Tibetan–English Cross-Lingual Sentence Similarity

This model scores the semantic similarity between a Tibetan sentence and an English sentence. Give it any Tibetan passage and any English passage, and it returns a number between –1 and 1: values near 1 mean the two passages express the same idea; values near 0 mean they are unrelated; negative values indicate contrasting meanings.

The model was developed to support work with Tibetan Buddhist literature. As large volumes of Tibetan text are digitized and translated into English, there is a practical need to align passages across parallel texts.

It is useful for researchers, translators, and Buddhist scholars working with Tibetan–English text pairs, as well as developers building text-alignment tools.

Intended Use

The primary use case is to score the similarity between a Tibetan input and an English input:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("khyentsevision/minilm-bo-en-sim")

tibetan = "ཆོས་ཉིད་རྣམ་པར་མ་རྟོག་པའི་ཡེ་ཤེས་སུ་འདོད་པ་ལ།"
english = "This is the meaning of perceiving the true reality."

embeddings = model.encode([tibetan, english])
similarity = model.similarity(embeddings[0], embeddings[1])
print(f"Similarity score: {similarity.item():.4f}")
# ~0.71 → strong semantic match
# ~0.0  → unrelated
# negative → contrasting meanings

To rank multiple English candidates against a single Tibetan source (e.g. to compare translation options):

candidates = [
    "This is the meaning of perceiving the true reality.",
    "Make the Secret Mantra teachings prosper and flourish!",
]

source_emb = model.encode([tibetan])
cand_embs  = model.encode(candidates)
scores = model.similarity(source_emb, cand_embs)
print(scores)  # higher score = better semantic match

Model Details

Model Description

Model Type: Sentence Transformer
Base model: sentence-transformers/all-MiniLM-L6-v2
Architecture: BERT-based transformer encoder with mean pooling
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity

Model Sources

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

Evaluation

Metrics

Semantic Similarity

Evaluated on held-out Tibetan–English pairs spanning high, low, and near-zero similarity ranges.

Metric	Score
Pearson Correlation	0.9391

Training Details

Training Dataset

Size: 3,400,000 training samples
Columns: Tibetan (bo), English (en), and similarity score derived from monolingual comparisons
Loss: CosineSimilarityLoss with MSELoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
learning_rate: 2e-05
warmup_ratio: 0.1
fp16: True
auto_find_batch_size: True

All Hyperparameters

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
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: 3
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: 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
bf16: False
fp16: True
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
hub_revision: None
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
include_tokens_per_second: False
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Framework Versions

Python: 3.11.9
Sentence Transformers: 5.2.0
Transformers: 4.57.3
PyTorch: 2.9.1+cu128
Accelerate: 1.12.0
Datasets: 4.4.2
Tokenizers: 0.22.1

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",
}