seongil-dn/bge-m3-kor-retrieval-283

SentenceTransformer based on BAAI/bge-m3

This is a sentence-transformers model finetuned from BAAI/bge-m3. 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

Model Description

• Model Type: Sentence Transformer
• Base model: BAAI/bge-m3
• Maximum Sequence Length: 1024 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': 1024, '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("seongil-dn/bge-m3-kor-retrieval-283")
# Run inference
sentences = [
    '전남지역의 석유와 화학제품은 왜 수출이 늘어나는 경향을 보였어',
    '(2) 전남지역\n2013년중 전남지역 수출은 전년대비 1.2% 감소로 전환하였다. 품목별로는 석유(+9.3% → +3.8%) 및 화학제품(+1.2% → +7.1%)이 중국 등 해외수요확대로 증가세를 지속하였으나 철강금속(+1.8% → -8.6%)은 글로벌 공급과잉 및 중국의 저가 철강수출 확대로, 선박(+7.6% → -49.2%)은 수주물량이 급격히 줄어들면서 감소로 전환하였다. 전남지역 수입은 원유, 화학제품, 철강금속 등의 수입이 줄면서 전년대비 7.4% 감소로 전환하였다.',
    '수출 증가세 지속\n1/4분기 중 수출은 전년동기대비 증가흐름을 지속하였다. 품목별로 보면 석유제품, 석유화학, 철강, 선박, 반도체, 자동차 등 대다수 품목에서 증가하였다. 석유제품은 글로벌 경기회복에 따른 에너지 수요 증가와 국제유가 급등으로 수출단가가 높은 상승세를 지속하면서 증가하였다. 석유화학도 중국, 아세안을 중심으로 합성수지, 고무 등의 수출이 큰 폭 증가한 데다 고유가로 인한 수출가격도 동반 상승하면서 증가세를 이어갔다. 철강은 건설, 조선 등 글로벌 전방산업의 수요 증대, 원자재가격 상승 및 중국 감산 등에 따른 수출단가 상승 등에 힘입어 증가세를 이어갔다. 선박은 1/4분기 중 인도물량이 확대됨에 따라 증가하였다. 반도체는 자동차 등 전방산업의 견조한 수요가 이어지는 가운데 전년동기대비로 높은 단가가 지속되면서 증가하였다. 자동차는 차량용 반도체 수급차질이 지속되었음에도 불구하고 글로벌 경기회복 흐름에 따라 수요가 늘어나면서 전년동기대비 소폭 증가하였다. 모니터링 결과 향후 수출은 증가세가 지속될 것으로 전망되었다. 석유화학 및 석유정제는 수출단가 상승과 전방산업의 수요확대 기조가 이어지면서 증가할 전망이다. 철강은 주요국 경기회복과 중국, 인도 등의 인프라 투자 확대 등으로 양호한 흐름을 이어갈 전망이다. 반도체는 글로벌 스마트폰 수요 회복, 디지털 전환 기조 등으로 견조한 증가세를 지속할 것으로 보인다. 자동차는 차량용 반도체 공급차질이 점차 완화되고 미국, 신흥시장을 중심으로 수요회복이 본격화됨에 따라 소폭 증가할 전망이다. 선박은 친환경 선박수요 지속, 글로별 교역 신장 등에도 불구하고 2021년 2/4분기 집중되었던 인도물량의 기저효과로 인해 감소할 것으로 보인다.',
]
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]

Training Details

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• gradient_accumulation_steps: 4
• learning_rate: 3e-05
• warmup_ratio: 0.05
• fp16: True
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 8
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 4
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 3e-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: 3
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.05
• 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: True
• 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: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• 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
• eval_use_gather_object: False
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0011	1	3.7042
0.0021	2	4.4098
0.0032	3	4.5599
0.0042	4	4.5564
0.0053	5	5.3164
0.0064	6	4.9723
0.0074	7	5.2419
0.0085	8	3.6708
0.0095	9	3.4174
0.0106	10	3.7081
0.0117	11	3.5893
0.0127	12	2.8265
0.0138	13	1.8535
0.0149	14	2.2631
0.0159	15	1.6212
0.0170	16	1.3256
0.0180	17	3.1196
0.0191	18	2.6933
0.0202	19	2.7525
0.0212	20	1.8354
0.0223	21	1.5399
0.0233	22	1.2657
0.0244	23	1.5086
0.0255	24	1.4753
0.0265	25	1.4019
0.0276	26	1.0282
0.0286	27	1.1981
0.0297	28	1.1639
0.0308	29	1.064
0.0318	30	1.1106
0.0329	31	0.8862
0.0339	32	0.9067
0.0350	33	1.0234
0.0361	34	1.0057
0.0371	35	0.7404
0.0382	36	0.5796
0.0392	37	0.6
0.0403	38	0.6473
0.0414	39	0.7274
0.0424	40	0.5312
0.0435	41	0.6884
0.0446	42	0.4993
0.0456	43	0.5445
0.0467	44	0.2793
0.0477	45	0.4398
0.0488	46	0.4882
0.0499	47	0.3142
0.0509	48	0.253
0.0520	49	0.1723
0.0530	50	0.4482
0.0541	51	0.3704
0.0552	52	0.3844
0.0562	53	0.3141
0.0573	54	0.2717
0.0583	55	0.0936
0.0594	56	0.0795
0.0605	57	0.0754
0.0615	58	0.0839
0.0626	59	0.0739
0.0636	60	0.0622
0.0647	61	0.0541
0.0658	62	0.4835
0.0668	63	0.4849
0.0679	64	0.5093
0.0689	65	0.4725
0.0700	66	0.4658
0.0711	67	0.4257
0.0721	68	0.4656
0.0732	69	0.5188
0.0743	70	0.465
0.0753	71	0.5166
0.0764	72	0.4152
0.0774	73	0.4874
0.0785	74	0.435
0.0796	75	0.4698
0.0806	76	0.4075
0.0817	77	0.2881
0.0827	78	0.3375
0.0838	79	0.3183
0.0849	80	0.3046
0.0859	81	0.5192
0.0870	82	0.4832
0.0880	83	0.4467
0.0891	84	0.3109
0.0902	85	0.4108
0.0912	86	0.3034
0.0923	87	0.2636
0.0933	88	0.2169
0.0944	89	0.2991
0.0955	90	0.2901
0.0965	91	0.335
0.0976	92	0.3621
0.0986	93	0.2661
0.0997	94	0.3448
0.1008	95	0.1964
0.1018	96	0.2323
0.1029	97	0.2856
0.1040	98	0.2986
0.1050	99	0.2628
0.1061	100	0.2865
0.1071	101	0.2288
0.1082	102	0.208
0.1093	103	0.2074
0.1103	104	0.1906
0.1114	105	0.1639
0.1124	106	0.1597
0.1135	107	0.1896
0.1146	108	0.1387
0.1156	109	0.1281
0.1167	110	0.2742
0.1177	111	0.1787
0.1188	112	0.1449
0.1199	113	0.1114
0.1209	114	0.1889
0.1220	115	0.1044
0.1230	116	0.2556
0.1241	117	0.2081
0.1252	118	0.2649
0.1262	119	0.3898
0.1273	120	0.6489
0.1283	121	0.6267
0.1294	122	0.6013
0.1305	123	0.5391
0.1315	124	0.5176
0.1326	125	0.4483
0.1337	126	0.4734
0.1347	127	0.6635
0.1358	128	0.3238
0.1368	129	0.1651
0.1379	130	0.4351
0.1390	131	0.2721
0.1400	132	0.2922
0.1411	133	0.3631
0.1421	134	0.4333
0.1432	135	0.2805
0.1443	136	0.0546
0.1453	137	0.0316
0.1464	138	0.0278
0.1474	139	0.0151
0.1485	140	0.0177
0.1496	141	0.0247
0.1506	142	0.0168
0.1517	143	0.0278
0.1527	144	0.0422
0.1538	145	0.0363
0.1549	146	0.0484
0.1559	147	0.0326
0.1570	148	0.009
0.1580	149	0.0216
0.1591	150	0.005
0.1602	151	0.0514
0.1612	152	0.0131
0.1623	153	0.0145
0.1634	154	0.0246
0.1644	155	0.0111
0.1655	156	0.0184
0.1665	157	0.0168
0.1676	158	0.0055
0.1687	159	0.0091
0.1697	160	0.0363
0.1708	161	0.0039
0.1718	162	0.0119
0.1729	163	0.0284
0.1740	164	0.0055
0.1750	165	0.0193
0.1761	166	0.0138
0.1771	167	0.0099
0.1782	168	0.026
0.1793	169	0.025
0.1803	170	0.0318
0.1814	171	0.0088
0.1824	172	0.0137
0.1835	173	0.0158
0.1846	174	0.0271
0.1856	175	0.0181
0.1867	176	0.026
0.1877	177	0.0207
0.1888	178	0.009
0.1899	179	0.0117
0.1909	180	0.0265
0.1920	181	0.0151
0.1931	182	0.0254
0.1941	183	0.0101
0.1952	184	0.0096
0.1962	185	0.0225
0.1973	186	0.0122
0.1984	187	0.0184
0.1994	188	0.0326
0.2005	189	0.0163
0.2015	190	0.0257
0.2026	191	0.0126
0.2037	192	0.0121
0.2047	193	0.0251
0.2058	194	0.0145
0.2068	195	0.0244
0.2079	196	0.0196
0.2090	197	0.0121
0.2100	198	0.0145
0.2111	199	0.0084
0.2121	200	0.013
0.2132	201	0.0123
0.2143	202	0.009
0.2153	203	0.0248
0.2164	204	0.0236
0.2174	205	0.0195
0.2185	206	0.0206
0.2196	207	0.0201
0.2206	208	0.0185
0.2217	209	0.0206
0.2228	210	0.0233
0.2238	211	0.0429
0.2249	212	0.0161
0.2259	213	0.0334
0.2270	214	0.0128
0.2281	215	0.0273
0.2291	216	0.0228
0.2302	217	0.0199
0.2312	218	0.0154
0.2323	219	0.0051
0.2334	220	0.018
0.2344	221	0.0194
0.2355	222	0.0095
0.2365	223	0.0058
0.2376	224	0.0285
0.2387	225	0.0107
0.2397	226	0.0196
0.2408	227	0.0311
0.2418	228	0.0198
0.2429	229	0.0126
0.2440	230	0.0168
0.2450	231	0.0069
0.2461	232	0.0112
0.2471	233	0.0133
0.2482	234	0.0234
0.2493	235	0.0174
0.2503	236	0.0133
0.2514	237	0.0068
0.2525	238	0.0213
0.2535	239	0.0197
0.2546	240	0.011
0.2556	241	0.0226
0.2567	242	0.0305
0.2578	243	0.0198
0.2588	244	0.0318
0.2599	245	0.024
0.2609	246	0.0349
0.2620	247	0.1405
0.2631	248	0.1075
0.2641	249	0.1303
0.2652	250	0.1108
0.2662	251	0.0913
0.2673	252	0.081
0.2684	253	0.0516
0.2694	254	0.082
0.2705	255	0.0558
0.2715	256	0.05
0.2726	257	0.0829
0.2737	258	0.1127
0.2747	259	0.0559
0.2758	260	0.1117
0.2768	261	0.06
0.2779	262	0.0525
0.2790	263	0.0488
0.2800	264	0.0403
0.2811	265	0.0978
0.2822	266	0.0404
0.2832	267	0.0481
0.2843	268	0.0357
0.2853	269	0.0327
0.2864	270	0.0615
0.2875	271	0.0662
0.2885	272	0.0546
0.2896	273	0.0523
0.2906	274	0.0436
0.2917	275	0.0509
0.2928	276	0.0279
0.2938	277	0.0405
0.2949	278	0.0608
0.2959	279	0.0223
0.2970	280	0.0103
0.2981	281	0.0432
0.2991	282	0.0491
0.3002	283	0.0237

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.2.1
• Transformers: 4.44.2
• PyTorch: 2.3.1+cu121
• Accelerate: 1.1.1
• Datasets: 2.21.0
• Tokenizers: 0.19.1

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",
}