SentenceTransformer

Note: This model has been superseded by khyentsevision/minilm-bo-en-sim, a further-finetuned sentence-similarity version of this model. Consider using that model instead.

This is a sentence-transformers model trained on the aggregated-bo-en 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. It is intended primarily for usage with the Tibetan language.

Model Details

Model Description

• Model Type: Sentence Transformer

• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • aggregated-bo-en

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': 512, '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("billingsmoore/minilm-bo")
# Run inference
sentences = [
    'He could do it, so he did.',
    'རེས་བྱེད་ཐུབ་པ་དེ་རེད། འོན་ཀྱང་། ཁོ་མོས་',
    'ཕྱི་སྟོང་པ་ཉིད་ཡོངས་སུ་དག་པ། ཕྱི་སྟོང་པ་ཉིད་ཡོངས་སུ་དག་པས། ཤེས་པ་པོ་ཡོངས་སུ་དག་པ་སྟེ། དེ་ལྟར་ན་ཤེས་པ་པོ་ཡོངས་སུ་དག་པ་དང་། ཕྱི་སྟོང་པ་ཉིད་ཡོངས་སུ་དག་པ་འདི་ལ་གཉིས་སུ་མྱེད་དེ་གཉིས་སུ་བྱར་མྱེད་སོ་སོ་མ་ཡིན་ཐ་མྱི་དད་དོ། །ཤེས་པ་པོ་ཡོངས་སུ་དག་པས།',
]
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

Knowledge Distillation

• Dataset: stsb-dev
• Evaluated with MSEEvaluator

Training Details

Training Dataset

aggregated-bo-en

• Dataset: aggregated-bo-en
• Size: 878,004 training samples
• Columns: tibetan and label
• Approximate statistics based on the first 1000 samples:

  	tibetan	label
  type	string	list
  details	min: 4 tokens mean: 29.06 tokens max: 373 tokens	size: 384 elements

• Samples:

  tibetan	label
  ཀི་ལོ་མི་ཊར་ ༤༧.༣༩	[-0.026894396170973778, 0.07161899656057358, -0.06451261788606644, 0.004668479785323143, -0.13893075287342072, ...]
  ཅ། ཁྱོད་དང་ང་།	[-0.03711550310254097, 0.04723873734474182, 0.027722617611289024, 0.03208618983626366, 0.0021679026540368795, ...]
  མཚོན་རྨ་གསོ་བ། དེ་བས་མང་། >>	[0.016887372359633446, -0.004544022027403116, -0.000849854841362685, -0.046510301530361176, -0.05679721385240555, ...]

• Loss: MSELoss

Evaluation Dataset

aggregated-bo-en

• Dataset: aggregated-bo-en
• Size: 878,004 evaluation samples
• Columns: english, tibetan, and label
• Approximate statistics based on the first 1000 samples:

  	english	tibetan	label
  type	string	string	list
  details	min: 3 tokens mean: 22.2 tokens max: 512 tokens	min: 4 tokens mean: 32.42 tokens max: 487 tokens	size: 384 elements

• Samples:

  english	tibetan	label
  East TN Children's Hospital.	ཤར་གངས་ཕྲུག་གི་གསས་ཁང་།	[-0.05563941225409508, 0.09337888658046722, 0.01915512979030609, 0.02351493015885353, -0.09008331596851349, ...]
  In this prayer, often called the "high priestly prayer of	སྡེ་ཚན་འདིའི་ནང་དུ་མང་། " མཁན་ཆེན་ཞི་བ་འཚོ། ཇོ་བོ་རྗེ་དཔལ་ལྡན་ཨ་ཏི་ཤ "	[0.033027056604623795, 0.013109864667057991, -0.051157161593437195, -0.07704736292362213, -0.04368748143315315, ...]
  Spoilers: Oh, I don't know.	ལ་མེད། ཤེས་ཀྱི་མེད། 아니오, 모르겠습니다.	[0.008215248584747314, -0.02530045434832573, -0.029446149244904518, 0.04361790046095848, 0.05075978860259056, ...]

• Loss: MSELoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• learning_rate: 2e-05
• num_train_epochs: 25
• warmup_ratio: 0.1
• save_safetensors: False
• auto_find_batch_size: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 8
• per_device_eval_batch_size: 8
• 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: 2e-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: 25
• 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: False
• 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: True
• 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: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Framework Versions

• Python: 3.12.3
• Sentence Transformers: 3.3.1
• Transformers: 4.48.1
• PyTorch: 2.5.1+cu124
• Accelerate: 1.2.0
• Datasets: 3.1.0
• 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",
}

MSELoss

@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}