Detomo/cl-nagoya-sup-simcse-ja-for-standard-name-v0_9_7

SentenceTransformer based on cl-nagoya/sup-simcse-ja-base

This is a sentence-transformers model finetuned from cl-nagoya/sup-simcse-ja-base. It maps sentences & paragraphs to a 768-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: cl-nagoya/sup-simcse-ja-base
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
)

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("Detomo/cl-nagoya-sup-simcse-ja-for-standard-name-v0_9_7")
# Run inference
sentences = [
    '科目:ﾀｲﾙ｡名称:床磁器質ﾀｲﾙ｡',
    '科目:ﾕﾆｯﾄ及びその他｡名称:#救助袋ｻｲﾝ(ｶﾞﾗｽ面)｡',
    '科目:ﾕﾆｯﾄ及びその他｡名称:案内ｽﾀﾝﾄﾞｻｲﾝ｡',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

• Size: 9,054 training samples
• Columns: sentence and label
• Approximate statistics based on the first 1000 samples:

  	sentence	label
  type	string	int
  details	min: 11 tokens mean: 17.78 tokens max: 32 tokens	0: ~0.20% 1: ~0.30% 2: ~0.30% 3: ~0.30% 4: ~0.20% 5: ~0.20% 6: ~0.20% 7: ~0.20% 8: ~0.20% 9: ~0.20% 10: ~0.30% 11: ~0.20% 12: ~0.20% 13: ~0.20% 14: ~0.20% 15: ~0.20% 16: ~0.20% 17: ~0.40% 18: ~0.20% 19: ~0.20% 20: ~0.20% 21: ~0.20% 22: ~0.20% 23: ~0.20% 24: ~0.20% 25: ~0.20% 26: ~0.20% 27: ~0.20% 28: ~0.20% 29: ~0.20% 30: ~0.20% 31: ~0.20% 32: ~0.20% 33: ~0.20% 34: ~0.20% 35: ~0.20% 36: ~0.20% 37: ~0.20% 38: ~0.20% 39: ~0.20% 40: ~0.20% 41: ~0.20% 42: ~0.20% 43: ~0.60% 44: ~0.70% 45: ~0.20% 46: ~0.20% 47: ~0.20% 48: ~0.20% 49: ~0.20% 50: ~0.30% 51: ~0.20% 52: ~0.20% 53: ~0.20% 54: ~0.20% 55: ~0.30% 56: ~0.40% 57: ~0.30% 58: ~0.20% 59: ~0.20% 60: ~0.20% 61: ~0.20% 62: ~0.20% 63: ~0.30% 64: ~0.20% 65: ~0.20% 66: ~0.20% 67: ~0.20% 68: ~0.40% 69: ~0.40% 70: ~0.20% 71: ~0.60% 72: ~0.20% 73: ~0.20% 74: ~0.20% 75: ~0.20% 76: ~0.20% 77: ~0.30% 78: ~0.20% 79: ~0.40% 80: ~0.20% 81: ~0.20% 82: ~0.50% 83: ~0.30% 84: ~0.60% 85: ~0.20% 86: ~0.30% 87: ~0.20% 88: ~0.20% 89: ~0.20% 90: ~0.20% 91: ~1.10% 92: ~1.70% 93: ~2.20% 94: ~0.50% 95: ~0.20% 96: ~0.20% 97: ~1.50% 98: ~0.20% 99: ~0.20% 100: ~0.20% 101: ~0.20% 102: ~0.40% 103: ~1.60% 104: ~0.20% 105: ~0.20% 106: ~0.40% 107: ~0.40% 108: ~0.20% 109: ~0.20% 110: ~0.20% 111: ~1.10% 112: ~0.20% 113: ~0.50% 114: ~0.50% 115: ~0.20% 116: ~0.20% 117: ~0.20% 118: ~0.20% 119: ~0.50% 120: ~0.20% 121: ~0.20% 122: ~0.20% 123: ~0.20% 124: ~0.20% 125: ~0.20% 126: ~0.30% 127: ~0.20% 128: ~0.20% 129: ~0.20% 130: ~0.50% 131: ~0.20% 132: ~0.20% 133: ~0.20% 134: ~0.20% 135: ~0.20% 136: ~0.20% 137: ~0.20% 138: ~0.30% 139: ~0.70% 140: ~0.20% 141: ~1.80% 142: ~0.20% 143: ~1.70% 144: ~0.30% 145: ~0.30% 146: ~0.50% 147: ~0.50% 148: ~0.50% 149: ~0.30% 150: ~0.20% 151: ~0.20% 152: ~0.20% 153: ~0.20% 154: ~0.20% 155: ~0.20% 156: ~0.20% 157: ~0.20% 158: ~0.20% 159: ~0.20% 160: ~0.20% 161: ~0.20% 162: ~0.40% 163: ~0.20% 164: ~0.20% 165: ~0.20% 166: ~0.20% 167: ~0.20% 168: ~0.20% 169: ~0.30% 170: ~0.30% 171: ~0.20% 172: ~0.20% 173: ~0.20% 174: ~0.20% 175: ~0.20% 176: ~0.60% 177: ~0.20% 178: ~0.20% 179: ~0.20% 180: ~0.20% 181: ~0.20% 182: ~0.40% 183: ~0.20% 184: ~0.20% 185: ~0.30% 186: ~0.20% 187: ~0.90% 188: ~0.30% 189: ~0.30% 190: ~0.20% 191: ~0.30% 192: ~0.20% 193: ~0.80% 194: ~0.20% 195: ~0.30% 196: ~0.20% 197: ~0.20% 198: ~0.20% 199: ~0.20% 200: ~0.20% 201: ~1.20% 202: ~0.40% 203: ~0.20% 204: ~0.20% 205: ~0.20% 206: ~0.20% 207: ~1.00% 208: ~0.20% 209: ~0.30% 210: ~0.20% 211: ~1.10% 212: ~0.30% 213: ~0.20% 214: ~0.20% 215: ~0.20% 216: ~0.20% 217: ~0.20% 218: ~0.20% 219: ~0.20% 220: ~0.30% 221: ~0.20% 222: ~0.90% 223: ~4.70% 224: ~0.20% 225: ~0.20% 226: ~0.20% 227: ~0.70% 228: ~0.20% 229: ~0.80% 230: ~0.20% 231: ~0.40% 232: ~0.30% 233: ~0.40% 234: ~0.20% 235: ~0.30% 236: ~0.50% 237: ~0.30% 238: ~0.20% 239: ~0.20% 240: ~0.30% 241: ~0.30% 242: ~0.30% 243: ~0.60% 244: ~0.20% 245: ~0.20% 246: ~0.20% 247: ~0.30% 248: ~0.20% 249: ~1.90% 250: ~0.20% 251: ~0.20% 252: ~0.20% 253: ~0.20% 254: ~0.20% 255: ~0.50% 256: ~0.20% 257: ~0.30% 258: ~0.20% 259: ~0.20% 260: ~1.00% 261: ~0.20% 262: ~0.20% 263: ~0.20% 264: ~0.40% 265: ~0.20% 266: ~0.20% 267: ~0.20% 268: ~0.20% 269: ~0.20% 270: ~0.20% 271: ~0.20% 272: ~3.60% 273: ~0.20% 274: ~0.20% 275: ~0.40% 276: ~0.20% 277: ~0.20% 278: ~0.90% 279: ~0.40% 280: ~0.20% 281: ~2.30% 282: ~0.30% 283: ~0.20% 284: ~0.10%

• Samples:

  sentence	label
  科目:ｺﾝｸﾘｰﾄ｡名称:免震基礎天端ｸﾞﾗｳﾄ注入｡	0
  科目:ｺﾝｸﾘｰﾄ｡名称:免震基礎天端ｸﾞﾗｳﾄ注入｡	0
  科目:ｺﾝｸﾘｰﾄ｡名称:ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送｡	1

• Loss: BatchAllTripletLoss

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 512
• per_device_eval_batch_size: 512
• learning_rate: 1e-05
• weight_decay: 0.01
• num_train_epochs: 200
• warmup_ratio: 0.1
• fp16: True
• batch_sampler: group_by_label

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 512
• per_device_eval_batch_size: 512
• 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: 1e-05
• weight_decay: 0.01
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 200
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• 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: 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}
• 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: None
• hub_always_push: False
• 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
• 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
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: group_by_label
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss
2.8889	50	0.7963
5.8333	100	0.7067
8.7778	150	0.6532
11.7222	200	0.6806
14.6667	250	0.652
17.6111	300	0.6508
20.5556	350	0.6566
23.5	400	0.6237
26.4444	450	0.6363
29.3889	500	0.6554
32.3333	550	0.6007
35.2778	600	0.6016
38.2222	650	0.5687
2.8889	50	0.5655
5.8333	100	0.6139
8.7778	150	0.514
11.7222	200	0.5867
14.6667	250	0.5699
17.6111	300	0.5472
20.5556	350	0.5793
23.5	400	0.5196
26.4444	450	0.5572
29.3889	500	0.5279
32.3333	550	0.5095
35.2778	600	0.4488
38.2222	650	0.4189
41.1667	700	0.5164
44.1111	750	0.591
47.0556	800	0.52
49.9444	850	0.5235
52.8889	900	0.5317
55.8333	950	0.5517
58.7778	1000	0.5618
61.7222	1050	0.5318
64.6667	1100	0.4685
67.6111	1150	0.4836
70.5556	1200	0.5426
73.5	1250	0.5356
76.4444	1300	0.4231
79.3889	1350	0.5104
82.3333	1400	0.4944
85.2778	1450	0.5301
88.2222	1500	0.4499
91.1667	1550	0.4745
94.1111	1600	0.4432
97.0556	1650	0.3892
99.9444	1700	0.4429
102.8889	1750	0.4973
105.8333	1800	0.5222
108.7778	1850	0.4502
111.7222	1900	0.4073
114.6667	1950	0.408
117.6111	2000	0.403
120.5556	2050	0.4122
123.5	2100	0.4357
126.4444	2150	0.4765
129.3889	2200	0.4069
132.3333	2250	0.388
135.2778	2300	0.341
138.2222	2350	0.333
141.1667	2400	0.4587
144.1111	2450	0.355
147.0556	2500	0.3552
149.9444	2550	0.3804
152.8889	2600	0.3692
155.8333	2650	0.3367
158.7778	2700	0.3662
161.7222	2750	0.3089
164.6667	2800	0.3016
167.6111	2850	0.3252
170.5556	2900	0.3409
173.5	2950	0.3128
176.4444	3000	0.3287
179.3889	3050	0.3148
182.3333	3100	0.3843
185.2778	3150	0.2281
188.2222	3200	0.2973
191.1667	3250	0.2891
194.1111	3300	0.3623
197.0556	3350	0.3626
199.9444	3400	0.2931
202.8889	3450	0.2755
205.8333	3500	0.2849
208.7778	3550	0.2608
211.7222	3600	0.3081
214.6667	3650	0.2724
217.6111	3700	0.2583
220.5556	3750	0.3132
223.5	3800	0.196
226.4444	3850	0.2554
229.3889	3900	0.2
232.3333	3950	0.2936
235.2778	4000	0.2326
238.2222	4050	0.2031
241.1667	4100	0.2492
244.1111	4150	0.2234
247.0556	4200	0.3034
249.9444	4250	0.2325
252.8889	4300	0.2453
255.8333	4350	0.2848
258.7778	4400	0.2447
261.7222	4450	0.2599
264.6667	4500	0.2073
267.6111	4550	0.2134
270.5556	4600	0.1886
273.5	4650	0.1229
276.4444	4700	0.2147
279.3889	4750	0.1993
282.3333	4800	0.1814
285.2778	4850	0.202
288.2222	4900	0.1947
291.1667	4950	0.14
294.1111	5000	0.2394
297.0556	5050	0.1798
299.9444	5100	0.1534
302.8889	5150	0.2622
305.8333	5200	0.1636
308.7778	5250	0.1966
311.7222	5300	0.1365
314.6667	5350	0.1501
317.6111	5400	0.1494
320.5556	5450	0.1341
323.5	5500	0.1791
326.4444	5550	0.1609
329.3889	5600	0.2268
332.3333	5650	0.2145
335.2778	5700	0.095
338.2222	5750	0.1161
341.1667	5800	0.1615
344.1111	5850	0.1261
347.0556	5900	0.2022
349.9444	5950	0.1503
352.8889	6000	0.1473
355.8333	6050	0.1703
358.7778	6100	0.1441
361.7222	6150	0.1439
364.6667	6200	0.1192
367.6111	6250	0.1312
370.5556	6300	0.0933
373.5	6350	0.1281
376.4444	6400	0.1516
379.3889	6450	0.1819
382.3333	6500	0.1877
385.2778	6550	0.1372
388.2222	6600	0.1551
391.1667	6650	0.1343
394.1111	6700	0.2394
397.0556	6750	0.1882
399.9444	6800	0.1786
402.8889	6850	0.125
405.8333	6900	0.1059
408.7778	6950	0.1414
411.7222	7000	0.0593
414.6667	7050	0.1037
417.6111	7100	0.098
420.5556	7150	0.1457
423.5	7200	0.1193
426.4444	7250	0.1061
429.3889	7300	0.1305
432.3333	7350	0.1416
435.2778	7400	0.1117
438.2222	7450	0.1003
441.1667	7500	0.1217
444.1111	7550	0.0872
447.0556	7600	0.1219
449.9444	7650	0.1061
452.8889	7700	0.1559
455.8333	7750	0.1599
458.7778	7800	0.1436
461.7222	7850	0.1207
464.6667	7900	0.1272
467.6111	7950	0.1048
470.5556	8000	0.1216
473.5	8050	0.133
476.4444	8100	0.0971
479.3889	8150	0.154
482.3333	8200	0.0697
485.2778	8250	0.136
488.2222	8300	0.1315
491.1667	8350	0.1103
494.1111	8400	0.1065
497.0556	8450	0.0784
499.9444	8500	0.134
2.8889	50	0.0581
5.8333	100	0.0804
8.7778	150	0.1214
11.7222	200	0.0513
14.6667	250	0.0923
17.6111	300	0.1311
20.5556	350	0.0714
23.5	400	0.1101
26.4444	450	0.1146
29.3889	500	0.0916
32.3333	550	0.1554
35.2778	600	0.1609
38.2222	650	0.1276
41.1667	700	0.1207
44.1111	750	0.1132
47.0556	800	0.1013
49.9444	850	0.1374
52.8889	900	0.1983
55.8333	950	0.165
58.7778	1000	0.1318
61.7222	1050	0.1405
64.6667	1100	0.1987
67.6111	1150	0.1422
70.5556	1200	0.082
73.5	1250	0.1607
76.4444	1300	0.1584
79.3889	1350	0.1337
82.3333	1400	0.1547
85.2778	1450	0.1706
88.2222	1500	0.1326
91.1667	1550	0.1441
94.1111	1600	0.2742
97.0556	1650	0.1885
99.9444	1700	0.1234
102.8889	1750	0.1107
105.8333	1800	0.1442
108.7778	1850	0.183
111.7222	1900	0.1245
114.6667	1950	0.1099
117.6111	2000	0.0903
120.5556	2050	0.1725
123.5	2100	0.1066
126.4444	2150	0.2075
129.3889	2200	0.1289
132.3333	2250	0.1013
135.2778	2300	0.1284
138.2222	2350	0.0915
141.1667	2400	0.105
144.1111	2450	0.143
147.0556	2500	0.0864
149.9444	2550	0.1254
152.8889	2600	0.172
155.8333	2650	0.0754
158.7778	2700	0.1027
161.7222	2750	0.1046
164.6667	2800	0.1141
167.6111	2850	0.11
170.5556	2900	0.1139
173.5	2950	0.1151
176.4444	3000	0.0967
179.3889	3050	0.155
182.3333	3100	0.1213
185.2778	3150	0.0937
188.2222	3200	0.1073
191.1667	3250	0.0971
194.1111	3300	0.1513
197.0556	3350	0.103
199.9444	3400	0.1429
202.8889	3450	0.1216
205.8333	3500	0.1303
208.7778	3550	0.083
211.7222	3600	0.0731
214.6667	3650	0.0696
217.6111	3700	0.05
220.5556	3750	0.0824
223.5	3800	0.0483
226.4444	3850	0.0994
229.3889	3900	0.1145
232.3333	3950	0.0616
235.2778	4000	0.0967
238.2222	4050	0.0927
241.1667	4100	0.0531
244.1111	4150	0.0681
247.0556	4200	0.1337
249.9444	4250	0.0586
252.8889	4300	0.1086
255.8333	4350	0.126
258.7778	4400	0.0678
261.7222	4450	0.0651
264.6667	4500	0.0352
267.6111	4550	0.0193
270.5556	4600	0.0517
273.5	4650	0.0617
276.4444	4700	0.0679
279.3889	4750	0.1138
282.3333	4800	0.0617
285.2778	4850	0.118
288.2222	4900	0.1163
291.1667	4950	0.1245
294.1111	5000	0.0556
297.0556	5050	0.04
299.9444	5100	0.0655
302.8889	5150	0.0217
305.8333	5200	0.0486
308.7778	5250	0.1352
311.7222	5300	0.0271
314.6667	5350	0.0747
317.6111	5400	0.008
320.5556	5450	0.0329
323.5	5500	0.0899
326.4444	5550	0.0808
329.3889	5600	0.05
332.3333	5650	0.0759
335.2778	5700	0.0614
338.2222	5750	0.0446
341.1667	5800	0.0516
344.1111	5850	0.0774
347.0556	5900	0.0238
349.9444	5950	0.0753
352.8889	6000	0.0983
355.8333	6050	0.1169
358.7778	6100	0.0832
361.7222	6150	0.0995
364.6667	6200	0.0373
367.6111	6250	0.0629
370.5556	6300	0.0508
373.5	6350	0.086
376.4444	6400	0.0953
379.3889	6450	0.077
382.3333	6500	0.0506
385.2778	6550	0.0654
388.2222	6600	0.0086
391.1667	6650	0.093
394.1111	6700	0.0363
397.0556	6750	0.0165
399.9444	6800	0.0325
402.8889	6850	0.0182
405.8333	6900	0.0386
408.7778	6950	0.0362
411.7222	7000	0.0231
414.6667	7050	0.0136
417.6111	7100	0.0603
420.5556	7150	0.035
423.5	7200	0.039
426.4444	7250	0.0657
429.3889	7300	0.026
432.3333	7350	0.0114
435.2778	7400	0.0348
438.2222	7450	0.0099
441.1667	7500	0.0338
444.1111	7550	0.0283
447.0556	7600	0.0256
449.9444	7650	0.0399
452.8889	7700	0.0471
455.8333	7750	0.0309
458.7778	7800	0.0371
461.7222	7850	0.0139
464.6667	7900	0.0281
467.6111	7950	0.0665
470.5556	8000	0.0612
473.5	8050	0.0872
476.4444	8100	0.0235
479.3889	8150	0.0504
482.3333	8200	0.0398
485.2778	8250	0.0517
488.2222	8300	0.0515
491.1667	8350	0.0072
494.1111	8400	0.0388
497.0556	8450	0.0679
499.9444	8500	0.043
2.8889	50	0.0756
5.8333	100	0.0985
8.7778	150	0.0746
11.7222	200	0.0865
14.6667	250	0.0505
17.6111	300	0.0577
20.5556	350	0.0895
23.5	400	0.0703
26.4444	450	0.0489
29.3889	500	0.0791
32.3333	550	0.0442
35.2778	600	0.0811
38.2222	650	0.0992
41.1667	700	0.1011
44.1111	750	0.1014
47.0556	800	0.0595
49.9444	850	0.1338
52.8889	900	0.0429
55.8333	950	0.0957
58.7778	1000	0.064
61.7222	1050	0.112
64.6667	1100	0.0484
67.6111	1150	0.0395
70.5556	1200	0.0474
73.5	1250	0.0605
76.4444	1300	0.0384
79.3889	1350	0.0782
82.3333	1400	0.0289
85.2778	1450	0.0069
88.2222	1500	0.0396
91.1667	1550	0.0412
94.1111	1600	0.036
97.0556	1650	0.1083
99.9444	1700	0.0171
2.8889	50	0.1652
5.8333	100	0.1102
8.7778	150	0.0945
11.7222	200	0.072
14.6667	250	0.0526
17.6111	300	0.0383
20.5556	350	0.0719
23.5	400	0.0765
26.4444	450	0.1147
29.3889	500	0.1828
32.3333	550	0.0692
35.2778	600	0.0787
38.2222	650	0.1025
41.1667	700	0.0358
44.1111	750	0.0197
47.0556	800	0.0261
49.9444	850	0.0198
52.8889	900	0.0772
55.8333	950	0.0757
58.7778	1000	0.038
61.7222	1050	0.0319
64.6667	1100	0.0304
67.6111	1150	0.0543
70.5556	1200	0.029
73.5	1250	0.0145
76.4444	1300	0.0414
79.3889	1350	0.0862
82.3333	1400	0.0553
85.2778	1450	0.0735
88.2222	1500	0.033
91.1667	1550	0.0218
94.1111	1600	0.0695
97.0556	1650	0.0375
99.9444	1700	0.0324
102.8889	1750	0.0408
105.8333	1800	0.0321
108.7778	1850	0.064
111.7222	1900	0.0547
114.6667	1950	0.0201
117.6111	2000	0.0191
120.5556	2050	0.029
123.5	2100	0.0139
126.4444	2150	0.0267
129.3889	2200	0.0128
132.3333	2250	0.0271
135.2778	2300	0.0203
138.2222	2350	0.0165
141.1667	2400	0.0156
144.1111	2450	0.0097
147.0556	2500	0.029
149.9444	2550	0.0236
152.8889	2600	0.0338
155.8333	2650	0.0226
158.7778	2700	0.0268
161.7222	2750	0.0343
164.6667	2800	0.0482
167.6111	2850	0.0201
170.5556	2900	0.0094
173.5	2950	0.0316
176.4444	3000	0.0132
179.3889	3050	0.0218
182.3333	3100	0.0134
185.2778	3150	0.0208
188.2222	3200	0.0217
191.1667	3250	0.0276
194.1111	3300	0.0067
197.0556	3350	0.015
199.9444	3400	0.01
2.8889	50	0.0623
5.8333	100	0.0555
8.7778	150	0.006
11.7222	200	0.0246
14.6667	250	0.0237
17.6111	300	0.0492
20.5556	350	0.0215
23.5	400	0.0823
26.4444	450	0.0542
29.3889	500	0.033
32.3333	550	0.0438
35.2778	600	0.0647
38.2222	650	0.0759
41.1667	700	0.0464
44.1111	750	0.0994
47.0556	800	0.0564
49.9444	850	0.0591
52.8889	900	0.0912
55.8333	950	0.0557
58.7778	1000	0.0747
61.7222	1050	0.0413
64.6667	1100	0.0384
67.6111	1150	0.083
70.5556	1200	0.0281
73.5	1250	0.0145
76.4444	1300	0.053
79.3889	1350	0.0523
82.3333	1400	0.0407
85.2778	1450	0.0588
88.2222	1500	0.0531
91.1667	1550	0.0146
94.1111	1600	0.0094
97.0556	1650	0.04
99.9444	1700	0.0129
102.8889	1750	0.0391
105.8333	1800	0.0071
108.7778	1850	0.0429
111.7222	1900	0.0454
114.6667	1950	0.0507
117.6111	2000	0.0183
120.5556	2050	0.0398
123.5	2100	0.0117
126.4444	2150	0.023
129.3889	2200	0.0319
132.3333	2250	0.0837
135.2778	2300	0.001
138.2222	2350	0.0114
141.1667	2400	0.0445
144.1111	2450	0.0182
147.0556	2500	0.043
149.9444	2550	0.0317
152.8889	2600	0.0429
155.8333	2650	0.0115
158.7778	2700	0.0077
161.7222	2750	0.0122
164.6667	2800	0.0269
167.6111	2850	0.0061
170.5556	2900	0.0163
173.5	2950	0.0113
176.4444	3000	0.0176
179.3889	3050	0.0
182.3333	3100	0.0397
185.2778	3150	0.0196
188.2222	3200	0.0148
191.1667	3250	0.0084
194.1111	3300	0.0051
197.0556	3350	0.0301
199.9444	3400	0.0049

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 3.4.1
• Transformers: 4.49.0
• PyTorch: 2.6.0+cu124
• Accelerate: 1.5.2
• Datasets: 3.4.1
• Tokenizers: 0.21.1

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",
}

BatchAllTripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}