SentenceTransformer
This is a sentence-transformers model trained on the train_set dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
Model Details
- Learning other languages besides Chinese and English is insufficient, so additional learning is needed to optimize use of other languages.
- This model is additionally trained on the Korean dataset.
Model Description
- Model Type: Sentence Transformer Transformer Encoder
- Maximum Sequence Length: 8192 tokens
- Output Dimensionality: 1024 tokens
- 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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, '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): 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.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("dragonkue/bge-m3-ko")
# Run inference
sentences = [
'수급권자 중 근로 능력이 없는 임산부는 몇 종에 해당하니?',
'내년부터 저소득층 1세 미만 아동의 \n의료비 부담이 더 낮아진다!\n의료급여제도 개요\n□ (목적) 생활유지 능력이 없거나 생활이 어려운 국민들에게 발생하는 질병, 부상, 출산 등에 대해 국가가 의료서비스 제공\n□ (지원대상) 국민기초생활보장 수급권자, 타 법에 의한 수급권자 등\n\n| 구분 | 국민기초생활보장법에 의한 수급권자 | 국민기초생활보장법 이외의 타 법에 의한 수급권자 |\n| --- | --- | --- |\n| 1종 | ○ 국민기초생활보장 수급권자 중 근로능력이 없는 자만으로 구성된 가구 - 18세 미만, 65세 이상 - 4급 이내 장애인 - 임산부, 병역의무이행자 등 | ○ 이재민(재해구호법) ○ 의상자 및 의사자의 유족○ 국내 입양된 18세 미만 아동○ 국가유공자 및 그 유족․가족○ 국가무형문화재 보유자 및 그 가족○ 새터민(북한이탈주민)과 그 가족○ 5․18 민주화운동 관련자 및 그 유가족○ 노숙인 ※ 행려환자 (의료급여법 시행령) |\n| 2종 | ○ 국민기초생활보장 수급권자 중 근로능력이 있는 가구 | - |\n',
'문재인 대통령의 국정수행 지지율과 더불어민주당의 지지율이 2주 연속 상승했다는 여론조사 결과가 나왔다. 한미정상회담의 효과로 풀이된다. \n한국갤럽은 25~27일 전국 만 18세 이상 1,003명에게 문 대통령의 직무수행 평가를 조사한 결과(표본오차 95% 신뢰수준에 ±3.1%포인트), 37%가 긍정 평가했다고 28일 밝혔다. \n긍정 평가는 지난주보다 3%포인트 올랐다. 부정 평가는 52%로 지난주보다 6%포인트 떨어졌다. 최근 60%대를 넘나들던 부정 평가가 50%대 초반으로 하락했다. 10%는 의견을 유보했다. \n지역별로 보면 서울과 인천·경기의 긍정 평가가 각각 37%란 점이 눈에 띈다. 부산·울산·경남도 33%로, 대구·경북(25%)과 달리 30%대로 나타났다. 연령별로는 40대가 49%로 가장 높았고, 18~29세는 31%로 집계됐다. 정치적 이념·성향이 중도라고 한 응답자의 34%는 긍정 평가했다. \n◇긍정 평가 이유 ‘외교·국제 관계’ 26%P 상승 \n문 대통령의 지지율 상승은 한미정상회담 성과가 영향을 미친 것으로 보인다. 긍정 평가 이유로는 ‘외교·국제 관계’가 가장 높았다. 30%로 지난주보다 26%포인트나 올랐다. 15개월간 ‘신종 코로나바이러스 감염증(코로나19) 대처’가 1위였는데, 한미정상회담 이후 외교·국제 관계로 순위가 바뀌었다. \n다음으로 ‘코로나19 대처’ 22%, ‘최선을 다함·열심히 한다’ 6%, ‘북한 관계’ 4%, ‘전반적으로 잘한다’ 4% 순이었다. \n부정 평가 이유로는 ‘부동산 정책’이 29%로 가장 높았다. 다음으로 ‘경제·민생 문제 해결 부족’ 10%, ‘코로나19 대처 미흡’ 5%, ‘공정하지 못함·내로남불’ 5%, ‘인사 문제’ 4% 순이었다. \n민주당 지지율 역시 문 대통령 지지율과 마찬가지로 2주 연속 상승했다. 정당 지지도 조사에서 민주당은 34%로 지난주보다 2%포인트 올랐다. \n국민의힘은 27%로 지난주보다 1%포인트 올랐다. 민주당과 국민의힘의 지지율 격차는 7%포인트로, 오차범위 밖을 벗어났다. 다음으로 정의당 5%, 열린민주당 3%, 국민의당 3% 순이었다. 무당층은 27%로 조사됐다. \n※자세한 내용은 한국갤럽 또는 중앙선거여론조사심의위원회 홈페이지를 참조하면 된다.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
- ndcg, mrr, map metrics are metrics that consider ranking, while accuracy, precision, and recall are metrics that do not consider ranking. (Example: When considering ranking for retrieval top 10, different scores are given when the correct document is in 1st place and when it is in 10th place. However, accuracy, precision, and recall scores are the same if they are in the top 10.)
Information Retrieval
- Korean Embedding Benchmark is a benchmark with a relatively long 3/4 quantile of string length of 1024
Korean Embedding Benchmark with AutoRAG
This is a benchmark of Korean embedding models. (https://github.com/Marker-Inc-Korea/AutoRAG-example-korean-embedding-benchmark)
- Top-k 1
Model name | F1 | Recall | Precision | mAP | mRR | NDCG |
---|---|---|---|---|---|---|
paraphrase-multilingual-mpnet-base-v2 | 0.3596 | 0.3596 | 0.3596 | 0.3596 | 0.3596 | 0.3596 |
KoSimCSE-roberta | 0.4298 | 0.4298 | 0.4298 | 0.4298 | 0.4298 | 0.4298 |
Cohere embed-multilingual-v3.0 | 0.3596 | 0.3596 | 0.3596 | 0.3596 | 0.3596 | 0.3596 |
openai ada 002 | 0.4737 | 0.4737 | 0.4737 | 0.4737 | 0.4737 | 0.4737 |
multilingual-e5-large-instruct | 0.4649 | 0.4649 | 0.4649 | 0.4649 | 0.4649 | 0.4649 |
Upstage Embedding | 0.6579 | 0.6579 | 0.6579 | 0.6579 | 0.6579 | 0.6579 |
paraphrase-multilingual-MiniLM-L12-v2 | 0.2982 | 0.2982 | 0.2982 | 0.2982 | 0.2982 | 0.2982 |
openai_embed_3_small | 0.5439 | 0.5439 | 0.5439 | 0.5439 | 0.5439 | 0.5439 |
ko-sroberta-multitask | 0.4211 | 0.4211 | 0.4211 | 0.4211 | 0.4211 | 0.4211 |
openai_embed_3_large | 0.6053 | 0.6053 | 0.6053 | 0.6053 | 0.6053 | 0.6053 |
KU-HIAI-ONTHEIT-large-v1 | 0.7105 | 0.7105 | 0.7105 | 0.7105 | 0.7105 | 0.7105 |
KU-HIAI-ONTHEIT-large-v1.1 | 0.7193 | 0.7193 | 0.7193 | 0.7193 | 0.7193 | 0.7193 |
kf-deberta-multitask | 0.4561 | 0.4561 | 0.4561 | 0.4561 | 0.4561 | 0.4561 |
gte-multilingual-base | 0.5877 | 0.5877 | 0.5877 | 0.5877 | 0.5877 | 0.5877 |
BGE-m3 | 0.6578 | 0.6578 | 0.6578 | 0.6578 | 0.6578 | 0.6578 |
BGE-m3-ko | 0.7456 | 0.7456 | 0.7456 | 0.7456 | 0.7456 | 0.7456 |
- Top-k 3
Model name | F1 | Recall | Precision | mAP | mRR | NDCG |
---|---|---|---|---|---|---|
paraphrase-multilingual-mpnet-base-v2 | 0.2368 | 0.4737 | 0.1579 | 0.2032 | 0.2032 | 0.2712 |
KoSimCSE-roberta | 0.3026 | 0.6053 | 0.2018 | 0.2661 | 0.2661 | 0.3515 |
Cohere embed-multilingual-v3.0 | 0.2851 | 0.5702 | 0.1901 | 0.2515 | 0.2515 | 0.3321 |
openai ada 002 | 0.3553 | 0.7105 | 0.2368 | 0.3202 | 0.3202 | 0.4186 |
multilingual-e5-large-instruct | 0.3333 | 0.6667 | 0.2222 | 0.2909 | 0.2909 | 0.3856 |
Upstage Embedding | 0.4211 | 0.8421 | 0.2807 | 0.3509 | 0.3509 | 0.4743 |
paraphrase-multilingual-MiniLM-L12-v2 | 0.2061 | 0.4123 | 0.1374 | 0.1740 | 0.1740 | 0.2340 |
openai_embed_3_small | 0.3640 | 0.7281 | 0.2427 | 0.3026 | 0.3026 | 0.4097 |
ko-sroberta-multitask | 0.2939 | 0.5877 | 0.1959 | 0.2500 | 0.2500 | 0.3351 |
openai_embed_3_large | 0.3947 | 0.7895 | 0.2632 | 0.3348 | 0.3348 | 0.4491 |
KU-HIAI-ONTHEIT-large-v1 | 0.4386 | 0.8772 | 0.2924 | 0.3421 | 0.3421 | 0.4766 |
KU-HIAI-ONTHEIT-large-v1.1 | 0.4430 | 0.8860 | 0.2953 | 0.3406 | 0.3406 | 0.4778 |
kf-deberta-multitask | 0.3158 | 0.6316 | 0.2105 | 0.2792 | 0.2792 | 0.3679 |
gte-multilingual-base | 0.4035 | 0.8070 | 0.2690 | 0.3450 | 0.3450 | 0.4614 |
BGE-m3 | 0.4254 | 0.8508 | 0.2836 | 0.3421 | 0.3421 | 0.4701 |
BGE-m3-ko | 0.4517 | 0.9035 | 0.3011 | 0.3494 | 0.3494 | 0.4886 |
- Top-k 5
Model name | F1 | Recall | Precision | mAP | mRR | NDCG |
---|---|---|---|---|---|---|
paraphrase-multilingual-mpnet-base-v2 | 0.1813 | 0.5439 | 0.1088 | 0.1575 | 0.1575 | 0.2491 |
KoSimCSE-roberta | 0.2164 | 0.6491 | 0.1298 | 0.1751 | 0.1751 | 0.2873 |
Cohere embed-multilingual-v3.0 | 0.2076 | 0.6228 | 0.1246 | 0.1640 | 0.1640 | 0.2731 |
openai ada 002 | 0.2602 | 0.7807 | 0.1561 | 0.2139 | 0.2139 | 0.3486 |
multilingual-e5-large-instruct | 0.2544 | 0.7632 | 0.1526 | 0.2194 | 0.2194 | 0.3487 |
Upstage Embedding | 0.2982 | 0.8947 | 0.1789 | 0.2237 | 0.2237 | 0.3822 |
paraphrase-multilingual-MiniLM-L12-v2 | 0.1637 | 0.4912 | 0.0982 | 0.1437 | 0.1437 | 0.2264 |
openai_embed_3_small | 0.2690 | 0.8070 | 0.1614 | 0.2148 | 0.2148 | 0.3553 |
ko-sroberta-multitask | 0.2164 | 0.6491 | 0.1298 | 0.1697 | 0.1697 | 0.2835 |
openai_embed_3_large | 0.2807 | 0.8421 | 0.1684 | 0.2088 | 0.2088 | 0.3586 |
KU-HIAI-ONTHEIT-large-v1 | 0.3041 | 0.9123 | 0.1825 | 0.2137 | 0.2137 | 0.3783 |
KU-HIAI-ONTHEIT-large-v1.1 | 0.3099 | 0.9298 | 0.1860 | 0.2148 | 0.2148 | 0.3834 |
kf-deberta-multitask | 0.2281 | 0.6842 | 0.1368 | 0.1724 | 0.1724 | 0.2939 |
gte-multilingual-base | 0.2865 | 0.8596 | 0.1719 | 0.2096 | 0.2096 | 0.3637 |
BGE-m3 | 0.4254 | 0.8508 | 0.2836 | 0.3421 | 0.3421 | 0.4701 |
BGE-m3-ko | 0.3099 | 0.9298 | 0.1860 | 0.2098 | 0.2098 | 0.3793 |
- Top-k 10
Model name | F1 | Recall | Precision | mAP | mRR | NDCG |
---|---|---|---|---|---|---|
paraphrase-multilingual-mpnet-base-v2 | 0.1212 | 0.6667 | 0.0667 | 0.1197 | 0.1197 | 0.2382 |
KoSimCSE-roberta | 0.1324 | 0.7281 | 0.0728 | 0.1080 | 0.1080 | 0.2411 |
Cohere embed-multilingual-v3.0 | 0.1324 | 0.7281 | 0.0728 | 0.1150 | 0.1150 | 0.2473 |
openai ada 002 | 0.1563 | 0.8596 | 0.0860 | 0.1051 | 0.1051 | 0.2673 |
multilingual-e5-large-instruct | 0.1483 | 0.8158 | 0.0816 | 0.0980 | 0.0980 | 0.2520 |
Upstage Embedding | 0.1707 | 0.9386 | 0.0939 | 0.1078 | 0.1078 | 0.2848 |
paraphrase-multilingual-MiniLM-L12-v2 | 0.1053 | 0.5789 | 0.0579 | 0.0961 | 0.0961 | 0.2006 |
openai_embed_3_small | 0.1547 | 0.8509 | 0.0851 | 0.0984 | 0.0984 | 0.2593 |
ko-sroberta-multitask | 0.1276 | 0.7018 | 0.0702 | 0.0986 | 0.0986 | 0.2275 |
openai_embed_3_large | 0.1643 | 0.9035 | 0.0904 | 0.1180 | 0.1180 | 0.2855 |
KU-HIAI-ONTHEIT-large-v1 | 0.1707 | 0.9386 | 0.0939 | 0.1105 | 0.1105 | 0.2860 |
KU-HIAI-ONTHEIT-large-v1.1 | 0.1722 | 0.9474 | 0.0947 | 0.1033 | 0.1033 | 0.2822 |
kf-deberta-multitask | 0.1388 | 0.7632 | 0.0763 | 0.1 | 0.1 | 0.2422 |
gte-multilingual-base | 0.1675 | 0.9211 | 0.0921 | 0.1066 | 0.1066 | 0.2805 |
BGE-m3 | 0.4254 | 0.8508 | 0.2836 | 0.3421 | 0.3421 | 0.4701 |
BGE-m3-ko | 0.1770 | 0.9736 | 0.0974 | 0.1097 | 0.1097 | 0.2932 |
Information Retrieval
- Dataset:
miracl-ko
(https://github.com/project-miracl/miracl) - miracl benchmark is a benchmark with a relatively short 3/4 quantile of string length of 220 on the Korean Wikidata set.
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6103 |
cosine_accuracy@3 | 0.8169 |
cosine_accuracy@5 | 0.8732 |
cosine_accuracy@10 | 0.9202 |
cosine_precision@1 | 0.6103 |
cosine_precision@3 | 0.3787 |
cosine_precision@5 | 0.2761 |
cosine_precision@10 | 0.1728 |
cosine_recall@1 | 0.3847 |
cosine_recall@3 | 0.5902 |
cosine_recall@5 | 0.6794 |
cosine_recall@10 | 0.7695 |
cosine_ndcg@10 | 0.6723 |
cosine_mrr@10 | 0.7262 |
cosine_map@100 | 0.6074 |
dot_accuracy@1 | 0.6103 |
dot_accuracy@3 | 0.8169 |
dot_accuracy@5 | 0.8732 |
dot_accuracy@10 | 0.9202 |
dot_precision@1 | 0.6103 |
dot_precision@3 | 0.3787 |
dot_precision@5 | 0.2761 |
dot_precision@10 | 0.1728 |
dot_recall@1 | 0.3847 |
dot_recall@3 | 0.5902 |
dot_recall@5 | 0.6794 |
dot_recall@10 | 0.7695 |
dot_ndcg@10 | 0.6723 |
dot_mrr@10 | 0.7262 |
dot_map@100 | 0.6074 |
Bias, Risks and Limitations
- Since the evaluation results are different for each domain, it is necessary to compare and evaluate the model in your own domain. In the Miracl benchmark, the evaluation was conducted using the Korean Wikipedia as a corpus, and in this case, the cosine_ndcg@10 score dropped by 0.2 points after learning. However, in the Auto-RAG benchmark, which is a financial domain, the ndcg score increased by 0.9 when it was top 1. This model may be advantageous for use in a specific domain.
- Also, since the miracl benchmark consists of a corpus of relatively short strings, while the Korean Embedding Benchmark consists of a corpus of longer strings, this model may be more advantageous if the length of the corpus you want to use is long.
Training Hyperparameters
Non-Default Hyperparameters
The batch size was referenced from the following paper: Text Embeddings by Weakly-Supervised Contrastive Pre-training (https://arxiv.org/pdf/2212.03533)
eval_strategy
: stepsper_device_train_batch_size
: 32768per_device_eval_batch_size
: 32768learning_rate
: 3e-05warmup_ratio
: 0.03333333333333333fp16
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 32768per_device_eval_batch_size
: 32768per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 3e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 3max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.03333333333333333warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Truedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
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",
}
@misc{bge-m3,
title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation},
author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},
year={2024},
eprint={2402.03216},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{wang2022text,
title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2212.03533},
year={2022}
}
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Evaluation results
- Cosine Accuracy@1 on miraclself-reported0.610
- Cosine Accuracy@3 on miraclself-reported0.817
- Cosine Accuracy@5 on miraclself-reported0.873
- Cosine Accuracy@10 on miraclself-reported0.920
- Cosine Precision@1 on miraclself-reported0.610
- Cosine Precision@3 on miraclself-reported0.379
- Cosine Precision@5 on miraclself-reported0.276
- Cosine Precision@10 on miraclself-reported0.173
- Cosine Recall@1 on miraclself-reported0.385
- Cosine Recall@3 on miraclself-reported0.590