SentenceTransformer based on sentence-transformers/LaBSE

This is a sentence-transformers model finetuned from sentence-transformers/LaBSE. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.

Model was fine-tuned Ruska Romani on Russian language pairs. Ruska Romani is the dialect of Romani language attributed to Ruska Roma, the largest subgroup of Romani people in Russia.

Model was trained and evaluated on parallel pairs data data from the Russian National Corpus.

The data curation process is described in the article The Parallel Corpus of Russian and Ruska Romani Languages. Please refer to that paper in any publications where the model was used.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: sentence-transformers/LaBSE
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'cls', 'include_prompt': True})
  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh', 'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'})
  (3): Normalize({})
)

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("midwestcyr/labse-ruskaromani")
# Run inference
sentences = [
    'Владимир разорвал их, не читая.',
    'Владимиро розрискирдя лэн на гины.',
    'Мандэ кэ ёв сыс баро уважэниё и ёв ман дрэван камья.',
]
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, 0.8380, 0.0814],
#         [0.8380, 1.0000, 0.1824],
#         [0.0814, 0.1824, 1.0000]])

Evaluation

Metrics

Translation

• Dataset: ruska-roma-validation
• Evaluated with TranslationEvaluator

Training Details

Training Dataset

Unnamed Dataset

• Size: 7,278 training samples
• Columns: sentence_0, sentence_1, and label
• Approximate statistics based on the first 100 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  modality	text	text
  details	min: 5 tokensmean: 23.74 tokensmax: 96 tokens	min: 6 tokensmean: 33.36 tokensmax: 135 tokens	min: 1.0mean: 1.0max: 1.0

• Samples:

  sentence_0	sentence_1	label
  Не бойтесь, государь милостив, я буду просить его. Он нас не обидит. Мы все его дети. А как ему за вас будет заступиться, если вы станете бунтовать и разбойничать».	На дарэнте, тагари куч, мэ лава тэ мангав лэс — ёв амэн на помэкэла — амэ сарэ лэскирэ чявэ — а сыр лэскэ пал тумэндэ тэ затэрдёл, коли тумэ лэна тэ газдэн бунто и разбоё.	1.0
  Можно было видеть, что мужчина высок и стоит у весла, широко расставив ноги, вполоборота к кругленькой, маленькой женщине, прислонившейся грудью к другому веслу, саженях в полутора от первого.	Могискирдо сыс тэ роздыкхэс, со гаджё учё и тэрдо пашо вёсло, буґлэс росчеви ґэра, дро паш обрисибэ кэ крэнглинько джювлы, сави припасия колынэса кэ вавир вёсло, надур екх екхэстыр.	1.0
  Мазурка кончилась, хозяева просили гостей к ужину, но полковник Б. отказался, сказав, что ему надо завтра рано вставать, и простился с хозяевами.	Мазурка кончисалыя, хулая мангнэ гостен ко хабэ, нэ полковнико Б. отпхэндяпэ и пхэндя, со лэскэ трэбинэ атася злокоса тэ уштэс и простиндяпэ хуланца.	1.0

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• fp16: True
• per_device_eval_batch_size: 16
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

• per_device_train_batch_size: 16
• num_train_epochs: 3
• max_steps: -1
• learning_rate: 5e-05
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_steps: 0
• optim: adamw_torch_fused
• optim_args: None
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• optim_target_modules: None
• gradient_accumulation_steps: 1
• average_tokens_across_devices: True
• max_grad_norm: 1
• label_smoothing_factor: 0.0
• bf16: False
• fp16: True
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• use_liger_kernel: False
• liger_kernel_config: None
• use_cache: False
• neftune_noise_alpha: None
• torch_empty_cache_steps: None
• auto_find_batch_size: False
• log_on_each_node: True
• logging_nan_inf_filter: True
• include_num_input_tokens_seen: no
• log_level: passive
• log_level_replica: warning
• disable_tqdm: False
• project: huggingface
• trackio_space_id: None
• trackio_bucket_id: None
• trackio_static_space_id: None
• per_device_eval_batch_size: 16
• prediction_loss_only: True
• eval_on_start: False
• eval_do_concat_batches: True
• eval_use_gather_object: False
• eval_accumulation_steps: None
• include_for_metrics: []
• batch_eval_metrics: False
• save_only_model: False
• save_on_each_node: False
• enable_jit_checkpoint: False
• push_to_hub: False
• hub_private_repo: None
• hub_model_id: None
• hub_strategy: every_save
• hub_always_push: False
• hub_revision: None
• load_best_model_at_end: False
• ignore_data_skip: False
• restore_callback_states_from_checkpoint: False
• full_determinism: False
• seed: 42
• data_seed: None
• use_cpu: False
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• parallelism_config: None
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• dataloader_prefetch_factor: None
• remove_unused_columns: True
• label_names: None
• train_sampling_strategy: random
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• ddp_static_graph: None
• ddp_backend: None
• ddp_timeout: 1800
• fsdp: None
• fsdp_config: None
• deepspeed: None
• debug: []
• skip_memory_metrics: True
• do_predict: False
• resume_from_checkpoint: None
• warmup_ratio: None
• local_rank: -1
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Training Time

• Training: 11.7 minutes

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 5.6.0
• Transformers: 5.12.1
• PyTorch: 2.11.0+cu128
• Accelerate: 1.14.0
• Datasets: 4.0.0
• Tokenizers: 0.22.2

Citation

BibTeX

Ruska Romani and Russian Parallel Corpus

@inproceedings{koncha-etal-2024-parallel,
    title = "The Parallel Corpus of {R}ussian and Ruska {R}omani Languages",
    author = "Koncha, Kirill  and
      Kukanova, Abina  and
      Tatiana, Kazakova  and
      Rozovskaya, Gloria",
    editor = "Serikov, Oleg  and
      Voloshina, Ekaterina  and
      Postnikova, Anna  and
      Muradoglu, Saliha  and
      Le Ferrand, Eric  and
      Klyachko, Elena  and
      Vylomova, Ekaterina  and
      Shavrina, Tatiana  and
      Tyers, Francis",
    booktitle = "Proceedings of the Third Workshop on NLP Applications to Field Linguistics",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.fieldmatters-1.1/",
    doi = "10.18653/v1/2024.fieldmatters-1.1",
    pages = "1--5"
}

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{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}