SentenceTransformer

Name: e5-dhivehi-qa-mnr
Availability: InStock

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.

Stage	Task	Model	Dataset	Objective
0	Base	Multilingual Base	`intfloat/multilingual-e5-base`	—
1	Paraphrase Identification (MNR)	`alakxender/e5-dhivehi-paws-mnr`	`alakxender/dhivehi-paws-labeled` > label=1 Only	MultipleNegativesRankingLoss
2	Paraphrase Identification (Cosine)	`alakxender/e5-dhivehi-paws-cos`	`alakxender/dhivehi-paws-labeled`	CosineSimilarityLoss
3	Question → Passage Matching	`alakxender/e5-dhivehi-qa-mnr`	`alakxender/dhivehi-qa-dataset`	MultipleNegativesRankingLoss
4	News Title → Content	`alakxender/e5-dhivehi-articles-mnr`	`alakxender/dhivehi-news-corpus`	MultipleNegativesRankingLoss
5	Summary → Content	`alakxender/e5-dhivehi-summaries-mnr`	`alakxender/dv-en-parallel-corpus-clean`, `alakxender/dv-summary-translation-corpus`	MultipleNegativesRankingLoss

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.

python

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:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

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

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
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
num_train_epochs: 3
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: False
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
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: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.2166	500	1.2589
0.4333	1000	1.0569
0.6499	1500	1.0562
0.8666	2000	0.9848
1.0832	2500	0.9565
1.2998	3000	0.9604
1.5165	3500	0.958
1.7331	4000	0.9232
1.9497	4500	0.9202
2.1664	5000	0.8813
2.3830	5500	0.893
2.5997	6000	0.8324
2.8163	6500	0.7792

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

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",
}

MultipleNegativesRankingLoss

bibtex

@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}
}

	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