billingsmoore/minilm-bo

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Note: This model has been superseded by khyentsevision/minilm-bo-en-sim, a further-finetuned sentence-similarity version of this model.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 <!-- - Base model: Unknown -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 384 dimensions
• —Similarity Function: Cosine Similarity
• —Training Dataset:
• —aggregated-bo-en <!-- - Language: Unknown --> <!-- - License: Unknown -->

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]

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Knowledge Distillation

• —Dataset: stsb-dev
• —Evaluated with <code>MSEEvaluator</code>

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Training Details

Training Dataset

aggregated-bo-en

• —Dataset: aggregated-bo-en
• —Size: 878,004 training samples
• —Columns: <code>tibetan</code> and <code>label</code>
• —Approximate statistics based on the first 1000 samples: | | tibetan | label | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------| | type | string | list | | details | <ul><li>min: 4 tokens</li><li>mean: 29.06 tokens</li><li>max: 373 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
• —Samples: | tibetan | label | |:------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------| | <code>ཀི་ལོ་མི་ཊར་ ༤༧.༣༩</code> | <code>[-0.026894396170973778, 0.07161899656057358, -0.06451261788606644, 0.004668479785323143, -0.13893075287342072, ...]</code> | | <code>ཅ། ཁྱོད་དང་ང་།</code> | <code>[-0.03711550310254097, 0.04723873734474182, 0.027722617611289024, 0.03208618983626366, 0.0021679026540368795, ...]</code> | | <code>མཚོན་རྨ་གསོ་བ། དེ་བས་མང་། >></code> | <code>[0.016887372359633446, -0.004544022027403116, -0.000849854841362685, -0.046510301530361176, -0.05679721385240555, ...]</code> |
• —Loss: <code>MSELoss</code>

Evaluation Dataset

aggregated-bo-en

• —Dataset: aggregated-bo-en
• —Size: 878,004 evaluation samples
• —Columns: <code>english</code>, <code>tibetan</code>, and <code>label</code>
• —Approximate statistics based on the first 1000 samples: | | english | tibetan | label | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------| | type | string | string | list | | details | <ul><li>min: 3 tokens</li><li>mean: 22.2 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 32.42 tokens</li><li>max: 487 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
• —Samples: | english | tibetan | label | |:-----------------------------------------------------------------------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------| | <code>East TN Children's Hospital.</code> | <code>ཤར་གངས་ཕྲུག་གི་གསས་ཁང་།</code> | <code>[-0.05563941225409508, 0.09337888658046722, 0.01915512979030609, 0.02351493015885353, -0.09008331596851349, ...]</code> | | <code>In this prayer, often called the "high priestly prayer of</code> | <code>སྡེ་ཚན་འདིའི་ནང་དུ་མང་། " མཁན་ཆེན་ཞི་བ་འཚོ། ཇོ་བོ་རྗེ་དཔལ་ལྡན་ཨ་ཏི་ཤ "</code> | <code>[0.033027056604623795, 0.013109864667057991, -0.051157161593437195, -0.07704736292362213, -0.04368748143315315, ...]</code> | | <code>Spoilers: Oh, I don't know.</code> | <code>ལ་མེད། ཤེས་ཀྱི་མེད། 아니오, 모르겠습니다.</code> | <code>[0.008215248584747314, -0.02530045434832573, -0.029446149244904518, 0.04361790046095848, 0.05075978860259056, ...]</code> |
• —Loss: <code>MSELoss</code>

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

<details><summary>Click to expand</summary>

• —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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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

</details>

Training Logs

<details><summary>Click to expand</summary>

</details>

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",
}

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