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

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 on the whybe-choi/ko-vdr-hn and whybe-choi/en-vdr-hn datasets. 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
• Training Datasets:
  • whybe-choi/ko-vdr-hn
  • whybe-choi/en-vdr-hn
• 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([[ 0.5166, -0.0260,  0.0939]])

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	0.7114	0.4389	0.6358	0.184
cosine_accuracy@3	0.9128	0.6561	0.8497	0.4049
cosine_accuracy@5	0.9396	0.7783	0.9017	0.5092
cosine_accuracy@10	0.9866	0.8733	0.9306	0.6503
cosine_precision@1	0.7114	0.4389	0.6358	0.184
cosine_precision@3	0.472	0.3152	0.4489	0.1452
cosine_precision@5	0.3503	0.2624	0.3457	0.1202
cosine_precision@10	0.2107	0.1796	0.2058	0.0816
cosine_recall@1	0.3503	0.1482	0.2532	0.0794
cosine_recall@3	0.6061	0.3104	0.4985	0.1894
cosine_recall@5	0.714	0.4317	0.6169	0.2599
cosine_recall@10	0.8187	0.5719	0.7158	0.3519
cosine_ndcg@5	0.6873	0.4089	0.5969	0.2213
cosine_ndcg@10	0.7359	0.4702	0.6415	0.2591
cosine_mrr@10	0.8154	0.5764	0.7455	0.324
cosine_map@100	0.6603	0.3848	0.5648	0.2027

Training Details

Training Datasets

whybe-choi/ko-vdr-hn

• Dataset: whybe-choi/ko-vdr-hn at 74a01af
• Size: 406,945 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
  }
  

whybe-choi/en-vdr-hn

• Dataset: whybe-choi/en-vdr-hn at c8c903e
• Size: 301,784 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: 26 tokens mean: 37.72 tokens max: 75 tokens	min: 246 tokens mean: 1215.73 tokens max: 1302 tokens	min: 126 tokens mean: 1199.3 tokens max: 1302 tokens	min: 174 tokens mean: 1205.24 tokens max: 1302 tokens	min: 118 tokens mean: 1205.7 tokens max: 1302 tokens	min: 126 tokens mean: 1210.24 tokens max: 1302 tokens	min: 134 tokens mean: 1209.33 tokens max: 1302 tokens	min: 100 tokens mean: 1211.77 tokens max: 1302 tokens	min: 70 tokens mean: 1191.3 tokens max: 1302 tokens

• Samples:

  anchor	positive	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7
  What is the primary purpose of the PTC in lithium batteries?	data/images/en/colpali/23d41ee76deaf320b9b7556c965caf7e.jpg	data/images/en/colpali/5a713f00d599b2bfddd115a67808de66.jpg	data/images/en/colpali/59c8e47f68536ba395df914e39029cec.jpg	data/images/en/colpali/73d6ab0da1b8290e6b5b1ed67a4a6885.jpg	data/images/en/colpali/dd1cd6682696b6ad9bdaa6da18da3a84.jpg	data/images/en/colpali/842382d0ea0cff88578e96904e652c6c.jpg	data/images/en/colpali/dd1ce2f3a4e7df95e98ef2cde8e4e7f1.jpg	data/images/en/colpali/719e704bce4e95da3e177a3b5db2dc2b.jpg
  How is the baseline CO2 emissions calculated for affected EGUs in the low load natural gas-fired or oil-fired subcategories?	data/images/en/colpali/bfd3dd15e306b6ed4ce26cef3956fa95.jpg	data/images/en/colpali/aaaa2c2811f72ac8ec402726c6578b13.jpg	data/images/en/colpali/1a570a15beb0d27a505db8a4634448a6.jpg	data/images/en/colpali/6a50944dae78a15c400dacfe9f2a9145.jpg	data/images/en/colpali/997f4d2e956f5ea37c8b174f43585f3d.jpg	data/images/en/colpali/3290c06c7e864b4e2e1c4cbd86592de2.jpg	data/images/en/colpali/dc09dbce746b4df935a6ff623471e839.jpg	data/images/en/colpali/00039af9d06cf667e28455ea3d33d4e2.jpg
  What are some suggestions Liberty Medical Group should consider to improve their accounts receivable turnover and days sales in receivables ratios?	data/images/en/colpali/09232c2534e9bcca51ef284a6153a974.jpg	data/images/en/colpali/e07a0cd87bd36ea9bedd44b48061e86a.jpg	data/images/en/colpali/56cc228953990c3d5b84ff67fb141872.jpg	data/images/en/colpali/b1eb0643c43238e35329bd2ec03ace8d.jpg	data/images/en/colpali/c19a0b262d75dd5790f1ac26aa0bd836.jpg	data/images/en/colpali/aa22a1ee2f4565656969fde81c7280c2.jpg	data/images/en/colpali/ab749e1287df0e66b7b2092eb4d20fc1.jpg	data/images/en/colpali/8d7b8a908e0d11d58c3d39146831a1f5.jpg

• 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	19.1379	-	-	-	-
0.0014	2	18.9229	-	-	-	-
0.0022	3	26.2451	-	-	-	-
0.0029	4	16.0166	-	-	-	-
0.0036	5	19.4486	-	-	-	-
0.0043	6	16.1000	-	-	-	-
0.0051	7	19.3822	-	-	-	-
0.0058	8	25.8949	-	-	-	-
0.0065	9	22.4898	-	-	-	-
0.0072	10	19.8494	-	-	-	-
0.0079	11	15.3722	-	-	-	-
0.0087	12	22.0873	-	-	-	-
0.0094	13	25.5506	-	-	-	-
0.0101	14	12.5892	-	-	-	-
0.0108	15	15.0147	-	-	-	-
0.0116	16	21.0032	-	-	-	-
0.0123	17	15.1064	-	-	-	-
0.0130	18	17.8412	-	-	-	-
0.0137	19	23.8582	-	-	-	-
0.0145	20	20.4970	-	-	-	-
0.0152	21	14.2144	-	-	-	-
0.0159	22	13.9488	-	-	-	-
0.0166	23	17.2926	-	-	-	-
0.0173	24	22.8800	-	-	-	-
0.0181	25	16.2528	-	-	-	-
0.0188	26	16.7433	-	-	-	-
0.0195	27	15.6478	-	-	-	-
0.0202	28	22.1664	-	-	-	-
0.0210	29	21.9191	-	-	-	-
0.0217	30	20.9106	-	-	-	-
0.0224	31	14.3773	-	-	-	-
0.0231	32	17.1562	-	-	-	-
0.0238	33	14.0399	-	-	-	-
0.0246	34	17.3966	-	-	-	-
0.0253	35	11.0495	-	-	-	-
0.0260	36	15.9159	-	-	-	-
0.0267	37	13.2785	-	-	-	-
0.0275	38	19.0166	-	-	-	-
0.0282	39	15.5850	-	-	-	-
0.0289	40	15.7148	-	-	-	-
0.0296	41	12.7469	-	-	-	-
0.0303	42	18.1033	-	-	-	-
0.0311	43	12.9498	-	-	-	-
0.0318	44	15.3477	-	-	-	-
0.0325	45	15.2469	-	-	-	-
0.0332	46	14.6017	-	-	-	-
0.0340	47	17.3267	-	-	-	-
0.0347	48	16.9670	-	-	-	-
0.0354	49	7.2643	-	-	-	-
0.0361	50	9.8470	-	-	-	-
0.0368	51	12.0698	-	-	-	-
0.0376	52	11.7251	-	-	-	-
0.0383	53	9.5320	-	-	-	-
0.0390	54	14.0723	-	-	-	-
0.0397	55	14.4786	-	-	-	-
0.0405	56	9.7275	-	-	-	-
0.0412	57	10.7453	-	-	-	-
0.0419	58	8.3056	-	-	-	-
0.0426	59	9.5374	-	-	-	-
0.0434	60	11.4509	-	-	-	-
0.0441	61	10.8726	-	-	-	-
0.0448	62	11.6201	-	-	-	-
0.0455	63	10.9658	-	-	-	-
0.0462	64	11.0918	-	-	-	-
0.0470	65	16.0870	-	-	-	-
0.0477	66	16.1759	-	-	-	-
0.0484	67	15.3073	-	-	-	-
0.0491	68	11.0324	-	-	-	-
0.0499	69	14.8292	-	-	-	-
0.0506	70	6.7717	-	-	-	-
0.0513	71	12.6847	-	-	-	-
0.0520	72	10.7565	-	-	-	-
0.0527	73	8.8755	-	-	-	-
0.0535	74	13.7171	-	-	-	-
0.0542	75	8.7086	-	-	-	-
0.0549	76	10.3438	-	-	-	-
0.0556	77	10.0306	-	-	-	-
0.0564	78	12.4025	-	-	-	-
0.0571	79	12.8694	-	-	-	-
0.0578	80	10.0686	-	-	-	-
0.0585	81	8.3960	-	-	-	-
0.0592	82	13.9795	-	-	-	-
0.0600	83	10.0807	-	-	-	-
0.0607	84	10.3220	-	-	-	-
0.0614	85	9.8989	-	-	-	-
0.0621	86	9.8388	-	-	-	-
0.0629	87	5.6388	-	-	-	-
0.0636	88	10.0262	-	-	-	-
0.0643	89	8.2790	-	-	-	-
0.0650	90	9.7402	-	-	-	-
0.0658	91	13.4122	-	-	-	-
0.0665	92	11.7798	-	-	-	-
0.0672	93	10.1880	-	-	-	-
0.0679	94	7.9499	-	-	-	-
0.0686	95	9.8354	-	-	-	-
0.0694	96	9.5834	-	-	-	-
0.0701	97	11.9159	-	-	-	-
0.0708	98	11.7099	-	-	-	-
0.0715	99	8.2514	-	-	-	-
0.0723	100	13.1260	0.6757	0.3140	0.5356	0.2223
0.0730	101	11.0303	-	-	-	-
0.0737	102	9.0223	-	-	-	-
0.0744	103	9.5915	-	-	-	-
0.0751	104	13.0176	-	-	-	-
0.0759	105	7.6247	-	-	-	-
0.0766	106	9.9025	-	-	-	-
0.0773	107	9.1125	-	-	-	-
0.0780	108	9.7029	-	-	-	-
0.0788	109	9.1896	-	-	-	-
0.0795	110	11.0168	-	-	-	-
0.0802	111	11.1087	-	-	-	-
0.0809	112	12.3785	-	-	-	-
0.0816	113	9.7064	-	-	-	-
0.0824	114	9.3411	-	-	-	-
0.0831	115	10.9778	-	-	-	-
0.0838	116	8.8305	-	-	-	-
0.0845	117	9.7394	-	-	-	-
0.0853	118	10.3325	-	-	-	-
0.0860	119	8.8358	-	-	-	-
0.0867	120	7.6604	-	-	-	-
0.0874	121	9.5209	-	-	-	-
0.0882	122	8.0198	-	-	-	-
0.0889	123	12.2697	-	-	-	-
0.0896	124	9.0912	-	-	-	-
0.0903	125	7.4947	-	-	-	-
0.0910	126	10.5419	-	-	-	-
0.0918	127	11.2453	-	-	-	-
0.0925	128	10.5963	-	-	-	-
0.0932	129	10.3412	-	-	-	-
0.0939	130	7.8679	-	-	-	-
0.0947	131	12.1248	-	-	-	-
0.0954	132	9.1620	-	-	-	-
0.0961	133	8.8963	-	-	-	-
0.0968	134	10.8781	-	-	-	-
0.0975	135	8.5860	-	-	-	-
0.0983	136	7.5210	-	-	-	-
0.0990	137	9.7468	-	-	-	-
0.0997	138	9.6623	-	-	-	-
0.1004	139	8.4799	-	-	-	-
0.1012	140	8.8839	-	-	-	-
0.1019	141	11.5312	-	-	-	-
0.1026	142	10.4133	-	-	-	-
0.1033	143	9.4093	-	-	-	-
0.1040	144	8.2124	-	-	-	-
0.1048	145	10.8451	-	-	-	-
0.1055	146	9.9916	-	-	-	-
0.1062	147	6.8962	-	-	-	-
0.1069	148	9.9801	-	-	-	-
0.1077	149	8.8308	-	-	-	-
0.1084	150	8.3326	-	-	-	-
0.1091	151	8.0163	-	-	-	-
0.1098	152	7.2944	-	-	-	-
0.1105	153	9.9907	-	-	-	-
0.1113	154	10.0831	-	-	-	-
0.1120	155	8.1769	-	-	-	-
0.1127	156	8.3163	-	-	-	-
0.1134	157	8.1429	-	-	-	-
0.1142	158	9.2245	-	-	-	-
0.1149	159	8.5503	-	-	-	-
0.1156	160	9.8514	-	-	-	-
0.1163	161	9.2164	-	-	-	-
0.1171	162	8.5017	-	-	-	-
0.1178	163	9.2241	-	-	-	-
0.1185	164	8.0048	-	-	-	-
0.1192	165	7.9466	-	-	-	-
0.1199	166	6.8125	-	-	-	-
0.1207	167	8.1324	-	-	-	-
0.1214	168	10.4128	-	-	-	-
0.1221	169	9.1096	-	-	-	-
0.1228	170	9.2938	-	-	-	-
0.1236	171	8.1461	-	-	-	-
0.1243	172	7.7910	-	-	-	-
0.125	173	7.8040	-	-	-	-
0.1257	174	6.9309	-	-	-	-
0.1264	175	8.0909	-	-	-	-
0.1272	176	7.9948	-	-	-	-
0.1279	177	8.1146	-	-	-	-
0.1286	178	9.1950	-	-	-	-
0.1293	179	8.5793	-	-	-	-
0.1301	180	5.2992	-	-	-	-
0.1308	181	5.6427	-	-	-	-
0.1315	182	7.0992	-	-	-	-
0.1322	183	8.0828	-	-	-	-
0.1329	184	5.1109	-	-	-	-
0.1337	185	9.0975	-	-	-	-
0.1344	186	6.7527	-	-	-	-
0.1351	187	9.3605	-	-	-	-
0.1358	188	11.1374	-	-	-	-
0.1366	189	7.0520	-	-	-	-
0.1373	190	6.7000	-	-	-	-
0.1380	191	10.2814	-	-	-	-
0.1387	192	6.9331	-	-	-	-
0.1395	193	6.7634	-	-	-	-
0.1402	194	8.9780	-	-	-	-
0.1409	195	6.9960	-	-	-	-
0.1416	196	7.7169	-	-	-	-
0.1423	197	9.1218	-	-	-	-
0.1431	198	9.8747	-	-	-	-
0.1438	199	8.2517	-	-	-	-
0.1445	200	9.6139	0.7182	0.3707	0.5726	0.1784
0.1452	201	6.3589	-	-	-	-
0.1460	202	7.4813	-	-	-	-
0.1467	203	9.6023	-	-	-	-
0.1474	204	7.6787	-	-	-	-
0.1481	205	7.2351	-	-	-	-
0.1488	206	6.8670	-	-	-	-
0.1496	207	10.3732	-	-	-	-
0.1503	208	9.9240	-	-	-	-
0.1510	209	9.3780	-	-	-	-
0.1517	210	9.3404	-	-	-	-
0.1525	211	8.4722	-	-	-	-
0.1532	212	8.0410	-	-	-	-
0.1539	213	7.7923	-	-	-	-
0.1546	214	8.8958	-	-	-	-
0.1553	215	6.4838	-	-	-	-
0.1561	216	6.5126	-	-	-	-
0.1568	217	6.7802	-	-	-	-
0.1575	218	9.2891	-	-	-	-
0.1582	219	8.7300	-	-	-	-
0.1590	220	9.1180	-	-	-	-
0.1597	221	9.0600	-	-	-	-
0.1604	222	8.6626	-	-	-	-
0.1611	223	9.1981	-	-	-	-
0.1618	224	8.0864	-	-	-	-
0.1626	225	6.5325	-	-	-	-
0.1633	226	7.6085	-	-	-	-
0.1640	227	9.0443	-	-	-	-
0.1647	228	6.4216	-	-	-	-
0.1655	229	9.4925	-	-	-	-
0.1662	230	8.0054	-	-	-	-
0.1669	231	5.3425	-	-	-	-
0.1676	232	8.4954	-	-	-	-
0.1684	233	6.6650	-	-	-	-
0.1691	234	7.7126	-	-	-	-
0.1698	235	7.3105	-	-	-	-
0.1705	236	9.6832	-	-	-	-
0.1712	237	7.1745	-	-	-	-
0.1720	238	8.7681	-	-	-	-
0.1727	239	8.4647	-	-	-	-
0.1734	240	8.5645	-	-	-	-
0.1741	241	9.6978	-	-	-	-
0.1749	242	7.8280	-	-	-	-
0.1756	243	8.1378	-	-	-	-
0.1763	244	8.8602	-	-	-	-
0.1770	245	8.7932	-	-	-	-
0.1777	246	9.0339	-	-	-	-
0.1785	247	5.5556	-	-	-	-
0.1792	248	9.0276	-	-	-	-
0.1799	249	6.3963	-	-	-	-
0.1806	250	8.6511	-	-	-	-
0.1814	251	6.8920	-	-	-	-
0.1821	252	7.6089	-	-	-	-
0.1828	253	6.1774	-	-	-	-
0.1835	254	6.5225	-	-	-	-
0.1842	255	7.1419	-	-	-	-
0.1850	256	6.4712	-	-	-	-
0.1857	257	7.5747	-	-	-	-
0.1864	258	6.2715	-	-	-	-
0.1871	259	7.3575	-	-	-	-
0.1879	260	7.8890	-	-	-	-
0.1886	261	8.7971	-	-	-	-
0.1893	262	8.2353	-	-	-	-
0.1900	263	7.6893	-	-	-	-
0.1908	264	6.1541	-	-	-	-
0.1915	265	6.5112	-	-	-	-
0.1922	266	8.3594	-	-	-	-
0.1929	267	6.3791	-	-	-	-
0.1936	268	7.8584	-	-	-	-
0.1944	269	8.3215	-	-	-	-
0.1951	270	7.1491	-	-	-	-
0.1958	271	7.3401	-	-	-	-
0.1965	272	8.6963	-	-	-	-
0.1973	273	6.4441	-	-	-	-
0.1980	274	7.3672	-	-	-	-
0.1987	275	9.0571	-	-	-	-
0.1994	276	8.8935	-	-	-	-
0.2001	277	7.7856	-	-	-	-
0.2009	278	6.4330	-	-	-	-
0.2016	279	8.5934	-	-	-	-
0.2023	280	8.6859	-	-	-	-
0.2030	281	7.6245	-	-	-	-
0.2038	282	8.6283	-	-	-	-
0.2045	283	5.9387	-	-	-	-
0.2052	284	7.2245	-	-	-	-
0.2059	285	7.2952	-	-	-	-
0.2066	286	7.6956	-	-	-	-
0.2074	287	8.9979	-	-	-	-
0.2081	288	8.9997	-	-	-	-
0.2088	289	6.6219	-	-	-	-
0.2095	290	6.7169	-	-	-	-
0.2103	291	6.3436	-	-	-	-
0.2110	292	8.2345	-	-	-	-
0.2117	293	7.1488	-	-	-	-
0.2124	294	8.1586	-	-	-	-
0.2132	295	7.0979	-	-	-	-
0.2139	296	7.5586	-	-	-	-
0.2146	297	7.0977	-	-	-	-
0.2153	298	8.8792	-	-	-	-
0.2160	299	7.1341	-	-	-	-
0.2168	300	8.1652	0.7142	0.4247	0.6050	0.2661
0.2175	301	9.5709	-	-	-	-
0.2182	302	7.9504	-	-	-	-
0.2189	303	7.8892	-	-	-	-
0.2197	304	6.2182	-	-	-	-
0.2204	305	8.2730	-	-	-	-
0.2211	306	7.8237	-	-	-	-
0.2218	307	7.2448	-	-	-	-
0.2225	308	8.5649	-	-	-	-
0.2233	309	9.0163	-	-	-	-
0.2240	310	6.3014	-	-	-	-
0.2247	311	6.2124	-	-	-	-
0.2254	312	9.1291	-	-	-	-
0.2262	313	7.1985	-	-	-	-
0.2269	314	7.2764	-	-	-	-
0.2276	315	9.3452	-	-	-	-
0.2283	316	7.4676	-	-	-	-
0.2290	317	6.8079	-	-	-	-
0.2298	318	8.5742	-	-	-	-
0.2305	319	9.9193	-	-	-	-
0.2312	320	5.9858	-	-	-	-
0.2319	321	6.8497	-	-	-	-
0.2327	322	6.3486	-	-	-	-
0.2334	323	7.6203	-	-	-	-
0.2341	324	7.9722	-	-	-	-
0.2348	325	9.6823	-	-	-	-
0.2355	326	9.0439	-	-	-	-
0.2363	327	7.0939	-	-	-	-
0.2370	328	7.3688	-	-	-	-
0.2377	329	7.9133	-	-	-	-
0.2384	330	6.1136	-	-	-	-
0.2392	331	8.3561	-	-	-	-
0.2399	332	8.9160	-	-	-	-
0.2406	333	6.2242	-	-	-	-
0.2413	334	6.0803	-	-	-	-
0.2421	335	8.0006	-	-	-	-
0.2428	336	6.2775	-	-	-	-
0.2435	337	7.8405	-	-	-	-
0.2442	338	6.5061	-	-	-	-
0.2449	339	9.0701	-	-	-	-
0.2457	340	6.6981	-	-	-	-
0.2464	341	8.0019	-	-	-	-
0.2471	342	7.0216	-	-	-	-
0.2478	343	7.8473	-	-	-	-
0.2486	344	6.8347	-	-	-	-
0.2493	345	8.0829	-	-	-	-
0.25	346	6.3380	-	-	-	-
0.2507	347	7.2192	-	-	-	-
0.2514	348	6.3448	-	-	-	-
0.2522	349	6.6750	-	-	-	-
0.2529	350	5.9190	-	-	-	-
0.2536	351	8.1827	-	-	-	-
0.2543	352	7.2935	-	-	-	-
0.2551	353	6.7141	-	-	-	-
0.2558	354	5.8674	-	-	-	-
0.2565	355	5.9296	-	-	-	-
0.2572	356	8.4803	-	-	-	-
0.2579	357	6.2370	-	-	-	-
0.2587	358	6.8575	-	-	-	-
0.2594	359	6.6572	-	-	-	-
0.2601	360	6.9343	-	-	-	-
0.2608	361	6.2653	-	-	-	-
0.2616	362	7.9433	-	-	-	-
0.2623	363	6.0115	-	-	-	-
0.2630	364	6.8610	-	-	-	-
0.2637	365	7.1822	-	-	-	-
0.2645	366	9.3142	-	-	-	-
0.2652	367	5.7889	-	-	-	-
0.2659	368	6.8332	-	-	-	-
0.2666	369	6.8526	-	-	-	-
0.2673	370	8.2198	-	-	-	-
0.2681	371	8.3718	-	-	-	-
0.2688	372	5.9132	-	-	-	-
0.2695	373	6.7588	-	-	-	-
0.2702	374	5.7428	-	-	-	-
0.2710	375	8.9354	-	-	-	-
0.2717	376	7.0662	-	-	-	-
0.2724	377	8.1334	-	-	-	-
0.2731	378	7.8102	-	-	-	-
0.2738	379	6.6322	-	-	-	-
0.2746	380	5.8444	-	-	-	-
0.2753	381	7.1785	-	-	-	-
0.2760	382	7.4896	-	-	-	-
0.2767	383	7.2497	-	-	-	-
0.2775	384	6.1833	-	-	-	-
0.2782	385	7.2845	-	-	-	-
0.2789	386	6.0034	-	-	-	-
0.2796	387	8.7919	-	-	-	-
0.2803	388	9.1982	-	-	-	-
0.2811	389	7.7848	-	-	-	-
0.2818	390	9.0149	-	-	-	-
0.2825	391	8.3518	-	-	-	-
0.2832	392	9.0526	-	-	-	-
0.2840	393	8.0202	-	-	-	-
0.2847	394	8.0344	-	-	-	-
0.2854	395	8.3646	-	-	-	-
0.2861	396	8.6428	-	-	-	-
0.2868	397	6.3407	-	-	-	-
0.2876	398	5.8925	-	-	-	-
0.2883	399	6.7617	-	-	-	-
0.2890	400	6.7791	0.7207	0.4104	0.5994	0.2346
0.2897	401	7.5716	-	-	-	-
0.2905	402	7.6600	-	-	-	-
0.2912	403	8.8924	-	-	-	-
0.2919	404	7.6409	-	-	-	-
0.2926	405	7.7922	-	-	-	-
0.2934	406	6.3738	-	-	-	-
0.2941	407	8.0666	-	-	-	-
0.2948	408	7.5364	-	-	-	-
0.2955	409	6.6041	-	-	-	-
0.2962	410	5.6804	-	-	-	-
0.2970	411	6.6729	-	-	-	-
0.2977	412	7.0913	-	-	-	-
0.2984	413	8.3783	-	-	-	-
0.2991	414	6.8530	-	-	-	-
0.2999	415	6.3296	-	-	-	-
0.3006	416	6.3762	-	-	-	-
0.3013	417	6.2155	-	-	-	-
0.3020	418	6.4117	-	-	-	-
0.3027	419	6.6793	-	-	-	-
0.3035	420	6.8263	-	-	-	-
0.3042	421	6.4028	-	-	-	-
0.3049	422	7.7596	-	-	-	-
0.3056	423	7.4189	-	-	-	-
0.3064	424	6.7374	-	-	-	-
0.3071	425	6.8963	-	-	-	-
0.3078	426	8.3500	-	-	-	-
0.3085	427	5.7948	-	-	-	-
0.3092	428	7.2937	-	-	-	-
0.3100	429	6.9338	-	-	-	-
0.3107	430	6.3859	-	-	-	-
0.3114	431	7.5478	-	-	-	-
0.3121	432	7.5736	-	-	-	-
0.3129	433	6.6288	-	-	-	-
0.3136	434	5.3462	-	-	-	-
0.3143	435	6.2010	-	-	-	-
0.3150	436	7.5095	-	-	-	-
0.3158	437	7.4142	-	-	-	-
0.3165	438	5.7945	-	-	-	-
0.3172	439	7.7259	-	-	-	-
0.3179	440	7.8878	-	-	-	-
0.3186	441	6.6473	-	-	-	-
0.3194	442	6.7967	-	-	-	-
0.3201	443	7.4747	-	-	-	-
0.3208	444	6.2259	-	-	-	-
0.3215	445	7.1695	-	-	-	-
0.3223	446	6.0938	-	-	-	-
0.3230	447	7.9218	-	-	-	-
0.3237	448	6.5265	-	-	-	-
0.3244	449	6.8363	-	-	-	-
0.3251	450	7.8182	-	-	-	-
0.3259	451	6.9549	-	-	-	-
0.3266	452	8.2831	-	-	-	-
0.3273	453	7.4832	-	-	-	-
0.3280	454	5.9621	-	-	-	-
0.3288	455	7.9571	-	-	-	-
0.3295	456	7.0751	-	-	-	-
0.3302	457	6.0157	-	-	-	-
0.3309	458	7.7424	-	-	-	-
0.3316	459	7.0588	-	-	-	-
0.3324	460	7.3170	-	-	-	-
0.3331	461	8.6086	-	-	-	-
0.3338	462	7.9951	-	-	-	-
0.3345	463	7.0834	-	-	-	-
0.3353	464	6.4804	-	-	-	-
0.3360	465	7.5319	-	-	-	-
0.3367	466	6.5684	-	-	-	-
0.3374	467	6.6974	-	-	-	-
0.3382	468	8.6434	-	-	-	-
0.3389	469	7.0722	-	-	-	-
0.3396	470	8.8410	-	-	-	-
0.3403	471	7.2724	-	-	-	-
0.3410	472	7.0114	-	-	-	-
0.3418	473	6.3182	-	-	-	-
0.3425	474	6.6589	-	-	-	-
0.3432	475	5.9354	-	-	-	-
0.3439	476	7.3281	-	-	-	-
0.3447	477	5.8394	-	-	-	-
0.3454	478	6.7008	-	-	-	-
0.3461	479	7.3013	-	-	-	-
0.3468	480	8.3300	-	-	-	-
0.3475	481	7.1566	-	-	-	-
0.3483	482	6.7329	-	-	-	-
0.3490	483	6.9272	-	-	-	-
0.3497	484	7.1012	-	-	-	-
0.3504	485	5.7480	-	-	-	-
0.3512	486	7.5637	-	-	-	-
0.3519	487	6.2862	-	-	-	-
0.3526	488	7.3636	-	-	-	-
0.3533	489	5.5500	-	-	-	-
0.3540	490	7.7685	-	-	-	-
0.3548	491	7.4621	-	-	-	-
0.3555	492	5.8204	-	-	-	-
0.3562	493	7.7541	-	-	-	-
0.3569	494	7.7046	-	-	-	-
0.3577	495	7.1885	-	-	-	-
0.3584	496	7.4024	-	-	-	-
0.3591	497	7.1346	-	-	-	-
0.3598	498	7.1843	-	-	-	-
0.3605	499	6.4963	-	-	-	-
0.3613	500	7.4144	0.7201	0.4334	0.6164	0.2291
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0.9075	1256	6.6625	-	-	-	-
0.9082	1257	5.8114	-	-	-	-
0.9090	1258	7.4676	-	-	-	-
0.9097	1259	6.8997	-	-	-	-
0.9104	1260	5.9862	-	-	-	-
0.9111	1261	5.9442	-	-	-	-
0.9118	1262	6.6340	-	-	-	-
0.9126	1263	6.6736	-	-	-	-
0.9133	1264	7.2402	-	-	-	-
0.9140	1265	5.8728	-	-	-	-
0.9147	1266	6.8089	-	-	-	-
0.9155	1267	6.7254	-	-	-	-
0.9162	1268	7.1492	-	-	-	-
0.9169	1269	7.3898	-	-	-	-
0.9176	1270	6.1858	-	-	-	-
0.9184	1271	7.3701	-	-	-	-
0.9191	1272	6.5232	-	-	-	-
0.9198	1273	6.1980	-	-	-	-
0.9205	1274	5.5368	-	-	-	-
0.9212	1275	6.8263	-	-	-	-
0.9220	1276	5.8699	-	-	-	-
0.9227	1277	6.0835	-	-	-	-
0.9234	1278	6.7414	-	-	-	-
0.9241	1279	6.1632	-	-	-	-
0.9249	1280	5.3919	-	-	-	-
0.9256	1281	5.3254	-	-	-	-
0.9263	1282	6.8750	-	-	-	-
0.9270	1283	6.6326	-	-	-	-
0.9277	1284	6.2277	-	-	-	-
0.9285	1285	7.0646	-	-	-	-
0.9292	1286	5.2621	-	-	-	-
0.9299	1287	4.0181	-	-	-	-
0.9306	1288	6.5457	-	-	-	-
0.9314	1289	5.1115	-	-	-	-
0.9321	1290	4.9947	-	-	-	-
0.9328	1291	7.8172	-	-	-	-
0.9335	1292	6.9967	-	-	-	-
0.9342	1293	4.2306	-	-	-	-
0.9350	1294	6.4433	-	-	-	-
0.9357	1295	6.2437	-	-	-	-
0.9364	1296	6.6085	-	-	-	-
0.9371	1297	6.7630	-	-	-	-
0.9379	1298	6.5646	-	-	-	-
0.9386	1299	7.1181	-	-	-	-
0.9393	1300	6.6304	0.7348	0.4665	0.6414	0.2600
0.9400	1301	6.9020	-	-	-	-
0.9408	1302	7.2471	-	-	-	-
0.9415	1303	7.2700	-	-	-	-
0.9422	1304	7.2717	-	-	-	-
0.9429	1305	7.0717	-	-	-	-
0.9436	1306	5.5470	-	-	-	-
0.9444	1307	6.9274	-	-	-	-
0.9451	1308	5.7937	-	-	-	-
0.9458	1309	7.3228	-	-	-	-
0.9465	1310	7.1520	-	-	-	-
0.9473	1311	6.0217	-	-	-	-
0.9480	1312	5.7415	-	-	-	-
0.9487	1313	7.0669	-	-	-	-
0.9494	1314	6.5617	-	-	-	-
0.9501	1315	6.0856	-	-	-	-
0.9509	1316	7.2686	-	-	-	-
0.9516	1317	6.7960	-	-	-	-
0.9523	1318	5.8250	-	-	-	-
0.9530	1319	7.0301	-	-	-	-
0.9538	1320	6.0094	-	-	-	-
0.9545	1321	6.6463	-	-	-	-
0.9552	1322	4.9603	-	-	-	-
0.9559	1323	4.6620	-	-	-	-
0.9566	1324	6.6628	-	-	-	-
0.9574	1325	6.3963	-	-	-	-
0.9581	1326	6.0725	-	-	-	-
0.9588	1327	6.4144	-	-	-	-
0.9595	1328	8.0120	-	-	-	-
0.9603	1329	7.1712	-	-	-	-
0.9610	1330	5.5660	-	-	-	-
0.9617	1331	5.3600	-	-	-	-
0.9624	1332	5.9571	-	-	-	-
0.9632	1333	5.0757	-	-	-	-
0.9639	1334	6.6106	-	-	-	-
0.9646	1335	6.4320	-	-	-	-
0.9653	1336	5.9044	-	-	-	-
0.9660	1337	6.4515	-	-	-	-
0.9668	1338	6.2933	-	-	-	-
0.9675	1339	6.8401	-	-	-	-
0.9682	1340	4.3254	-	-	-	-
0.9689	1341	4.2630	-	-	-	-
0.9697	1342	5.1197	-	-	-	-
0.9704	1343	4.5743	-	-	-	-
0.9711	1344	4.3811	-	-	-	-
0.9718	1345	4.0961	-	-	-	-
0.9725	1346	4.2583	-	-	-	-
0.9733	1347	4.3204	-	-	-	-
0.9740	1348	4.5999	-	-	-	-
0.9747	1349	4.3744	-	-	-	-
0.9754	1350	4.1518	-	-	-	-
0.9762	1351	4.4905	-	-	-	-
0.9769	1352	4.5970	-	-	-	-
0.9776	1353	4.5256	-	-	-	-
0.9783	1354	4.2960	-	-	-	-
0.9790	1355	5.0964	-	-	-	-
0.9798	1356	4.6103	-	-	-	-
0.9805	1357	4.6055	-	-	-	-
-1	-1	-	0.7359	0.4702	0.6415	0.2591

Training Time

• Training: 6.0 days
• Evaluation: 1.6 hours
• Total: 6.1 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}
}