alakxender / e5-dhivehi-qa-mnr

SentenceTransformer

This is a sentence-transformers model trained. It maps sentences & paragraphs to a 768-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
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

Fine-tuned Models

This model is part of a progressive series of sentence embedding models based on intfloat/multilingual-e5-base , fine-tuned specifically for Dhivehi language understanding.

Each stage leverages a targeted dataset to specialize the model for semantic similarity, question answering, and summarization tasks — improving performance for real-world Dhivehi NLP applications.

Each model builds upon the previous checkpoint, incrementally enhancing the semantic capabilities of the model for Dhivehi. The goal is to support high-quality sentence embeddings for a wide range of Dhivehi information retrieval and understanding tasks.

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': 256, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'word_embedding_dimension': 768, '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("alakxender/e5-dhivehi-qa-mnr")
# Run inference
sentences = [
    'query: އަކްރަމުގެ ދިރިއުޅުމަށް އައި ބަދަލުތަކަކީ ކޮބައިތޯ؟',
    'passage: މިގޮތުގެމަތިން އަކްރަމަށް ލިބުނު ޖަވާހިރުތައް ވިއްކައި، ލިބުނު ގިނަގުނަ ފައިސާއިން ޖަވާހިރުގެ ވިޔަފާރިފަށައި، ބައްޕައެކޭ އެއްފަދައިން މަޝްހޫރު ވިޔަފާރިވެރިއަކަށް ވެއްޖެއެވެ. އަދި އަކްރަމާއި ޢާއިލާގެ ދިރިއުޅުން ކުރިއެކޭވެސް އެއްފަދަ ތަނަވަސް ދިރިއުޅުމަކަށް ބަދަލުވެގެން ހިނގައްޖެއެވެ.',
    'passage: ސިވިލް ސަރވިސްގެ މުވައްޒަފުން މަޢުލޫމާތު ހާމަކުރުމުގައި ޢަމަލުކުރަންވާނީ ތިރީގައިވާ އުޞޫލުތަކުގެ މަތިންނެވެ. ޤާނޫނާ ގަވާއިދާ އެއްގޮތްވާގޮތުގެމަތިން، މަޢުލޫމާތު ދޭންޖެހޭ ކޮންމެ ޙާލަތެއްގައި ތެދު މަޢުލޫމާތު ދިނުން.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.4978, 0.4952],
#         [0.4978, 1.0000, 0.8843],
#         [0.4952, 0.8843, 1.0000]])

Training Details

Training Dataset

• Size: 9,232 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1
  type	string	string
  details	min: 9 tokens mean: 17.88 tokens max: 43 tokens	min: 9 tokens mean: 62.22 tokens max: 256 tokens

• Samples:

  sentence_0	sentence_1
  query: ދީނީ ތަގްރީރުކުރުމާއި ދީނީ ނަސޭހަތް ދިނުން ކަމަށް ބުނެފައިވަނީ ކޮން ކަމަކަށް؟	passage: "ދީނީ ތަގްރީރުކުރުމާއި ދީނީ ނަސޭހަތް ދިނުން" ކަމަށް ބުނެފައި އެވަނީ، ހަބަރުފަތުރާ ވަސީލަތަކުން ނުވަތަ ހާންމު މީހުން ޖަމާވެފައިވާ ތަނެއްގައި ދީނީ ތަގްރީރުކުރުމާއި ދީނީ ނަސޭހަތް ދިނުމަށެވެ. ދީނީ އެއްބައިވަންތަކަން ހިމާޔަތް ކުރުމުގެ ގާނޫނު (ގާނޫނު ނަންބަރު 6/1994) އަށް 1 ވަނަ އިސްލާހު ގެނައުމުގެ ގާނޫނު (ގާނޫނު ނަންބަރު 8/2014) ގެ 7 ވަނަ މާއްދާގެ (ނ) ގައި
  query: ގާނޫނީ ގޮތުން ގައިދީން ކަމަށް ބުނެފައިވަނީ ކޮން ބައެއް؟	passage: "ގައިދީ" ނުވަތަ "ގައިދީން" ކަމަށް ބުނެފައިއެވަނީ، ޖަލުގައި ތިބޭ މީހުންގެ ތެރެއިން ހުކުމެއް ތަންފީޒުކުރަމުންދާ މީހުންނާއި އަރުވާލަން ނުވަތަ ގޭބަންދަށް ކޯޓުން ހުކުމްކޮށްފައި ތިބޭ މީހުންނަށެވެ. ޖަލުތަކާއި ޕެރޯލްގެ ގާނޫނު (ގާނޫނު ނަންބަރު 14/2013) ގެ 155 ވަނަ މާއްދާގެ (ލ) ގައި
  query: އިސްލާމްދީނުގައި ޢިލްމު އުނގެނުމަކީ ކޮބައިތޯ؟	passage: ކީރިތި ރަސޫލާ އަންގަވައިފައިވަނީ، [طلب العلم فريضة على كلّ مسلم ومسلمة] [ޢިލްމު އުނގެނުމަކީ ކޮންމެ މުސްލިމް ފިރިހެނަކާއި ކޮންމެ މުސްލިމް އަންހެނެއްގެ މައްޗަށް އޮތް ވާޖިބެކޭ، ފަރުޟެކޭ.]

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size : 4
• per_device_eval_batch_size : 4
• multi_dataset_batch_sampler : round_robin

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.9.21
• Sentence Transformers: 5.0.0
• Transformers: 4.52.4
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• 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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}