metadata

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 model finetuned from 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
Maximum Sequence Length: 128 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- csv

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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

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

	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 with these parameters:

{
    "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

	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 with these parameters:

{
    "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

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

@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",
}