whybe-choi/Qwen3-VL-Embedding-2B-ko-vdr-preview-v0.3

Qwen3-VL-Embedding-2B model trained on Korean Visual Document Retrieval query-document screenshot pairs

This is a sentence-transformers model finetuned from Qwen/Qwen3-VL-Embedding-2B. It maps sentences & paragraphs to a 2048-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: Qwen/Qwen3-VL-Embedding-2B
• Maximum Sequence Length: 262144 tokens
• Output Dimensionality: 2048 dimensions
• Similarity Function: Cosine Similarity
• Supported Modalities: Text, Image, Video, Message
• Language: ko
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'image': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'video': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'message': {'method': 'forward', 'method_output_name': 'last_hidden_state', 'format': 'structured'}}, 'module_output_name': 'token_embeddings', 'processing_kwargs': {'chat_template': {'add_generation_prompt': True}}, 'unpad_inputs': False, 'architecture': 'Qwen3VLModel'})
  (1): Pooling({'embedding_dimension': 2048, 'pooling_mode': 'lasttoken', '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("sentence_transformers_model_id")
# Run inference
queries = [
    '30인 이상 상용근로자를 보유한 기업의 1인당 평균 월별 법정외 복지비용이 10~29인 규모 기업보다 높은지 판단해 주세요.',
]
documents = [
    'data/images/ko/ko-vdr-public/3818.png',
    'data/images/ko/ko-vdr-public/3950.png',
    'data/images/ko/ko-vdr-public/6891.png',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 2048] [3, 2048]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.5044, -0.0077,  0.1005]])

Evaluation

Metrics

Information Retrieval

• Datasets: kovidore-v2-cybersecurity-beir-eval, kovidore-v2-hr-beir-eval, kovidore-v2-energy-beir-eval and kovidore-v2-economic-beir-eval
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "query_prompt": "Find a screenshot that is relevant to the user's question."
  }
  

Metric	kovidore-v2-cybersecurity-beir-eval	kovidore-v2-hr-beir-eval	kovidore-v2-energy-beir-eval	kovidore-v2-economic-beir-eval
cosine_accuracy@1	0.7181	0.4434	0.6647	0.2515
cosine_accuracy@3	0.8993	0.6787	0.8613	0.4294
cosine_accuracy@5	0.9195	0.7828	0.9075	0.5215
cosine_accuracy@10	0.9597	0.8824	0.9249	0.6503
cosine_precision@1	0.7181	0.4434	0.6647	0.2515
cosine_precision@3	0.4698	0.3243	0.4663	0.1575
cosine_precision@5	0.3436	0.2597	0.3422	0.119
cosine_precision@10	0.2067	0.1828	0.2075	0.0791
cosine_recall@1	0.344	0.1471	0.2575	0.105
cosine_recall@3	0.6076	0.3176	0.5152	0.2027
cosine_recall@5	0.7006	0.4189	0.6092	0.2594
cosine_recall@10	0.81	0.577	0.7181	0.347
cosine_ndcg@5	0.6774	0.4071	0.604	0.2378
cosine_ndcg@10	0.7272	0.4755	0.6541	0.2726
cosine_mrr@10	0.8084	0.5864	0.7681	0.3681
cosine_map@100	0.6519	0.3883	0.5807	0.2154

Training Details

Training Dataset

Unnamed Dataset

• Size: 708,729 training samples
• Columns: anchor, positive, negative_1, negative_2, negative_3, negative_4, negative_5, negative_6, and negative_7
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7
  type	string	string	string	string	string	string	string	string	string
  details	min: 36 tokens mean: 66.87 tokens max: 118 tokens	min: 1240 tokens mean: 1272.48 tokens max: 1298 tokens	min: 1240 tokens mean: 1272.4 tokens max: 1298 tokens	min: 1240 tokens mean: 1272.51 tokens max: 1298 tokens	min: 1240 tokens mean: 1272.28 tokens max: 1298 tokens	min: 1240 tokens mean: 1272.3 tokens max: 1298 tokens	min: 1240 tokens mean: 1272.69 tokens max: 1298 tokens	min: 1240 tokens mean: 1272.49 tokens max: 1298 tokens	min: 1240 tokens mean: 1272.36 tokens max: 1298 tokens

• Samples:

  anchor	positive	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7
  30인 이상 상용근로자를 보유한 기업의 1인당 평균 월별 법정외 복지비용이 10~29인 규모 기업보다 높은지 판단해 주세요.	data/images/ko/ko-vdr-public/3818.png	data/images/ko/ko-vdr-public/3763.png	data/images/ko/ko-vdr-public/7798.png	data/images/ko/ko-vdr-public/3752.png	data/images/ko/ko-vdr-public/3770.png	data/images/ko/ko-vdr-public/3773.png	data/images/ko/ko-vdr-public/7805.png	data/images/ko/ko-vdr-public/3785.png
  CAP 기반 조정이 Jensen‑Shannon 발산을 활용한 분포 유사성 검증에 어떤 영향을 미치는가?	data/images/ko/ko-vdr-public/3950.png	data/images/ko/ko-vdr-public/3934.png	data/images/ko/ko-vdr-public/5252.png	data/images/ko/ko-vdr-public/3959.png	data/images/ko/ko-vdr-public/5096.png	data/images/ko/ko-vdr-public/3960.png	data/images/ko/ko-vdr-public/5196.png	data/images/ko/ko-vdr-public/891.png
  소상공인 음식점업 체감경기 회복세가 온라인 음식서비스 성장률 확대와 소매판매액 내 비내구재 성장세 강화에 기여했는가?	data/images/ko/ko-vdr-public/6891.png	data/images/ko/ko-vdr-public/6886.png	data/images/ko/ko-vdr-public/6919.png	data/images/ko/ko-vdr-public/6883.png	data/images/ko/ko-vdr-public/6948.png	data/images/ko/ko-vdr-public/6882.png	data/images/ko/ko-vdr-public/6915.png	data/images/ko/ko-vdr-public/6921.png

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "CachedMultipleNegativesRankingLoss",
      "matryoshka_dims": [
          2048,
          1024,
          768,
          512,
          256,
          128
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 128
• num_train_epochs: 1.0
• learning_rate: 2e-05
• lr_scheduler_type: cosine
• warmup_steps: 0.1
• gradient_accumulation_steps: 2
• bf16: True
• per_device_eval_batch_size: 128
• eval_on_start: True
• ddp_find_unused_parameters: True
• batch_sampler: no_duplicates_hashed

All Hyperparameters

Click to expand

• per_device_train_batch_size: 128
• num_train_epochs: 1.0
• max_steps: -1
• learning_rate: 2e-05
• lr_scheduler_type: cosine
• lr_scheduler_kwargs: None
• warmup_steps: 0.1
• optim: adamw_torch_fused
• optim_args: None
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• optim_target_modules: None
• gradient_accumulation_steps: 2
• average_tokens_across_devices: True
• max_grad_norm: 1.0
• label_smoothing_factor: 0.0
• bf16: True
• fp16: False
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• use_liger_kernel: False
• liger_kernel_config: None
• use_cache: False
• neftune_noise_alpha: None
• torch_empty_cache_steps: None
• auto_find_batch_size: False
• log_on_each_node: True
• logging_nan_inf_filter: True
• include_num_input_tokens_seen: no
• log_level: passive
• log_level_replica: warning
• disable_tqdm: False
• project: huggingface
• trackio_space_id: trackio
• per_device_eval_batch_size: 128
• prediction_loss_only: True
• eval_on_start: True
• eval_do_concat_batches: True
• eval_use_gather_object: False
• eval_accumulation_steps: None
• include_for_metrics: []
• batch_eval_metrics: False
• save_only_model: False
• save_on_each_node: False
• enable_jit_checkpoint: False
• push_to_hub: False
• hub_private_repo: None
• hub_model_id: None
• hub_strategy: every_save
• hub_always_push: False
• hub_revision: None
• load_best_model_at_end: False
• ignore_data_skip: False
• restore_callback_states_from_checkpoint: False
• full_determinism: False
• seed: 42
• data_seed: None
• use_cpu: False
• 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
• dataloader_drop_last: True
• dataloader_num_workers: 0
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• dataloader_prefetch_factor: None
• remove_unused_columns: True
• label_names: None
• train_sampling_strategy: random
• length_column_name: length
• ddp_find_unused_parameters: True
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• ddp_backend: None
• ddp_timeout: 1800
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• deepspeed: None
• debug: []
• skip_memory_metrics: True
• do_predict: False
• resume_from_checkpoint: None
• warmup_ratio: None
• local_rank: -1
• prompts: None
• batch_sampler: no_duplicates_hashed
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}
• mini_batch_size: 8
• matryoshka_dims: [2048, 1024, 768, 512, 256, 128]
• use_lora: False
• lora_r: 32
• lora_alpha: 32
• lora_dropout: 0.05
• lora_target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']
• use_self_guide: False
• self_guide_margin: -0.1

Training Logs

Click to expand

Epoch	Step	Training Loss	kovidore-v2-cybersecurity-beir-eval_cosine_ndcg@10	kovidore-v2-hr-beir-eval_cosine_ndcg@10	kovidore-v2-energy-beir-eval_cosine_ndcg@10	kovidore-v2-economic-beir-eval_cosine_ndcg@10
0	0	-	0.6056	0.1877	0.4050	0.1478
0.0007	1	19.0919	-	-	-	-
0.0014	2	19.8867	-	-	-	-
0.0022	3	19.5917	-	-	-	-
0.0029	4	19.1449	-	-	-	-
0.0036	5	19.3840	-	-	-	-
0.0043	6	20.0008	-	-	-	-
0.0051	7	19.0200	-	-	-	-
0.0058	8	19.2317	-	-	-	-
0.0065	9	19.3274	-	-	-	-
0.0072	10	18.6426	-	-	-	-
0.0079	11	19.6216	-	-	-	-
0.0087	12	19.2203	-	-	-	-
0.0094	13	18.5694	-	-	-	-
0.0101	14	18.8146	-	-	-	-
0.0108	15	18.9514	-	-	-	-
0.0116	16	18.0845	-	-	-	-
0.0123	17	16.9328	-	-	-	-
0.0130	18	17.3781	-	-	-	-
0.0137	19	17.1977	-	-	-	-
0.0145	20	17.0873	-	-	-	-
0.0152	21	16.2790	-	-	-	-
0.0159	22	16.2738	-	-	-	-
0.0166	23	16.5812	-	-	-	-
0.0173	24	16.4922	-	-	-	-
0.0181	25	15.8195	-	-	-	-
0.0188	26	16.2999	-	-	-	-
0.0195	27	15.0997	-	-	-	-
0.0202	28	15.2898	-	-	-	-
0.0210	29	15.2226	-	-	-	-
0.0217	30	14.5302	-	-	-	-
0.0224	31	14.2536	-	-	-	-
0.0231	32	14.1144	-	-	-	-
0.0238	33	13.7726	-	-	-	-
0.0246	34	13.0787	-	-	-	-
0.0253	35	13.3732	-	-	-	-
0.0260	36	13.0835	-	-	-	-
0.0267	37	12.8277	-	-	-	-
0.0275	38	12.6780	-	-	-	-
0.0282	39	12.4271	-	-	-	-
0.0289	40	12.3580	-	-	-	-
0.0296	41	12.0198	-	-	-	-
0.0303	42	12.6874	-	-	-	-
0.0311	43	12.8180	-	-	-	-
0.0318	44	11.5252	-	-	-	-
0.0325	45	12.6015	-	-	-	-
0.0332	46	10.8566	-	-	-	-
0.0340	47	11.6519	-	-	-	-
0.0347	48	11.7856	-	-	-	-
0.0354	49	11.1542	-	-	-	-
0.0361	50	11.7578	-	-	-	-
0.0368	51	11.5312	-	-	-	-
0.0376	52	11.4435	-	-	-	-
0.0383	53	12.1531	-	-	-	-
0.0390	54	11.4413	-	-	-	-
0.0397	55	11.6788	-	-	-	-
0.0405	56	11.1894	-	-	-	-
0.0412	57	11.1426	-	-	-	-
0.0419	58	11.5071	-	-	-	-
0.0426	59	11.5983	-	-	-	-
0.0434	60	11.0551	-	-	-	-
0.0441	61	10.2813	-	-	-	-
0.0448	62	11.5840	-	-	-	-
0.0455	63	10.4358	-	-	-	-
0.0462	64	10.3742	-	-	-	-
0.0470	65	11.0668	-	-	-	-
0.0477	66	10.2637	-	-	-	-
0.0484	67	10.0504	-	-	-	-
0.0491	68	10.6066	-	-	-	-
0.0499	69	9.9561	-	-	-	-
0.0506	70	9.9868	-	-	-	-
0.0513	71	9.8717	-	-	-	-
0.0520	72	10.5210	-	-	-	-
0.0527	73	10.0393	-	-	-	-
0.0535	74	10.6454	-	-	-	-
0.0542	75	9.9684	-	-	-	-
0.0549	76	9.5214	-	-	-	-
0.0556	77	10.0693	-	-	-	-
0.0564	78	10.5039	-	-	-	-
0.0571	79	10.1645	-	-	-	-
0.0578	80	10.3510	-	-	-	-
0.0585	81	9.0350	-	-	-	-
0.0592	82	9.4375	-	-	-	-
0.0600	83	9.3925	-	-	-	-
0.0607	84	9.0280	-	-	-	-
0.0614	85	9.6051	-	-	-	-
0.0621	86	9.8118	-	-	-	-
0.0629	87	8.7345	-	-	-	-
0.0636	88	9.2292	-	-	-	-
0.0643	89	9.4511	-	-	-	-
0.0650	90	8.9964	-	-	-	-
0.0658	91	8.9374	-	-	-	-
0.0665	92	9.4078	-	-	-	-
0.0672	93	9.7125	-	-	-	-
0.0679	94	9.4957	-	-	-	-
0.0686	95	8.6961	-	-	-	-
0.0694	96	8.8827	-	-	-	-
0.0701	97	9.0507	-	-	-	-
0.0708	98	8.2216	-	-	-	-
0.0715	99	8.4956	-	-	-	-
0.0723	100	8.6325	0.6742	0.3172	0.5185	0.2072
0.0730	101	9.5200	-	-	-	-
0.0737	102	8.8228	-	-	-	-
0.0744	103	8.8142	-	-	-	-
0.0751	104	8.6033	-	-	-	-
0.0759	105	8.9870	-	-	-	-
0.0766	106	8.5835	-	-	-	-
0.0773	107	8.6829	-	-	-	-
0.0780	108	8.2830	-	-	-	-
0.0788	109	8.6042	-	-	-	-
0.0795	110	7.9054	-	-	-	-
0.0802	111	8.8614	-	-	-	-
0.0809	112	8.4062	-	-	-	-
0.0816	113	8.9702	-	-	-	-
0.0824	114	8.2764	-	-	-	-
0.0831	115	8.2097	-	-	-	-
0.0838	116	7.8429	-	-	-	-
0.0845	117	8.7368	-	-	-	-
0.0853	118	8.3684	-	-	-	-
0.0860	119	8.1289	-	-	-	-
0.0867	120	7.8589	-	-	-	-
0.0874	121	8.3147	-	-	-	-
0.0882	122	8.2876	-	-	-	-
0.0889	123	8.2678	-	-	-	-
0.0896	124	8.5950	-	-	-	-
0.0903	125	8.7891	-	-	-	-
0.0910	126	8.3199	-	-	-	-
0.0918	127	8.4374	-	-	-	-
0.0925	128	8.3170	-	-	-	-
0.0932	129	8.1996	-	-	-	-
0.0939	130	7.9703	-	-	-	-
0.0947	131	8.4551	-	-	-	-
0.0954	132	8.2848	-	-	-	-
0.0961	133	7.7070	-	-	-	-
0.0968	134	8.2779	-	-	-	-
0.0975	135	8.1997	-	-	-	-
0.0983	136	8.0540	-	-	-	-
0.0990	137	8.2734	-	-	-	-
0.0997	138	8.0741	-	-	-	-
0.1004	139	8.2742	-	-	-	-
0.1012	140	8.5328	-	-	-	-
0.1019	141	8.0972	-	-	-	-
0.1026	142	7.8412	-	-	-	-
0.1033	143	8.0742	-	-	-	-
0.1040	144	8.3305	-	-	-	-
0.1048	145	8.2164	-	-	-	-
0.1055	146	8.2332	-	-	-	-
0.1062	147	8.9792	-	-	-	-
0.1069	148	8.4994	-	-	-	-
0.1077	149	7.7255	-	-	-	-
0.1084	150	8.0231	-	-	-	-
0.1091	151	8.3959	-	-	-	-
0.1098	152	8.3997	-	-	-	-
0.1105	153	8.2747	-	-	-	-
0.1113	154	8.3838	-	-	-	-
0.1120	155	7.8968	-	-	-	-
0.1127	156	7.2480	-	-	-	-
0.1134	157	7.3077	-	-	-	-
0.1142	158	7.7862	-	-	-	-
0.1149	159	7.9345	-	-	-	-
0.1156	160	7.4820	-	-	-	-
0.1163	161	6.9438	-	-	-	-
0.1171	162	7.5755	-	-	-	-
0.1178	163	8.0783	-	-	-	-
0.1185	164	7.8913	-	-	-	-
0.1192	165	8.3917	-	-	-	-
0.1199	166	7.9000	-	-	-	-
0.1207	167	7.3779	-	-	-	-
0.1214	168	7.6499	-	-	-	-
0.1221	169	7.8189	-	-	-	-
0.1228	170	7.0421	-	-	-	-
0.1236	171	7.5455	-	-	-	-
0.1243	172	7.6591	-	-	-	-
0.125	173	7.5315	-	-	-	-
0.1257	174	6.9696	-	-	-	-
0.1264	175	8.0363	-	-	-	-
0.1272	176	7.5222	-	-	-	-
0.1279	177	7.3863	-	-	-	-
0.1286	178	7.7400	-	-	-	-
0.1293	179	7.3972	-	-	-	-
0.1301	180	7.2888	-	-	-	-
0.1308	181	7.2564	-	-	-	-
0.1315	182	8.3560	-	-	-	-
0.1322	183	7.2628	-	-	-	-
0.1329	184	7.5267	-	-	-	-
0.1337	185	7.0959	-	-	-	-
0.1344	186	7.8697	-	-	-	-
0.1351	187	7.7619	-	-	-	-
0.1358	188	7.8522	-	-	-	-
0.1366	189	7.2222	-	-	-	-
0.1373	190	8.1619	-	-	-	-
0.1380	191	7.0979	-	-	-	-
0.1387	192	7.7890	-	-	-	-
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0.1402	194	7.5562	-	-	-	-
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0.1416	196	7.6017	-	-	-	-
0.1423	197	7.4089	-	-	-	-
0.1431	198	7.1814	-	-	-	-
0.1438	199	7.8917	-	-	-	-
0.1445	200	7.3054	0.7095	0.3974	0.5768	0.2234
0.1452	201	7.3791	-	-	-	-
0.1460	202	7.4072	-	-	-	-
0.1467	203	7.2233	-	-	-	-
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0.1481	205	7.0956	-	-	-	-
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0.1503	208	7.0760	-	-	-	-
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0.1590	220	7.0423	-	-	-	-
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0.1640	227	6.9864	-	-	-	-
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0.1662	230	7.3279	-	-	-	-
0.1669	231	7.0851	-	-	-	-
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0.1684	233	7.3738	-	-	-	-
0.1691	234	7.2146	-	-	-	-
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0.1705	236	7.1412	-	-	-	-
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0.1792	248	6.7485	-	-	-	-
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0.1806	250	6.7431	-	-	-	-
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0.1821	252	7.0699	-	-	-	-
0.1828	253	6.8825	-	-	-	-
0.1835	254	6.3211	-	-	-	-
0.1842	255	7.3109	-	-	-	-
0.1850	256	6.7846	-	-	-	-
0.1857	257	7.0479	-	-	-	-
0.1864	258	6.4024	-	-	-	-
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0.1886	261	7.1546	-	-	-	-
0.1893	262	6.8526	-	-	-	-
0.1900	263	6.9781	-	-	-	-
0.1908	264	7.0425	-	-	-	-
0.1915	265	7.0311	-	-	-	-
0.1922	266	7.2632	-	-	-	-
0.1929	267	6.4864	-	-	-	-
0.1936	268	6.9299	-	-	-	-
0.1944	269	6.4814	-	-	-	-
0.1951	270	6.6264	-	-	-	-
0.1958	271	6.9644	-	-	-	-
0.1965	272	6.7054	-	-	-	-
0.1973	273	7.0413	-	-	-	-
0.1980	274	6.7343	-	-	-	-
0.1987	275	6.3281	-	-	-	-
0.1994	276	7.0185	-	-	-	-
0.2001	277	6.7784	-	-	-	-
0.2009	278	7.0943	-	-	-	-
0.2016	279	6.8568	-	-	-	-
0.2023	280	6.7533	-	-	-	-
0.2030	281	6.8833	-	-	-	-
0.2038	282	7.0575	-	-	-	-
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0.2103	291	6.4098	-	-	-	-
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0.2160	299	6.3351	-	-	-	-
0.2168	300	6.6346	0.6961	0.4173	0.6300	0.2373
0.2175	301	6.6551	-	-	-	-
0.2182	302	6.3409	-	-	-	-
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0.2204	305	6.4818	-	-	-	-
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0.6871	951	5.6849	-	-	-	-
0.6879	952	5.4932	-	-	-	-
0.6886	953	5.2280	-	-	-	-
0.6893	954	5.3022	-	-	-	-
0.6900	955	5.2211	-	-	-	-
0.6908	956	5.3864	-	-	-	-
0.6915	957	5.4473	-	-	-	-
0.6922	958	5.3845	-	-	-	-
0.6929	959	4.7171	-	-	-	-
0.6936	960	5.3825	-	-	-	-
0.6944	961	5.4428	-	-	-	-
0.6951	962	4.9681	-	-	-	-
0.6958	963	5.3186	-	-	-	-
0.6965	964	5.2438	-	-	-	-
0.6973	965	5.7513	-	-	-	-
0.6980	966	5.2074	-	-	-	-
0.6987	967	5.5972	-	-	-	-
0.6994	968	5.0319	-	-	-	-
0.7001	969	5.4092	-	-	-	-
0.7009	970	5.0395	-	-	-	-
0.7016	971	5.2044	-	-	-	-
0.7023	972	5.5324	-	-	-	-
0.7030	973	5.4694	-	-	-	-
0.7038	974	5.5656	-	-	-	-
0.7045	975	5.6317	-	-	-	-
0.7052	976	5.5206	-	-	-	-
0.7059	977	5.6643	-	-	-	-
0.7066	978	5.1649	-	-	-	-
0.7074	979	6.0422	-	-	-	-
0.7081	980	5.7866	-	-	-	-
0.7088	981	5.4611	-	-	-	-
0.7095	982	5.3482	-	-	-	-
0.7103	983	5.6990	-	-	-	-
0.7110	984	5.4506	-	-	-	-
0.7117	985	5.3709	-	-	-	-
0.7124	986	5.2311	-	-	-	-
0.7132	987	5.1362	-	-	-	-
0.7139	988	5.6586	-	-	-	-
0.7146	989	4.9879	-	-	-	-
0.7153	990	5.6985	-	-	-	-
0.7160	991	5.2650	-	-	-	-
0.7168	992	5.5303	-	-	-	-
0.7175	993	5.0728	-	-	-	-
0.7182	994	5.4079	-	-	-	-
0.7189	995	5.6700	-	-	-	-
0.7197	996	5.3255	-	-	-	-
0.7204	997	5.2993	-	-	-	-
0.7211	998	5.6256	-	-	-	-
0.7218	999	5.2808	-	-	-	-
0.7225	1000	5.5055	0.7223	0.4691	0.6523	0.2657
0.7233	1001	5.1685	-	-	-	-
0.7240	1002	5.0808	-	-	-	-
0.7247	1003	5.3123	-	-	-	-
0.7254	1004	5.2041	-	-	-	-
0.7262	1005	5.2156	-	-	-	-
0.7269	1006	5.5056	-	-	-	-
0.7276	1007	5.7968	-	-	-	-
0.7283	1008	4.8278	-	-	-	-
0.7290	1009	5.0452	-	-	-	-
0.7298	1010	5.3035	-	-	-	-
0.7305	1011	5.4277	-	-	-	-
0.7312	1012	5.5039	-	-	-	-
0.7319	1013	5.1184	-	-	-	-
0.7327	1014	5.4212	-	-	-	-
0.7334	1015	5.2474	-	-	-	-
0.7341	1016	5.7134	-	-	-	-
0.7348	1017	5.1479	-	-	-	-
0.7355	1018	4.9485	-	-	-	-
0.7363	1019	5.9090	-	-	-	-
0.7370	1020	5.2582	-	-	-	-
0.7377	1021	5.3592	-	-	-	-
0.7384	1022	5.1288	-	-	-	-
0.7392	1023	5.1742	-	-	-	-
0.7399	1024	5.2111	-	-	-	-
0.7406	1025	5.0484	-	-	-	-
0.7413	1026	5.4047	-	-	-	-
0.7421	1027	5.0253	-	-	-	-
0.7428	1028	4.9168	-	-	-	-
0.7435	1029	5.3110	-	-	-	-
0.7442	1030	5.7918	-	-	-	-
0.7449	1031	5.1020	-	-	-	-
0.7457	1032	5.9942	-	-	-	-
0.7464	1033	4.9788	-	-	-	-
0.7471	1034	5.3004	-	-	-	-
0.7478	1035	5.3815	-	-	-	-
0.7486	1036	5.7455	-	-	-	-
0.7493	1037	5.1280	-	-	-	-
0.75	1038	5.4730	-	-	-	-
0.7507	1039	5.4256	-	-	-	-
0.7514	1040	5.6683	-	-	-	-
0.7522	1041	4.9913	-	-	-	-
0.7529	1042	5.7886	-	-	-	-
0.7536	1043	5.5245	-	-	-	-
0.7543	1044	5.1682	-	-	-	-
0.7551	1045	5.3222	-	-	-	-
0.7558	1046	5.2782	-	-	-	-
0.7565	1047	4.9509	-	-	-	-
0.7572	1048	5.7216	-	-	-	-
0.7579	1049	5.4174	-	-	-	-
0.7587	1050	5.2517	-	-	-	-
0.7594	1051	5.1345	-	-	-	-
0.7601	1052	4.8413	-	-	-	-
0.7608	1053	5.8908	-	-	-	-
0.7616	1054	5.2968	-	-	-	-
0.7623	1055	5.6777	-	-	-	-
0.7630	1056	5.2377	-	-	-	-
0.7637	1057	5.3109	-	-	-	-
0.7645	1058	5.2000	-	-	-	-
0.7652	1059	5.1186	-	-	-	-
0.7659	1060	5.3536	-	-	-	-
0.7666	1061	5.2641	-	-	-	-
0.7673	1062	5.4459	-	-	-	-
0.7681	1063	5.1539	-	-	-	-
0.7688	1064	5.3753	-	-	-	-
0.7695	1065	4.7219	-	-	-	-
0.7702	1066	5.4860	-	-	-	-
0.7710	1067	5.3111	-	-	-	-
0.7717	1068	5.0592	-	-	-	-
0.7724	1069	5.4875	-	-	-	-
0.7731	1070	4.8750	-	-	-	-
0.7738	1071	5.4281	-	-	-	-
0.7746	1072	5.6073	-	-	-	-
0.7753	1073	5.6222	-	-	-	-
0.7760	1074	5.4030	-	-	-	-
0.7767	1075	5.2540	-	-	-	-
0.7775	1076	5.3791	-	-	-	-
0.7782	1077	5.7722	-	-	-	-
0.7789	1078	5.4970	-	-	-	-
0.7796	1079	5.4802	-	-	-	-
0.7803	1080	5.4868	-	-	-	-
0.7811	1081	5.5680	-	-	-	-
0.7818	1082	5.5559	-	-	-	-
0.7825	1083	5.5265	-	-	-	-
0.7832	1084	5.2988	-	-	-	-
0.7840	1085	4.9672	-	-	-	-
0.7847	1086	4.8483	-	-	-	-
0.7854	1087	5.3563	-	-	-	-
0.7861	1088	5.1705	-	-	-	-
0.7868	1089	5.7830	-	-	-	-
0.7876	1090	5.5567	-	-	-	-
0.7883	1091	4.9965	-	-	-	-
0.7890	1092	5.2197	-	-	-	-
0.7897	1093	5.1033	-	-	-	-
0.7905	1094	5.1716	-	-	-	-
0.7912	1095	5.7410	-	-	-	-
0.7919	1096	5.3409	-	-	-	-
0.7926	1097	5.2111	-	-	-	-
0.7934	1098	5.1350	-	-	-	-
0.7941	1099	5.1364	-	-	-	-
0.7948	1100	5.8232	0.7235	0.4738	0.6578	0.2646
0.7955	1101	5.5140	-	-	-	-
0.7962	1102	5.4516	-	-	-	-
0.7970	1103	5.6997	-	-	-	-
0.7977	1104	5.0577	-	-	-	-
0.7984	1105	5.7355	-	-	-	-
0.7991	1106	5.3483	-	-	-	-
0.7999	1107	5.2335	-	-	-	-
0.8006	1108	5.5465	-	-	-	-
0.8013	1109	4.7978	-	-	-	-
0.8020	1110	5.2400	-	-	-	-
0.8027	1111	5.3041	-	-	-	-
0.8035	1112	5.4598	-	-	-	-
0.8042	1113	5.1203	-	-	-	-
0.8049	1114	5.4111	-	-	-	-
0.8056	1115	5.1817	-	-	-	-
0.8064	1116	5.4440	-	-	-	-
0.8071	1117	5.2471	-	-	-	-
0.8078	1118	5.2582	-	-	-	-
0.8085	1119	5.8283	-	-	-	-
0.8092	1120	5.6835	-	-	-	-
0.8100	1121	5.3220	-	-	-	-
0.8107	1122	5.3752	-	-	-	-
0.8114	1123	4.8916	-	-	-	-
0.8121	1124	5.2444	-	-	-	-
0.8129	1125	6.2362	-	-	-	-
0.8136	1126	4.9891	-	-	-	-
0.8143	1127	5.4165	-	-	-	-
0.8150	1128	5.2151	-	-	-	-
0.8158	1129	5.6450	-	-	-	-
0.8165	1130	5.5678	-	-	-	-
0.8172	1131	5.2326	-	-	-	-
0.8179	1132	5.1352	-	-	-	-
0.8186	1133	5.2369	-	-	-	-
0.8194	1134	5.3933	-	-	-	-
0.8201	1135	5.2654	-	-	-	-
0.8208	1136	5.7079	-	-	-	-
0.8215	1137	5.5263	-	-	-	-
0.8223	1138	5.1707	-	-	-	-
0.8230	1139	5.4294	-	-	-	-
0.8237	1140	5.1440	-	-	-	-
0.8244	1141	5.1251	-	-	-	-
0.8251	1142	5.5497	-	-	-	-
0.8259	1143	5.1902	-	-	-	-
0.8266	1144	5.7840	-	-	-	-
0.8273	1145	5.3416	-	-	-	-
0.8280	1146	5.2176	-	-	-	-
0.8288	1147	5.3414	-	-	-	-
0.8295	1148	4.9403	-	-	-	-
0.8302	1149	5.5676	-	-	-	-
0.8309	1150	5.7204	-	-	-	-
0.8316	1151	5.2942	-	-	-	-
0.8324	1152	4.6860	-	-	-	-
0.8331	1153	4.8432	-	-	-	-
0.8338	1154	5.1931	-	-	-	-
0.8345	1155	5.0743	-	-	-	-
0.8353	1156	5.2642	-	-	-	-
0.8360	1157	5.2093	-	-	-	-
0.8367	1158	5.3999	-	-	-	-
0.8374	1159	5.6863	-	-	-	-
0.8382	1160	5.4952	-	-	-	-
0.8389	1161	5.4543	-	-	-	-
0.8396	1162	5.4074	-	-	-	-
0.8403	1163	5.0643	-	-	-	-
0.8410	1164	5.1385	-	-	-	-
0.8418	1165	5.0698	-	-	-	-
0.8425	1166	5.2398	-	-	-	-
0.8432	1167	5.1608	-	-	-	-
0.8439	1168	4.9286	-	-	-	-
0.8447	1169	5.1664	-	-	-	-
0.8454	1170	5.2369	-	-	-	-
0.8461	1171	5.4637	-	-	-	-
0.8468	1172	5.6047	-	-	-	-
0.8475	1173	5.6302	-	-	-	-
0.8483	1174	5.1770	-	-	-	-
0.8490	1175	5.4205	-	-	-	-
0.8497	1176	5.1925	-	-	-	-
0.8504	1177	5.4395	-	-	-	-
0.8512	1178	5.0647	-	-	-	-
0.8519	1179	5.3476	-	-	-	-
0.8526	1180	5.3967	-	-	-	-
0.8533	1181	5.4103	-	-	-	-
0.8540	1182	4.9899	-	-	-	-
0.8548	1183	5.2094	-	-	-	-
0.8555	1184	5.3716	-	-	-	-
0.8562	1185	5.3476	-	-	-	-
0.8569	1186	5.7847	-	-	-	-
0.8577	1187	5.0475	-	-	-	-
0.8584	1188	5.0959	-	-	-	-
0.8591	1189	4.9680	-	-	-	-
0.8598	1190	5.4295	-	-	-	-
0.8605	1191	5.2959	-	-	-	-
0.8613	1192	5.1507	-	-	-	-
0.8620	1193	5.1398	-	-	-	-
0.8627	1194	5.0043	-	-	-	-
0.8634	1195	5.3290	-	-	-	-
0.8642	1196	5.6150	-	-	-	-
0.8649	1197	5.4368	-	-	-	-
0.8656	1198	4.9513	-	-	-	-
0.8663	1199	5.6064	-	-	-	-
0.8671	1200	5.2296	0.7210	0.4771	0.6557	0.2696
0.8678	1201	5.6796	-	-	-	-
0.8685	1202	5.2094	-	-	-	-
0.8692	1203	5.5388	-	-	-	-
0.8699	1204	5.2802	-	-	-	-
0.8707	1205	4.7212	-	-	-	-
0.8714	1206	5.1474	-	-	-	-
0.8721	1207	5.6223	-	-	-	-
0.8728	1208	5.6116	-	-	-	-
0.8736	1209	5.3112	-	-	-	-
0.8743	1210	5.1664	-	-	-	-
0.875	1211	5.3291	-	-	-	-
0.8757	1212	4.9297	-	-	-	-
0.8764	1213	5.4814	-	-	-	-
0.8772	1214	5.6206	-	-	-	-
0.8779	1215	5.1043	-	-	-	-
0.8786	1216	5.2393	-	-	-	-
0.8793	1217	5.2451	-	-	-	-
0.8801	1218	5.6090	-	-	-	-
0.8808	1219	5.7220	-	-	-	-
0.8815	1220	5.5524	-	-	-	-
0.8822	1221	4.7361	-	-	-	-
0.8829	1222	5.4003	-	-	-	-
0.8837	1223	4.8673	-	-	-	-
0.8844	1224	4.9721	-	-	-	-
0.8851	1225	5.5393	-	-	-	-
0.8858	1226	4.8313	-	-	-	-
0.8866	1227	5.5424	-	-	-	-
0.8873	1228	5.4957	-	-	-	-
0.8880	1229	5.2470	-	-	-	-
0.8887	1230	5.3981	-	-	-	-
0.8895	1231	5.1233	-	-	-	-
0.8902	1232	5.4014	-	-	-	-
0.8909	1233	5.5700	-	-	-	-
0.8916	1234	5.1361	-	-	-	-
0.8923	1235	5.6769	-	-	-	-
0.8931	1236	5.5002	-	-	-	-
0.8938	1237	5.5607	-	-	-	-
0.8945	1238	5.4528	-	-	-	-
0.8952	1239	5.7288	-	-	-	-
0.8960	1240	5.0941	-	-	-	-
0.8967	1241	4.8220	-	-	-	-
0.8974	1242	5.3630	-	-	-	-
0.8981	1243	6.1381	-	-	-	-
0.8988	1244	5.7304	-	-	-	-
0.8996	1245	5.6156	-	-	-	-
0.9003	1246	5.8270	-	-	-	-
0.9010	1247	5.5655	-	-	-	-
0.9017	1248	5.6237	-	-	-	-
0.9025	1249	5.1033	-	-	-	-
0.9032	1250	5.4019	-	-	-	-
0.9039	1251	5.5166	-	-	-	-
0.9046	1252	5.4347	-	-	-	-
0.9053	1253	6.0410	-	-	-	-
0.9061	1254	5.2520	-	-	-	-
0.9068	1255	5.1262	-	-	-	-
0.9075	1256	5.5556	-	-	-	-
0.9082	1257	4.6695	-	-	-	-
0.9090	1258	5.4925	-	-	-	-
0.9097	1259	5.4788	-	-	-	-
0.9104	1260	4.9913	-	-	-	-
0.9111	1261	5.8043	-	-	-	-
0.9118	1262	4.9158	-	-	-	-
0.9126	1263	6.1506	-	-	-	-
0.9133	1264	5.7525	-	-	-	-
0.9140	1265	5.4413	-	-	-	-
0.9147	1266	4.9375	-	-	-	-
0.9155	1267	5.1976	-	-	-	-
0.9162	1268	5.3966	-	-	-	-
0.9169	1269	4.9203	-	-	-	-
0.9176	1270	5.1365	-	-	-	-
0.9184	1271	4.9536	-	-	-	-
0.9191	1272	5.7576	-	-	-	-
0.9198	1273	5.3663	-	-	-	-
0.9205	1274	5.1386	-	-	-	-
0.9212	1275	5.4468	-	-	-	-
0.9220	1276	5.4881	-	-	-	-
0.9227	1277	5.4349	-	-	-	-
0.9234	1278	5.4886	-	-	-	-
0.9241	1279	5.4140	-	-	-	-
0.9249	1280	5.4578	-	-	-	-
0.9256	1281	5.4469	-	-	-	-
0.9263	1282	5.3661	-	-	-	-
0.9270	1283	5.4411	-	-	-	-
0.9277	1284	4.9452	-	-	-	-
0.9285	1285	5.1046	-	-	-	-
0.9292	1286	5.4537	-	-	-	-
0.9299	1287	5.5682	-	-	-	-
0.9306	1288	5.2226	-	-	-	-
0.9314	1289	5.5745	-	-	-	-
0.9321	1290	5.2679	-	-	-	-
0.9328	1291	5.4995	-	-	-	-
0.9335	1292	5.3984	-	-	-	-
0.9342	1293	5.5281	-	-	-	-
0.9350	1294	5.6193	-	-	-	-
0.9357	1295	5.4300	-	-	-	-
0.9364	1296	5.0684	-	-	-	-
0.9371	1297	5.2344	-	-	-	-
0.9379	1298	5.5178	-	-	-	-
0.9386	1299	5.5361	-	-	-	-
0.9393	1300	4.9324	0.7272	0.4755	0.6541	0.2726

Training Time

• Training: 5.5 days
• Evaluation: 1.7 hours
• Total: 5.5 days

Framework Versions

• Python: 3.10.19
• Sentence Transformers: 5.4.1
• Transformers: 5.5.4
• PyTorch: 2.11.0+cu130
• Accelerate: 1.13.0
• Datasets: 4.8.4
• Tokenizers: 0.22.2

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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

CachedMultipleNegativesRankingLoss

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}