---
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)
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- csv
### 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]
```
## Evaluation
### Metrics
#### Semantic Similarity
* Datasets: `dev-t` and `test-t`
* Evaluated with [EmbeddingSimilarityEvaluator](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** |
## Training Details
### Training Dataset
#### csv
* Dataset: csv
* Size: 77,201 training samples
* Columns: sentence1, sentence2, and score
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------|
| type | string | string | float |
| details | - min: 3 tokens
- mean: 16.02 tokens
- max: 96 tokens
| - min: 3 tokens
- mean: 14.66 tokens
- max: 87 tokens
| - min: -0.14
- mean: 0.63
- max: 1.0
|
* Samples:
| sentence1 | sentence2 | score |
|:---------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|:--------------------------------|
| Маргааш мэдээлэл өгнө | Хэвлэлийн хурал болно. | 0.5448001623153687 |
| Дотоод аудитын шалгалтаар 2012-2013 оны үйл ажиллагаанд 16 зөрчил илэрлээ | “Монголын Хөрөнгийн Бирж” ТӨХК-ийн Төлөөлөн удирдах зөвлөл болон Гүйцэтгэх удирдлагад 13 зөвлөмж өгөгдсөн байна. | 0.4059729874134063 |
| "хохирогчид ажлын байраар хангагдана" | "ажил олддог болно." | 0.6021140813827515 |
* Loss: [CosineSimilarityLoss](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: sentence1, sentence2, and score
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------|
| type | string | string | float |
| details | - min: 3 tokens
- mean: 16.53 tokens
- max: 85 tokens
| - min: 3 tokens
- mean: 14.68 tokens
- max: 83 tokens
| - min: -0.04
- mean: 0.62
- max: 1.0
|
* Samples:
| sentence1 | sentence2 | score |
|:---------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------|:--------------------------------|
| Ченжүүд мэдээллийн сүлжээтэй лут холбогдсон байх юм | "Энд ноолуурын үнэ асуусан хэдэн нөхөд яваад байна" гээд хэлчихсэн бололтой юм | 0.3234536349773407 |
| Хий дэлбэрэлт гарсан тухай мэдээлэл байна уу? | Мэдээлэл цуглуулж байна. | 0.3009476661682129 |
| "Энэ нь хэн нэгнээр дамжуулж биш өөрөө сонгоно гэсэн утгатай. | Өөрөө сонгоно гэсэн утгатай." | 0.770484447479248 |
* Loss: [CosineSimilarityLoss](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
Click to expand
- `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