        | <code>,Sothеbey’s-ийн зарсан төхөөрөмж ажилладаг гэж Sothеbey’s-ийн сайтан дээрх барааны танилцуулгад бичсэн байна.</code> | <code>0.0607873052358627</code> |
  | <code>Түүний хит дуу нь "Миллион алых розe"</code>                 | <code>Тэрбээр "Миллион алых розe" дууг олон хэлээр дуулсан.</code>                                                         | <code>0.7139256596565247</code> |
  | <code>“Нисванис” хамтлаг олон арга хэмжээ зохион байгуулдаг</code> | <code>Хамтлагийн тоглолт нь рок цугларалт, шоу юм</code>                                                                   | <code>0.454399824142456</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: 4 tokens</li><li>mean: 14.52 tokens</li><li>max: 95 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.92 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: -0.09</li><li>mean: 0.48</li><li>max: 0.98</li></ul> |
* Samples:
  | sentence1                                                           | sentence2                                                                                             | score                           |
  |:--------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:--------------------------------|
  | <code>Америк хөгжмийн зохиолуудыг бэлтгэл хийж байж тоглодог</code> | <code>Монгол, Америк хоёр улсын хөгжмийн нотны тэмдэглэгээ өөр.</code>                                | <code>0.5598467588424683</code> |
  | <code>Энэхүү хэсэг нь дөрвөн ангитай байна.</code>                  | <code>Зохиолч Б.Мөнхболд энэхүү хөгжмийг бичихдээ Галдан бошготийн түүхийг таван жил судласан.</code> | <code>0.2354833334684372</code> |
  | <code>Д.Шинэцог удахгүй тоглолт хийхээр төлөвлөж байна</code>       | <code>Д.Шинэцог Монголын шилдэг хөгжмийн зохиолчдын бүтээлийг тоглоно</code>                          | <code>0.3461045920848846</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    | -             | -               | 0.2082                  |
| 0.1799 | 100  | 0.0433        | -               | -                       |
| 0.3597 | 200  | 0.0367        | -               | -                       |
| 0.5396 | 300  | 0.033         | -               | -                       |
| 0.7194 | 400  | 0.0351        | -               | -                       |
| 0.8993 | 500  | 0.0312        | 0.0329          | 0.4878                  |


### 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",
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->