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("panagoa/LaBSE-kbd-v0.2")
# Run inference
sentences = [
    "We've never seen Tom this angry before.",
    "Tom'u daha önce asla bu kadar öfkeli görmedik.",
    'Soyez attentive aux voleurs à la tire.',
]
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]

Evaluation

Metrics

Semantic Similarity

Dataset: validation
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	-0.28
spearman_cosine	-0.3212

Training Details

Training Dataset

Unnamed Dataset

Size: 3,395,988 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: 5 tokens
mean: 10.33 tokens
max: 50 tokens
min: 5 tokens
mean: 13.81 tokens
max: 46 tokens
min: 0.0
mean: 0.36
max: 0.98

Samples:

sentence_0	sentence_1	label
`Почему вас это удивило?`	`Сыт ар щIывгъэщIэгъуар?`	`0.9298050403594972`
`Ребёнка кто-нибудь видел?`	`Quelqu'un a-t-il vu l'enfant ?`	`0.0`
`Marie se couchait.`	`Мэри гъуэлъырт.`	`0.9330472946166992`

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: 16
per_device_eval_batch_size: 16
num_train_epochs: 2
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: 16
per_device_eval_batch_size: 16
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: 1.0
num_train_epochs: 2
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: 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: False
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: round_robin

Training Logs

Click to expand

Epoch	Step	Training Loss	validation_spearman_cosine
0.0005	100	-	-0.7761
0.0009	200	-	-0.7598
0.0014	300	-	-0.7485
0.0019	400	-	-0.7412
0.0024	500	0.2864	-0.7354
0.0028	600	-	-0.7307
0.0033	700	-	-0.7191
0.0038	800	-	-0.7206
0.0042	900	-	-0.7197
0.0047	1000	0.0463	-0.7037
0.0052	1100	-	-0.6866
0.0057	1200	-	-0.6798
0.0061	1300	-	-0.6844
0.0066	1400	-	-0.6716
0.0071	1500	0.0184	-0.6658
0.0075	1600	-	-0.6620
0.0080	1700	-	-0.6532
0.0085	1800	-	-0.6455
0.0090	1900	-	-0.6452
0.0094	2000	0.011	-0.6360
0.0099	2100	-	-0.6240
0.0104	2200	-	-0.6220
0.0108	2300	-	-0.6294
0.0113	2400	-	-0.6038
0.0118	2500	0.0092	-0.6116
0.0122	2600	-	-0.5996
0.0127	2700	-	-0.6120
0.0132	2800	-	-0.5940
0.0137	2900	-	-0.5848
0.0141	3000	0.0071	-0.5958
0.0146	3100	-	-0.5840
0.0151	3200	-	-0.5944
0.0155	3300	-	-0.5895
0.0160	3400	-	-0.5849
0.0165	3500	0.0056	-0.5708
0.0005	100	-	-0.5686
0.0009	200	-	-0.5608
0.0014	300	-	-0.5587
0.0024	500	0.0053	-
0.0047	1000	0.0081	-0.5882
0.0071	1500	0.0058	-
0.0094	2000	0.0064	-0.5127
0.0118	2500	0.004	-
0.0141	3000	0.0042	-0.4934
0.0165	3500	0.0048	-
0.0188	4000	0.0036	-0.4762
0.0212	4500	0.0051	-
0.0236	5000	0.0054	-0.4754
0.0259	5500	0.0054	-
0.0283	6000	0.0054	-0.4609
0.0306	6500	0.0044	-
0.0330	7000	0.0048	-0.4716
0.0353	7500	0.0061	-
0.0377	8000	0.0018	-0.4293
0.0400	8500	0.0047	-
0.0424	9000	0.0043	-0.4311
0.0448	9500	0.0034	-
0.0471	10000	0.0041	-0.4429
0.0495	10500	0.0028	-
0.0518	11000	0.0032	-0.4324
0.0542	11500	0.0025	-
0.0565	12000	0.0037	-0.4374
0.0589	12500	0.003	-
0.0612	13000	0.005	-0.4522
0.0636	13500	0.0051	-
0.0660	14000	0.0048	-0.3994
0.0683	14500	0.0034	-
0.0707	15000	0.0032	-0.4148
0.0730	15500	0.0046	-
0.0754	16000	0.0026	-0.3848
0.0777	16500	0.0036	-
0.0801	17000	0.0051	-0.3845
0.0824	17500	0.0031	-
0.0848	18000	0.0035	-0.3500
0.0872	18500	0.0028	-
0.0895	19000	0.0021	-0.3634
0.0919	19500	0.0025	-
0.0942	20000	0.0023	-0.3428
0.0966	20500	0.0042	-
0.0989	21000	0.0038	-0.3432
0.1013	21500	0.005	-
0.1037	22000	0.0024	-0.3515
0.1060	22500	0.0029	-
0.1084	23000	0.0033	-0.3929
0.1107	23500	0.003	-
0.1131	24000	0.0029	-0.3309
0.1154	24500	0.0038	-
0.1178	25000	0.0028	-0.3369
0.1201	25500	0.0025	-
0.1225	26000	0.002	-0.3257
0.1249	26500	0.0025	-
0.1272	27000	0.0033	-0.3659
0.1296	27500	0.0023	-
0.1319	28000	0.0031	-0.3208
0.1343	28500	0.0027	-
0.1366	29000	0.0031	-0.3298
0.1390	29500	0.0047	-
0.1413	30000	0.003	-0.3460
0.1437	30500	0.004	-
0.1461	31000	0.0027	-0.3567
0.1484	31500	0.0063	-
0.1508	32000	0.003	-0.3382
0.1531	32500	0.0022	-
0.1555	33000	0.0048	-0.3475
0.1578	33500	0.0021	-
0.1602	34000	0.0043	-0.3323
0.1625	34500	0.0031	-
0.1649	35000	0.0024	-0.3207
0.1673	35500	0.0029	-
0.1696	36000	0.0032	-0.3004
0.1720	36500	0.0046	-
0.1743	37000	0.0033	-0.3085
0.1767	37500	0.002	-
0.1790	38000	0.0022	-0.3270
0.1814	38500	0.0036	-
0.1837	39000	0.0034	-0.3042
0.1861	39500	0.0034	-
0.1885	40000	0.0016	-0.3193
0.1908	40500	0.0026	-
0.1932	41000	0.0028	-0.2945
0.1955	41500	0.0031	-
0.1979	42000	0.0016	-0.2942
0.2002	42500	0.0021	-
0.2026	43000	0.003	-0.2998
0.2049	43500	0.0042	-
0.2073	44000	0.0023	-0.3245
0.2097	44500	0.0018	-
0.2120	45000	0.0021	-0.3212

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.48.3
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 3.3.2
Tokenizers: 0.21.0

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

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	sentence_0	sentence_1	label
type	string	string	float
details	min: 5 tokens mean: 10.33 tokens max: 50 tokens	min: 5 tokens mean: 13.81 tokens max: 46 tokens	min: 0.0 mean: 0.36 max: 0.98