SentenceTransformer

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

Metric	Value
negative_mse	-0.1737

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

	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

	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

Epoch	Step	Training Loss	Validation Loss	stsb-dev_negative_mse
0	0	-	-	-7.179603
0.0051	500	0.0546	-	-
0.0101	1000	0.0348	-	-
0.0152	1500	0.0169	-	-
0.0202	2000	0.0087	-	-
0.0253	2500	0.0055	-	-
0.0304	3000	0.0041	-	-
0.0354	3500	0.0036	-	-
0.0405	4000	0.0033	-	-
0.0456	4500	0.003	-	-
0.0506	5000	0.0029	-	-
0.0557	5500	0.0028	-	-
0.0607	6000	0.0027	-	-
0.0658	6500	0.0027	-	-
0.0709	7000	0.0026	-	-
0.0759	7500	0.0025	-	-
0.0810	8000	0.0025	-	-
0.0861	8500	0.0025	-	-
0.0911	9000	0.0025	-	-
0.0962	9500	0.0025	-	-
0.1012	10000	0.0024	-	-
0.1063	10500	0.0024	-	-
0.1114	11000	0.0024	-	-
0.1164	11500	0.0024	-	-
0.1215	12000	0.0024	-	-
0.1265	12500	0.0024	-	-
0.1316	13000	0.0024	-	-
0.1367	13500	0.0024	-	-
0.1417	14000	0.0024	-	-
0.1468	14500	0.0024	-	-
0.1519	15000	0.0024	-	-
0.1569	15500	0.0024	-	-
0.1620	16000	0.0024	-	-
0.1670	16500	0.0024	-	-
0.1721	17000	0.0024	-	-
0.1772	17500	0.0024	-	-
0.1822	18000	0.0024	-	-
0.1873	18500	0.0024	-	-
0.1924	19000	0.0024	-	-
0.1974	19500	0.0024	-	-
0.2025	20000	0.0024	-	-
0.2075	20500	0.0024	-	-
0.2126	21000	0.0024	-	-
0.2177	21500	0.0024	-	-
0.2227	22000	0.0024	-	-
0.2278	22500	0.0024	-	-
0.2329	23000	0.0024	-	-
0.2379	23500	0.0024	-	-
0.2430	24000	0.0023	-	-
0.2480	24500	0.0024	-	-
0.2531	25000	0.0024	-	-
0.2582	25500	0.0023	-	-
0.2632	26000	0.0024	-	-
0.2683	26500	0.0024	-	-
0.2733	27000	0.0023	-	-
0.2784	27500	0.0023	-	-
0.2835	28000	0.0023	-	-
0.2885	28500	0.0023	-	-
0.2936	29000	0.0023	-	-
0.2987	29500	0.0023	-	-
0.3037	30000	0.0023	-	-
0.3088	30500	0.0023	-	-
0.3138	31000	0.0023	-	-
0.3189	31500	0.0023	-	-
0.3240	32000	0.0023	-	-
0.3290	32500	0.0023	-	-
0.3341	33000	0.0023	-	-
0.3392	33500	0.0023	-	-
0.3442	34000	0.0023	-	-
0.3493	34500	0.0023	-	-
0.3543	35000	0.0023	-	-
0.3594	35500	0.0023	-	-
0.3645	36000	0.0023	-	-
0.3695	36500	0.0023	-	-
0.3746	37000	0.0023	-	-
0.3796	37500	0.0023	-	-
0.3847	38000	0.0023	-	-
0.3898	38500	0.0023	-	-
0.3948	39000	0.0023	-	-
0.3999	39500	0.0023	-	-
0.4050	40000	0.0023	-	-
0.4100	40500	0.0023	-	-
0.4151	41000	0.0023	-	-
0.4201	41500	0.0023	-	-
0.4252	42000	0.0023	-	-
0.4303	42500	0.0023	-	-
0.4353	43000	0.0023	-	-
0.4404	43500	0.0023	-	-
0.4455	44000	0.0022	-	-
0.4505	44500	0.0023	-	-
0.4556	45000	0.0023	-	-
0.4606	45500	0.0022	-	-
0.4657	46000	0.0022	-	-
0.4708	46500	0.0022	-	-
0.4758	47000	0.0022	-	-
0.4809	47500	0.0022	-	-
0.4859	48000	0.0022	-	-
0.4910	48500	0.0022	-	-
0.4961	49000	0.0022	-	-
0.5011	49500	0.0022	-	-
0.5062	50000	0.0022	-	-
0.5113	50500	0.0022	-	-
0.5163	51000	0.0022	-	-
0.5214	51500	0.0022	-	-
0.5264	52000	0.0022	-	-
0.5315	52500	0.0022	-	-
0.5366	53000	0.0022	-	-
0.5416	53500	0.0022	-	-
0.5467	54000	0.0022	-	-
0.5518	54500	0.0022	-	-
0.5568	55000	0.0022	-	-
0.5619	55500	0.0022	-	-
0.5669	56000	0.0022	-	-
0.5720	56500	0.0022	-	-
0.5771	57000	0.0022	-	-
0.5821	57500	0.0022	-	-
0.5872	58000	0.0022	-	-
0.5922	58500	0.0022	-	-
0.5973	59000	0.0022	-	-
0.6024	59500	0.0022	-	-
0.6074	60000	0.0022	-	-
0.6125	60500	0.0022	-	-
0.6176	61000	0.0022	-	-
0.6226	61500	0.0022	-	-
0.6277	62000	0.0022	-	-
0.6327	62500	0.0022	-	-
0.6378	63000	0.0022	-	-
0.6429	63500	0.0022	-	-
0.6479	64000	0.0022	-	-
0.6530	64500	0.0022	-	-
0.6581	65000	0.0022	-	-
0.6631	65500	0.0022	-	-
0.6682	66000	0.0022	-	-
0.6732	66500	0.0021	-	-
0.6783	67000	0.0021	-	-
0.6834	67500	0.0021	-	-
0.6884	68000	0.0021	-	-
0.6935	68500	0.0021	-	-
0.6986	69000	0.0021	-	-
0.7036	69500	0.0021	-	-
0.7087	70000	0.0021	-	-
0.7137	70500	0.0021	-	-
0.7188	71000	0.0021	-	-
0.7239	71500	0.0021	-	-
0.7289	72000	0.0021	-	-
0.7340	72500	0.0021	-	-
0.7390	73000	0.0021	-	-
0.7441	73500	0.0021	-	-
0.7492	74000	0.0021	-	-
0.7542	74500	0.0021	-	-
0.7593	75000	0.0021	-	-
0.7644	75500	0.0021	-	-
0.7694	76000	0.0021	-	-
0.7745	76500	0.0021	-	-
0.7795	77000	0.0021	-	-
0.7846	77500	0.0021	-	-
0.7897	78000	0.0021	-	-
0.7947	78500	0.0021	-	-
0.7998	79000	0.0021	-	-
0.8049	79500	0.0021	-	-
0.8099	80000	0.0021	-	-
0.8150	80500	0.0021	-	-
0.8200	81000	0.0021	-	-
0.8251	81500	0.0021	-	-
0.8302	82000	0.0021	-	-
0.8352	82500	0.0021	-	-
0.8403	83000	0.0021	-	-
0.8453	83500	0.0021	-	-
0.8504	84000	0.0021	-	-
0.8555	84500	0.0021	-	-
0.8605	85000	0.0021	-	-
0.8656	85500	0.0021	-	-
0.8707	86000	0.0021	-	-
0.8757	86500	0.0021	-	-
0.8808	87000	0.0021	-	-
0.8858	87500	0.0021	-	-
0.8909	88000	0.0021	-	-
0.8960	88500	0.0021	-	-
0.9010	89000	0.0021	-	-
0.9061	89500	0.0021	-	-
0.9112	90000	0.0021	-	-
0.9162	90500	0.002	-	-
0.9213	91000	0.0021	-	-
0.9263	91500	0.0021	-	-
0.9314	92000	0.0021	-	-
0.9365	92500	0.0021	-	-
0.9415	93000	0.002	-	-
0.9466	93500	0.002	-	-
0.9516	94000	0.0021	-	-
0.9567	94500	0.002	-	-
0.9618	95000	0.002	-	-
0.9668	95500	0.002	-	-
0.9719	96000	0.002	-	-
0.9770	96500	0.002	-	-
0.9820	97000	0.002	-	-
0.9871	97500	0.002	-	-
0.9921	98000	0.002	-	-
0.9972	98500	0.002	-	-
1.0	98776	-	0.0022	-0.1987867
1.0023	99000	0.002	-	-
0.0051	500	0.002	-	-
0.0101	1000	0.002	-	-
0.0152	1500	0.002	-	-
0.0202	2000	0.002	-	-
0.0253	2500	0.002	-	-
0.0304	3000	0.002	-	-
0.0354	3500	0.002	-	-
0.0405	4000	0.002	-	-
0.0456	4500	0.002	-	-
0.0506	5000	0.002	-	-
0.0557	5500	0.002	-	-
0.0607	6000	0.002	-	-
0.0658	6500	0.002	-	-
0.0709	7000	0.002	-	-
0.0759	7500	0.002	-	-
0.0810	8000	0.002	-	-
0.0861	8500	0.002	-	-
0.0911	9000	0.002	-	-
0.0962	9500	0.002	-	-
0.1012	10000	0.002	-	-
0.1063	10500	0.002	-	-
0.1114	11000	0.002	-	-
0.1164	11500	0.002	-	-
0.1215	12000	0.002	-	-
0.1265	12500	0.002	-	-
0.1316	13000	0.002	-	-
0.1367	13500	0.002	-	-
0.1417	14000	0.002	-	-
0.1468	14500	0.002	-	-
0.1519	15000	0.002	-	-
0.1569	15500	0.002	-	-
0.1620	16000	0.002	-	-
0.1670	16500	0.002	-	-
0.1721	17000	0.002	-	-
0.1772	17500	0.002	-	-
0.1822	18000	0.002	-	-
0.1873	18500	0.002	-	-
0.1924	19000	0.002	-	-
0.1974	19500	0.002	-	-
0.2025	20000	0.002	-	-
0.2075	20500	0.002	-	-
0.2126	21000	0.002	-	-
0.2177	21500	0.002	-	-
0.2227	22000	0.002	-	-
0.2278	22500	0.002	-	-
0.2329	23000	0.002	-	-
0.2379	23500	0.002	-	-
0.2430	24000	0.002	-	-
0.2480	24500	0.002	-	-
0.2531	25000	0.002	-	-
0.2582	25500	0.002	-	-
0.2632	26000	0.002	-	-
0.2683	26500	0.002	-	-
0.2733	27000	0.002	-	-
0.2784	27500	0.002	-	-
0.2835	28000	0.002	-	-
0.2885	28500	0.002	-	-
0.2936	29000	0.002	-	-
0.2987	29500	0.002	-	-
0.3037	30000	0.002	-	-
0.3088	30500	0.002	-	-
0.3138	31000	0.002	-	-
0.3189	31500	0.002	-	-
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1.0	98776	-	0.0021	-0.17975432
0.0051	500	0.0018	-	-
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1.0	98776	-	0.0021	-0.17605598
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0.1012	10000	0.0018	-	-
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0.4050	40000	0.0018	-	-
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0.4201	41500	0.0018	-	-
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0.4556	45000	0.0018	-	-
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0.6024	59500	0.0018	-	-
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0.6125	60500	0.0017	-	-
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0.6226	61500	0.0017	-	-
0.6277	62000	0.0018	-	-
0.6327	62500	0.0018	-	-
0.6378	63000	0.0018	-	-
0.6429	63500	0.0018	-	-
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0.6530	64500	0.0017	-	-
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0.6631	65500	0.0017	-	-
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0.7188	71000	0.0017	-	-
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0.7340	72500	0.0017	-	-
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0.7542	74500	0.0017	-	-
0.7593	75000	0.0017	-	-
0.7644	75500	0.0017	-	-
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0.7745	76500	0.0017	-	-
0.7795	77000	0.0017	-	-
0.7846	77500	0.0017	-	-
0.7897	78000	0.0017	-	-
0.7947	78500	0.0017	-	-
0.7998	79000	0.0017	-	-
0.8049	79500	0.0017	-	-
0.8099	80000	0.0017	-	-
0.8150	80500	0.0017	-	-
0.8200	81000	0.0017	-	-
0.8251	81500	0.0017	-	-
0.8302	82000	0.0017	-	-
0.8352	82500	0.0018	-	-
0.8403	83000	0.0017	-	-
0.8453	83500	0.0017	-	-
0.8504	84000	0.0017	-	-
0.8555	84500	0.0017	-	-
0.8605	85000	0.0017	-	-
0.8656	85500	0.0017	-	-
0.8707	86000	0.0017	-	-
0.8757	86500	0.0017	-	-
0.8808	87000	0.0017	-	-
0.8858	87500	0.0017	-	-
0.8909	88000	0.0017	-	-
0.8960	88500	0.0017	-	-
0.9010	89000	0.0017	-	-
0.9061	89500	0.0017	-	-
0.9112	90000	0.0017	-	-
0.9162	90500	0.0017	-	-
0.9213	91000	0.0017	-	-
0.9263	91500	0.0017	-	-
0.9314	92000	0.0017	-	-
0.9365	92500	0.0017	-	-
0.9415	93000	0.0017	-	-
0.9466	93500	0.0017	-	-
0.9516	94000	0.0017	-	-
0.9567	94500	0.0017	-	-
0.9618	95000	0.0017	-	-
0.9668	95500	0.0017	-	-
0.9719	96000	0.0017	-	-
0.9770	96500	0.0017	-	-
0.9820	97000	0.0017	-	-
0.9871	97500	0.0017	-	-
0.9921	98000	0.0017	-	-
0.9972	98500	0.0017	-	-
1.0	98776	-	0.0021	-0.17373772

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",
}

Downloads last month: 16

Safetensors

Model size

22.7M params

Tensor type

F32

Model tree for billingsmoore/minilm-bo

Base model

sentence-transformers/all-MiniLM-L6-v2

Finetuned

(756)

this model

Papers for billingsmoore/minilm-bo

Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation

Paper • 2004.09813 • Published Apr 21, 2020

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Paper • 1908.10084 • Published Aug 27, 2019 • 12

Evaluation results

Negative Mse on stsb dev
self-reported

-0.174