This is a sentence-transformers model trained. 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.

Model Details

Model Description

Model Type: Sentence Transformer

Maximum Sequence Length: 128 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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False, 'architecture': '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("josangho99/ko-paraphrase-multilingual-MiniLM-L12-v2-multiTask-Fin")
# Run inference
sentences = [
    '연차주주총회는 이 투자회사의 등록사무소나 총회 소집 통지서에 기재되는 룩셈부르크의 다른 장소에서 개최됩니다.',
    '연차주주총회는 이 투자회사의 등록사무소나 총회 소집 통지서에 기재되는 룩셈부르크의 다른 장소에서 개최됩니다.',
    '② 국제거래에 대해서는 「소득세법」 제41조와 「법인세법」 제52조를 적용하지 아니한다.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 1.0000, 0.0730],
#         [1.0000, 1.0000, 0.0730],
#         [0.0730, 0.0730, 1.0000]])

Evaluation

Metrics

Semantic Similarity

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.9873
spearman_cosine	0.8671
pearson_euclidean	0.975
spearman_euclidean	0.8667
pearson_manhattan	0.9749
spearman_manhattan	0.8667
pearson_dot	0.9289
spearman_dot	0.8659
pearson_max	0.9873
spearman_max	0.8671

Semantic Similarity

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.9874
spearman_cosine	0.8672
pearson_euclidean	0.9752
spearman_euclidean	0.867
pearson_manhattan	0.975
spearman_manhattan	0.867
pearson_dot	0.9293
spearman_dot	0.866
pearson_max	0.9874
spearman_max	0.8672

Training Details

Training Dataset

Unnamed Dataset

Size: 203,584 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 15 tokens
mean: 47.4 tokens
max: 128 tokens
min: 14 tokens
mean: 45.62 tokens
max: 128 tokens

Samples:

sentence_0	sentence_1
`기존 패러다임의 한계를 극복하고 새로운 도 에 응하기 해 국가발 패러다임을 성장과 복지가 함께 가는 동반성장 략으로 환할 필요가 있다.`	`기존 패러다임의 한계를 극복하고 국가발 패러다임을 성장과 복지가 함께 가는 동반성장 전략으로 환할 필요가 있다.`
`회사는 계속기록법에 따라 기록한 재고자산의 수량을 실지 재고조사에 의하여 확정하고, 그 금액은 개별법에 의하여 평가하고 있습니다.`	`회사는 계속기록법에 따라 기록한 재고자산의 수량을 실지 재고조사에 의하여 확정하고, 그 금액은 개별법에 의하여 평가하고 있습니다.`
`금융당국은 2014년 몇 차례 공청회를 개최하여 각계의 의견 수렴을 바탕으로 K-NCR 제도 개선의 방향과 의지를 강하게 표명하고 있다.`	`금융당국은 2014년 몇 차례 공청회를 개최하여 각계의 의견 수렴을 바탕으로 K-NCR 제도 개선의 방향과 의지를 강하게 표명하고 있다.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
num_train_epochs: 2
batch_sampler: no_duplicates
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: 64
per_device_eval_batch_size: 64
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: 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}
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
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: 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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	spearman_cosine
0.1000	318	-	0.8672
0.1572	500	0.0011	-
0.1999	636	-	0.8672
0.2999	954	-	0.8671
0.3144	1000	0.0015	-
0.3999	1272	-	0.8672
0.4715	1500	0.0012	-
0.4998	1590	-	0.8672
0.5998	1908	-	0.8672
0.6287	2000	0.0009	-
0.6998	2226	-	0.8671
0.7859	2500	0.0011	-
0.7997	2544	-	0.8672
0.8997	2862	-	0.8672
0.9431	3000	0.0004	-
0.9997	3180	-	0.8671
1.0	3181	-	0.8671
1.0997	3498	-	0.8672
1.1003	3500	0.0009	-
1.1996	3816	-	0.8672
1.2575	4000	0.0008	-
1.2996	4134	-	0.8671
1.3996	4452	-	0.8671
1.4146	4500	0.0007	-
1.4995	4770	-	0.8671
1.5718	5000	0.0003	-
1.5995	5088	-	0.8671
1.6995	5406	-	0.8671
1.7290	5500	0.0002	-
1.7994	5724	-	0.8671
1.8862	6000	0.0003	-
1.8994	6042	-	0.8672
1.9994	6360	-	0.8672
2.0	6362	-	0.8671
-1	-1	-	0.8672

Framework Versions

Python: 3.12.11
Sentence Transformers: 5.1.0
Transformers: 4.56.1
PyTorch: 2.8.0+cu126
Accelerate: 1.10.1
Datasets: 4.0.0
Tokenizers: 0.22.0

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
type	string	string
details	min: 15 tokens mean: 47.4 tokens max: 128 tokens	min: 14 tokens mean: 45.62 tokens max: 128 tokens