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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:77201
- loss:CosineSimilarityLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
- source_sentence: '"17 тэрбумын хэрэгт холбогдсон хүмүүсийг шалгаж байна."'
sentences:
- Шинэ сайд томилогдлоо."
- '"Авлига авсан хүмүүсийг шалгаж байна."'
- Шүүхийг засварлах мөнгө байхгүй байна."
- source_sentence: '"Гэмт хэрэг үйлдсэн. "'
sentences:
- LIKE дар.
- Саусгоби сэндс компанийн хэргээр мөрдөн байцаалт явагдаж байна."
- '"Гэмтэл учруулсан."'
- source_sentence: '"Иргэдийн хүсэлтийг шинэчлэлийн Засгийн газар хэрэгжүүлнэ."'
sentences:
- '"Засгийн газар иргэдийн хүсэлтийг хэрэгжүүлэх бодолтой байна."'
- '"Ц.Болд албан тушаалаа ашиглан төсвөөс мөнгө завшсан байна."'
- Шүүх хараат бус байх ёстой."
- source_sentence: '"Ам.долларын ханш суларснаас бэрхшээл үүсэж байна."'
sentences:
- '"тушаал"'
- Шүүхийн шийдвэрийн талаарх судалгаа хийнэ."
- '"Валютын ханшийн өөрчлөлтөөс болж бэрхшээл гарч байна."'
- source_sentence: '"Сэтгүүлч анд маань хоёр дахь номоо хэвлэлтээс гаргажээ"'
sentences:
- БНХАУ-ын аж үйлдвэрлэлийн үйлдвэрлэлт буурсан.
- Жастин Бибер, Кэти Перри нарын элэглэл хамгийн түрүүнд дүрслэгдэх аж.
- '"Л.Болормаагийн хоёр дахь ном “Завгүй” хэмээн нэрийджээ."'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: dev t
type: dev-t
metrics:
- type: pearson_cosine
value: 0.9547459589724314
name: Pearson Cosine
- type: spearman_cosine
value: 0.9538075641510714
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: test t
type: test-t
metrics:
- type: pearson_cosine
value: 0.956384303059334
name: Pearson Cosine
- type: spearman_cosine
value: 0.9566981709702497
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-mongolian-minilm-mn_v2")
# Run inference
sentences = [
'"Сэтгүүлч анд маань хоёр дахь номоо хэвлэлтээс гаргажээ"',
'"Л.Болормаагийн хоёр дахь ном “Завгүй” хэмээн нэрийджээ."',
'БНХАУ-ын аж үйлдвэрлэлийн үйлдвэрлэлт буурсан.',
]
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
* Datasets: `dev-t` and `test-t`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | dev-t | test-t |
|:--------------------|:-----------|:-----------|
| pearson_cosine | 0.9547 | 0.9564 |
| **spearman_cosine** | **0.9538** | **0.9567** |
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## Training Details
### Training Dataset
#### csv
* Dataset: csv
* Size: 77,201 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: 3 tokens</li><li>mean: 16.02 tokens</li><li>max: 96 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 14.66 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: -0.14</li><li>mean: 0.63</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:---------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|:--------------------------------|
| <code>Маргааш мэдээлэл өгнө</code> | <code>Хэвлэлийн хурал болно.</code> | <code>0.5448001623153687</code> |
| <code>Дотоод аудитын шалгалтаар 2012-2013 оны үйл ажиллагаанд 16 зөрчил илэрлээ</code> | <code>“Монголын Хөрөнгийн Бирж” ТӨХК-ийн Төлөөлөн удирдах зөвлөл болон Гүйцэтгэх удирдлагад 13 зөвлөмж өгөгдсөн байна.</code> | <code>0.4059729874134063</code> |
| <code>"хохирогчид ажлын байраар хангагдана"</code> | <code>"ажил олддог болно."</code> | <code>0.6021140813827515</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: 77,201 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: 3 tokens</li><li>mean: 16.53 tokens</li><li>max: 85 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 14.68 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: -0.04</li><li>mean: 0.62</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:---------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------|:--------------------------------|
| <code>Ченжүүд мэдээллийн сүлжээтэй лут холбогдсон байх юм</code> | <code>"Энд ноолуурын үнэ асуусан хэдэн нөхөд яваад байна" гээд хэлчихсэн бололтой юм</code> | <code>0.3234536349773407</code> |
| <code>Хий дэлбэрэлт гарсан тухай мэдээлэл байна уу?</code> | <code>Мэдээлэл цуглуулж байна.</code> | <code>0.3009476661682129</code> |
| <code>"Энэ нь хэн нэгнээр дамжуулж биш өөрөө сонгоно гэсэн утгатай.</code> | <code>Өөрөө сонгоно гэсэн утгатай."</code> | <code>0.770484447479248</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`: 5
- `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`: 5
- `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 | dev-t_spearman_cosine | test-t_spearman_cosine |
|:------:|:-----:|:-------------:|:---------------:|:---------------------:|:----------------------:|
| 0 | 0 | - | - | 1.0000 | - |
| 0.1727 | 500 | 0.0046 | - | - | - |
| 0.3454 | 1000 | 0.0054 | 0.0042 | 0.9549 | - |
| 0.5181 | 1500 | 0.0069 | - | - | - |
| 0.6908 | 2000 | 0.008 | 0.0067 | 0.9298 | - |
| 0.8636 | 2500 | 0.0076 | - | - | - |
| 1.0363 | 3000 | 0.0075 | 0.0065 | 0.9317 | - |
| 1.2090 | 3500 | 0.0069 | - | - | - |
| 1.3817 | 4000 | 0.0063 | 0.0063 | 0.9366 | - |
| 1.5544 | 4500 | 0.0055 | - | - | - |
| 1.7271 | 5000 | 0.0049 | 0.0057 | 0.9411 | - |
| 1.8998 | 5500 | 0.0045 | - | - | - |
| 2.0725 | 6000 | 0.0045 | 0.0056 | 0.9405 | - |
| 2.2453 | 6500 | 0.004 | - | - | - |
| 2.4180 | 7000 | 0.0038 | 0.0053 | 0.9432 | - |
| 2.5907 | 7500 | 0.0034 | - | - | - |
| 2.7634 | 8000 | 0.0032 | 0.0053 | 0.9448 | - |
| 2.9361 | 8500 | 0.0029 | - | - | - |
| 3.1088 | 9000 | 0.0028 | 0.0051 | 0.9459 | - |
| 3.2815 | 9500 | 0.0025 | - | - | - |
| 3.4542 | 10000 | 0.0023 | 0.0047 | 0.9498 | - |
| 3.6269 | 10500 | 0.0022 | - | - | - |
| 3.7997 | 11000 | 0.0021 | 0.0046 | 0.9510 | - |
| 3.9724 | 11500 | 0.0019 | - | - | - |
| 4.1451 | 12000 | 0.0019 | 0.0046 | 0.9525 | - |
| 4.3178 | 12500 | 0.0016 | - | - | - |
| 4.4905 | 13000 | 0.0016 | 0.0045 | 0.9528 | - |
| 4.6632 | 13500 | 0.0014 | - | - | - |
| 4.8359 | 14000 | 0.0013 | 0.0044 | 0.9538 | - |
| 5.0 | 14475 | - | - | - | 0.9567 |
### 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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