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 semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

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

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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
  (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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Генри Джастис Форд',
    'Форд, Генри Джастис',
    'Я вышел из ванны свеж и бодр, как будто собирался на бал.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

Size: 1,000,000 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 3 tokens
mean: 21.82 tokens
max: 127 tokens
min: 4 tokens
mean: 21.16 tokens
max: 136 tokens
min: 1.0
mean: 1.0
max: 1.0

Samples:

sentence_0	sentence_1	label
`Темех мар.`	`Дело десятое.`	`1.0`
`Уругвайӑн тĕн ĕҫченĕсем`	`Религиозные деятели Уругвая`	`1.0`
`Эп аванах ас тӑватӑп, пилӗк ҫул каялла пахчана эпир лайӑх тасатнӑччӗ.`	`А пять лет тому назад я знал, что сад был чищен.`	`1.0`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 12
per_device_eval_batch_size: 12
num_train_epochs: 1
fp16: True
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 12
per_device_eval_batch_size: 12
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: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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
use_ipex: 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}
tp_size: 0
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: False
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: 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: round_robin

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0012	100	-
0.0024	200	-
0.0036	300	-
0.0048	400	-
0.0060	500	0.5331
0.0072	600	-
0.0084	700	-
0.0096	800	-
0.0108	900	-
0.0120	1000	0.3694
0.0132	1100	-
0.0144	1200	-
0.0156	1300	-
0.0168	1400	-
0.0180	1500	0.3141
0.0192	1600	-
0.0204	1700	-
0.0216	1800	-
0.0228	1900	-
0.0240	2000	0.2836
0.0252	2100	-
0.0264	2200	-
0.0276	2300	-
0.0288	2400	-
0.0300	2500	0.2823
0.0312	2600	-
0.0324	2700	-
0.0336	2800	-
0.0348	2900	-
0.0360	3000	0.265
0.0372	3100	-
0.0384	3200	-
0.0396	3300	-
0.0408	3400	-
0.0420	3500	0.2599
0.0432	3600	-
0.0444	3700	-
0.0456	3800	-
0.0468	3900	-
0.0480	4000	0.234
0.0492	4100	-
0.0504	4200	-
0.0516	4300	-
0.0528	4400	-
0.0540	4500	0.1966
0.0552	4600	-
0.0564	4700	-
0.0576	4800	-
0.0588	4900	-
0.0600	5000	0.2204
0.0612	5100	-
0.0624	5200	-
0.0636	5300	-
0.0648	5400	-
0.0660	5500	0.2272
0.0672	5600	-
0.0684	5700	-
0.0696	5800	-
0.0708	5900	-
0.0720	6000	0.2256
0.0732	6100	-
0.0744	6200	-
0.0756	6300	-
0.0768	6400	-
0.0780	6500	0.2071
0.0792	6600	-
0.0804	6700	-
0.0816	6800	-
0.0828	6900	-
0.0840	7000	0.2113
0.0852	7100	-
0.0864	7200	-
0.0876	7300	-
0.0888	7400	-
0.0900	7500	0.2222
0.0912	7600	-
0.0924	7700	-
0.0936	7800	-
0.0948	7900	-
0.0960	8000	0.2186
0.0972	8100	-
0.0984	8200	-
0.0996	8300	-
0.1008	8400	-
0.1020	8500	0.2137
0.1032	8600	-
0.1044	8700	-
0.1056	8800	-
0.1068	8900	-
0.1080	9000	0.1928
0.1092	9100	-
0.1104	9200	-
0.1116	9300	-
0.1128	9400	-
0.1140	9500	0.2117
0.1152	9600	-
0.1164	9700	-
0.1176	9800	-
0.1188	9900	-
0.1200	10000	0.1987
0.1212	10100	-
0.1224	10200	-
0.1236	10300	-
0.1248	10400	-
0.1260	10500	0.2011
0.1272	10600	-
0.1284	10700	-
0.1296	10800	-
0.1308	10900	-
0.1320	11000	0.1775
0.1332	11100	-
0.1344	11200	-
0.1356	11300	-
0.1368	11400	-
0.1380	11500	0.2048
0.1392	11600	-
0.1404	11700	-
0.1416	11800	-
0.1428	11900	-
0.1440	12000	0.2064
0.1452	12100	-
0.1464	12200	-
0.1476	12300	-
0.1488	12400	-
0.1500	12500	0.1883
0.1512	12600	-
0.1524	12700	-
0.1536	12800	-
0.1548	12900	-
0.1560	13000	0.2084
0.1572	13100	-
0.1584	13200	-
0.1596	13300	-
0.1608	13400	-
0.1620	13500	0.2077
0.1632	13600	-
0.1644	13700	-
0.1656	13800	-
0.1668	13900	-
0.1680	14000	0.1866
0.1692	14100	-
0.1704	14200	-
0.1716	14300	-
0.1728	14400	-
0.1740	14500	0.1859
0.1752	14600	-
0.1764	14700	-
0.1776	14800	-
0.1788	14900	-
0.1800	15000	0.1735
0.1812	15100	-
0.1824	15200	-
0.1836	15300	-
0.1848	15400	-
0.1860	15500	0.171
0.1872	15600	-
0.1884	15700	-
0.1896	15800	-
0.1908	15900	-
0.1920	16000	0.1465
0.1932	16100	-
0.1944	16200	-
0.1956	16300	-
0.1968	16400	-
0.1980	16500	0.1921
0.1992	16600	-
0.2004	16700	-
0.2016	16800	-
0.2028	16900	-
0.2040	17000	0.1669
0.2052	17100	-
0.2064	17200	-
0.2076	17300	-
0.2088	17400	-
0.2100	17500	0.1656
0.2112	17600	-
0.2124	17700	-
0.2136	17800	-
0.2148	17900	-
0.2160	18000	0.1952
0.2172	18100	-
0.2184	18200	-
0.2196	18300	-
0.2208	18400	-
0.2220	18500	0.1658
0.2232	18600	-
0.2244	18700	-
0.2256	18800	-
0.2268	18900	-
0.2280	19000	0.1774
0.2292	19100	-
0.2304	19200	-
0.2316	19300	-
0.2328	19400	-
0.2340	19500	0.1802
0.2352	19600	-
0.2364	19700	-
0.2376	19800	-
0.2388	19900	-
0.2400	20000	0.1724
0.2412	20100	-
0.2424	20200	-
0.2436	20300	-
0.2448	20400	-
0.2460	20500	0.1653
0.2472	20600	-
0.2484	20700	-
0.2496	20800	-
0.2508	20900	-
0.2520	21000	0.1484
0.2532	21100	-
0.2544	21200	-
0.2556	21300	-
0.2568	21400	-
0.2580	21500	0.1544
0.2592	21600	-
0.2604	21700	-
0.2616	21800	-
0.2628	21900	-
0.2640	22000	0.174
0.2652	22100	-
0.2664	22200	-
0.2676	22300	-
0.2688	22400	-
0.2700	22500	0.1488
0.2712	22600	-
0.2724	22700	-
0.2736	22800	-
0.2748	22900	-
0.2760	23000	0.1696
0.2772	23100	-
0.2784	23200	-
0.2796	23300	-
0.2808	23400	-
0.2820	23500	0.1468
0.2832	23600	-
0.2844	23700	-
0.2856	23800	-
0.2868	23900	-
0.2880	24000	0.1738
0.2892	24100	-
0.2904	24200	-
0.2916	24300	-
0.2928	24400	-
0.2940	24500	0.1667
0.2952	24600	-
0.2964	24700	-
0.2976	24800	-
0.2988	24900	-
0.3000	25000	0.1562
0.3012	25100	-
0.3024	25200	-
0.3036	25300	-
0.3048	25400	-
0.3060	25500	0.1628
0.3072	25600	-
0.3084	25700	-
0.3096	25800	-
0.3108	25900	-
0.3120	26000	0.1392
0.3132	26100	-
0.3144	26200	-

Framework Versions

Python: 3.12.10
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.6.0+cu124
Accelerate: 1.8.1
Datasets: 3.6.0
Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

bibtex

@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