Paraphrase Mongolian Minilm Mn V2

🚀 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.

✨ Features

• Tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:77201, loss:CosineSimilarityLoss
• Widget examples are provided to demonstrate sentence similarity comparisons.

📦 Installation

First install the Sentence Transformers library:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

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]

📚 Documentation

Model Details

Model Description

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})
)

Evaluation

Metrics - Semantic Similarity

• Datasets: dev-t and test-t
• Evaluated with EmbeddingSimilarityEvaluator

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 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

• 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

Training Logs

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",
}