all-MiniLM-L6-v36-pair_score

This is a sentence-transformers model finetuned from KhaledReda/all-MiniLM-L6-v35-pair_score on the pairs_with_scores_v30 dataset. It maps sentences & paragraphs to a 384-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: KhaledReda/all-MiniLM-L6-v35-pair_score
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • pairs_with_scores_v30
• Language: en
• 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({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'sanosan',
    'v-neck cotton vest black category fashion designer wear outerwear vest tags classic suit vest buttonup front pockets vest false welt pockets vest adjustable strap vest cotton vest vest vneck vest gilet vneck gilet keywords vest vneck vest gilet vneck gilet attrs gender women brand palma generic name vest size s features adjustable strap with buckle types of fashion styles classic formal collar v-neck closure style button-up pocket style false welt pockets sleeve style sleeveless material cotton color black fashion style v-neck description our v-neck cotton vest inspired by classic suit vests offers versatility and style. the button-up front and false welt pockets on the front add a touch of elegance while the adjustable strap with a buckle on the back ensures comfort and customization. whether for formal occasions or adding a polished touch to your everyday attire this refined cotton vest is a stylish choice and a perfect addition to your wardrobe. material 100 cotton. model is wearing size m.',
    'lash brow category beauty skincare eye treatment eye treatment tags lashes growth serum brow serum lash serum keywords brow serum lash serum description this natural growth serum causes existing lashes to become longer and stimulates growth in hair follicles not currently producing lashes. it has shown effects after 4 weeks of usage which is why this product is considered to be highly effective.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, -0.0603, -0.1349],
#         [-0.0603,  1.0000, -0.0353],
#         [-0.1349, -0.0353,  1.0000]])

Training Details

Training Dataset

pairs_with_scores_v30

• Dataset: pairs_with_scores_v30 at 33eac44
• Size: 19,759,758 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 3 tokens mean: 5.54 tokens max: 20 tokens	min: 13 tokens mean: 86.78 tokens max: 256 tokens	min: 0.0 mean: 0.04 max: 1.0

• Samples:

  sentence1	sentence2	score
  boots 3780 nort	mediterranean blue mug category home and garden drinkware mug mug tags blue mug porcelain mug mediterranean mug mug keywords mediterranean mug mug attrs brand inches home generic name mug product name mediterranean measurements 350 ml number of pieces 1 shape mug purpose drinking material porcelain color blue description embrace the warmth and affection with mediterranean blue mug that makes your heartwarming moments even more special. product specifications material high quality porcelain.	0.0
  men shorts	ardell natural lashes black /125 category beauty cosmetics eye make-up eyelash tags ardell ardell lashes ardell natural lashes natural lashes keywords ardell ardell lashes ardell natural lashes natural lashes attrs color black	0.0
  boots 2316 b-16-j	fresh iced pink grapefruit juice category restaurants juices and drinks beverage juice tags fresh iced juice fresh juice iced juice juice pink grapefruit juice keywords fresh juice iced juice juice pink grapefruit juice	0.0

• Loss: CoSENTLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Evaluation Dataset

pairs_with_scores_v30

• Dataset: pairs_with_scores_v30 at 33eac44
• Size: 99,296 evaluation samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 3 tokens mean: 5.33 tokens max: 19 tokens	min: 16 tokens mean: 86.78 tokens max: 256 tokens	min: 0.0 mean: 0.05 max: 1.0

• Samples:

  sentence1	sentence2	score
  accuchek guide me	bright peach cover up category fashion swimwear bathing cover bathing cover tags peach cover up cover-up bright cover up cover up keywords bright cover up cover up attrs gender women brand pepla generic name cover up color bright peach	0.0
  medical accessory	ferragamo for men edt 100 ml category beauty fragrances perfume perfume tags men eau de toilette fragrances eau de toilette ferragamo ferragamo eau de toilette perfume cologne fragrance keywords eau de toilette ferragamo ferragamo eau de toilette perfume cologne fragrance attrs units 100 millilitre target group men	0.0
  muslin kimono	fake date cookies category restaurants keto sweet biscuit tags coconut flour cookies grass fed butter cookies ghee cookies cinnamon powder cookies raw cacao cookies sesame cookies low carb cookies diabetic friendly cookies keto friendly cookies gluten free cookies almond flour cookies cookies date cookies fake date cookies keywords almond flour cookies cookies date cookies fake date cookies description almond flour - coconut flour - grass fed butter - ghee - egg - cinnamon powder - raw cacao - sesame nutritional facts per 10 gr 58 kcal - 4.6 gr fat - 1 gr net carb - 1.2 gr fiber - 1.9 gr protein suitable for low carb - diabetic friendly - keto friendly - gluten free 14 day on shelf air tight container .	0.0

• Loss: CoSENTLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• fp16: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• 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: 2e-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: 1
• 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
• hub_revision: None
• 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
• 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
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0006	100	0.0014
0.0013	200	0.0019
0.0019	300	0.0014
0.0026	400	0.001
0.0032	500	0.0017
0.0039	600	0.0012
0.0045	700	0.0027
0.0052	800	0.001
0.0058	900	0.0065
0.0065	1000	0.0028
0.0071	1100	0.0014
0.0078	1200	0.0012
0.0084	1300	0.0106
0.0091	1400	0.0015
0.0097	1500	0.0014
0.0104	1600	0.0013
0.0110	1700	0.0365
0.0117	1800	0.0024
0.0123	1900	0.0007
0.0130	2000	0.0174
0.0136	2100	0.0019
0.0143	2200	0.0141
0.0149	2300	0.0012
0.0155	2400	0.001
0.0162	2500	0.0009
0.0168	2600	0.0015
0.0175	2700	0.001
0.0181	2800	0.0014
0.0188	2900	0.0094
0.0194	3000	0.0009
0.0201	3100	0.0015
0.0207	3200	0.0016
0.0214	3300	0.0106
0.0220	3400	0.0011
0.0227	3500	0.0019
0.0233	3600	0.0007
0.0240	3700	0.0029
0.0246	3800	0.0013
0.0253	3900	0.0013
0.0259	4000	0.0063
0.0266	4100	0.0026
0.0272	4200	0.0013
0.0279	4300	0.0016
0.0285	4400	0.0013
0.0291	4500	0.0013
0.0298	4600	0.0011
0.0304	4700	0.0076
0.0311	4800	0.0015
0.0317	4900	0.0013
0.0324	5000	0.0039
0.0330	5100	0.0007
0.0337	5200	0.0012
0.0343	5300	0.003
0.0350	5400	0.0017
0.0356	5500	0.0016
0.0363	5600	0.001
0.0369	5700	0.0009
0.0376	5800	0.0014
0.0382	5900	0.0013
0.0389	6000	0.0023
0.0395	6100	0.001
0.0402	6200	0.005
0.0408	6300	0.0017
0.0415	6400	0.0012
0.0421	6500	0.0007
0.0428	6600	0.001
0.0434	6700	0.0008
0.0440	6800	0.0033
0.0447	6900	0.0006
0.0453	7000	0.001
0.0460	7100	0.0011
0.0466	7200	0.002
0.0473	7300	0.002
0.0479	7400	0.0008
0.0486	7500	0.0094
0.0492	7600	0.001
0.0499	7700	0.0013
0.0505	7800	0.0007
0.0512	7900	0.0016
0.0518	8000	0.0015
0.0525	8100	0.0018
0.0531	8200	0.0011
0.0538	8300	0.0015
0.0544	8400	0.0013
0.0551	8500	0.0013
0.0557	8600	0.0009
0.0564	8700	0.0008
0.0570	8800	0.0011
0.0577	8900	0.0008
0.0583	9000	0.0022
0.0589	9100	0.0013
0.0596	9200	0.0009
0.0602	9300	0.002
0.0609	9400	0.0016
0.0615	9500	0.0023
0.0622	9600	0.0007
0.0628	9700	0.001
0.0635	9800	0.0007
0.0641	9900	0.0056
0.0648	10000	0.0027
0.0654	10100	0.0032
0.0661	10200	0.0019
0.0667	10300	0.0007
0.0674	10400	0.0012
0.0680	10500	0.0009
0.0687	10600	0.0052
0.0693	10700	0.0017
0.0700	10800	0.0014
0.0706	10900	0.0009
0.0713	11000	0.0006
0.0719	11100	0.0008
0.0726	11200	0.0014
0.0732	11300	0.0011
0.0738	11400	0.0027
0.0745	11500	0.0012
0.0751	11600	0.0014
0.0758	11700	0.001
0.0764	11800	0.002
0.0771	11900	0.0008
0.0777	12000	0.0047
0.0784	12100	0.0013
0.0790	12200	0.0013
0.0797	12300	0.001
0.0803	12400	0.001
0.0810	12500	0.001
0.0816	12600	0.0013
0.0823	12700	0.001
0.0829	12800	0.0041
0.0836	12900	0.0017
0.0842	13000	0.0008
0.0849	13100	0.0019
0.0855	13200	0.0013
0.0862	13300	0.0015
0.0868	13400	0.002
0.0874	13500	0.001
0.0881	13600	0.0015
0.0887	13700	0.0012
0.0894	13800	0.0024
0.0900	13900	0.011
0.0907	14000	0.0007
0.0913	14100	0.0471
0.0920	14200	0.0008
0.0926	14300	0.0008
0.0933	14400	0.0007
0.0939	14500	0.0004
0.0946	14600	0.0073
0.0952	14700	0.0014
0.0959	14800	0.0006
0.0965	14900	0.0011
0.0972	15000	0.0007
0.0978	15100	0.0009
0.0985	15200	0.0008
0.0991	15300	0.0017
0.0998	15400	0.0024
0.1004	15500	0.0011
0.1011	15600	0.0021
0.1017	15700	0.001
0.1023	15800	0.0013
0.1030	15900	0.0013
0.1036	16000	0.0007
0.1043	16100	0.0008
0.1049	16200	0.0016
0.1056	16300	0.0008
0.1062	16400	0.0171
0.1069	16500	0.0132
0.1075	16600	0.0012
0.1082	16700	0.001
0.1088	16800	0.0015
0.1095	16900	0.0008
0.1101	17000	0.0034
0.1108	17100	0.0012
0.1114	17200	0.0019
0.1121	17300	0.0008
0.1127	17400	0.0018
0.1134	17500	0.0007
0.1140	17600	0.0028
0.1147	17700	0.0102
0.1153	17800	0.0009
0.1160	17900	0.0008
0.1166	18000	0.0148
0.1172	18100	0.0012
0.1179	18200	0.0011
0.1185	18300	0.0014
0.1192	18400	0.0022
0.1198	18500	0.0269
0.1205	18600	0.0007
0.1211	18700	0.0009
0.1218	18800	0.0008
0.1224	18900	0.001
0.1231	19000	0.0116
0.1237	19100	0.0011
0.1244	19200	0.0019
0.1250	19300	0.0014
0.1257	19400	0.0032
0.1263	19500	0.0017
0.1270	19600	0.0008
0.1276	19700	0.0011
0.1283	19800	0.0019
0.1289	19900	0.0008
0.1296	20000	0.0009
0.1302	20100	0.0012
0.1309	20200	0.0031
0.1315	20300	0.0016
0.1321	20400	0.0009
0.1328	20500	0.0162
0.1334	20600	0.001
0.1341	20700	0.0011
0.1347	20800	0.0012
0.1354	20900	0.0006
0.1360	21000	0.0007
0.1367	21100	0.0008
0.1373	21200	0.001
0.1380	21300	0.0014
0.1386	21400	0.0036
0.1393	21500	0.0011
0.1399	21600	0.0023
0.1406	21700	0.0011
0.1412	21800	0.0007
0.1419	21900	0.0007
0.1425	22000	0.0008
0.1432	22100	0.0011
0.1438	22200	0.0007
0.1445	22300	0.0006
0.1451	22400	0.0075
0.1457	22500	0.0009
0.1464	22600	0.0011
0.1470	22700	0.001
0.1477	22800	0.0011
0.1483	22900	0.0013
0.1490	23000	0.0009
0.1496	23100	0.0008
0.1503	23200	0.0016
0.1509	23300	0.0017
0.1516	23400	0.0016
0.1522	23500	0.0019
0.1529	23600	0.0011
0.1535	23700	0.0007
0.1542	23800	0.0011
0.1548	23900	0.0082
0.1555	24000	0.0012
0.1561	24100	0.0011
0.1568	24200	0.001
0.1574	24300	0.0201
0.1581	24400	0.0008
0.1587	24500	0.0033
0.1594	24600	0.0006
0.1600	24700	0.0009
0.1606	24800	0.0011
0.1613	24900	0.0009
0.1619	25000	0.0009
0.1626	25100	0.0012
0.1632	25200	0.0009
0.1639	25300	0.0013
0.1645	25400	0.0026
0.1652	25500	0.0008
0.1658	25600	0.001
0.1665	25700	0.0008
0.1671	25800	0.0035
0.1678	25900	0.0008
0.1684	26000	0.0007
0.1691	26100	0.0022
0.1697	26200	0.001
0.1704	26300	0.0012
0.1710	26400	0.0011
0.1717	26500	0.0013
0.1723	26600	0.001
0.1730	26700	0.0015
0.1736	26800	0.001
0.1743	26900	0.0007
0.1749	27000	0.0008
0.1755	27100	0.0012
0.1762	27200	0.0016
0.1768	27300	0.0384
0.1775	27400	0.0013
0.1781	27500	0.0016
0.1788	27600	0.0013
0.1794	27700	0.0014
0.1801	27800	0.0018
0.1807	27900	0.0017
0.1814	28000	0.001
0.1820	28100	0.0011
0.1827	28200	0.0013
0.1833	28300	0.0006
0.1840	28400	0.001
0.1846	28500	0.0009
0.1853	28600	0.0009
0.1859	28700	0.0101
0.1866	28800	0.0013
0.1872	28900	0.002
0.1879	29000	0.0157
0.1885	29100	0.0014
0.1892	29200	0.0006
0.1898	29300	0.002
0.1904	29400	0.0008
0.1911	29500	0.0015
0.1917	29600	0.0007
0.1924	29700	0.0008
0.1930	29800	0.0008
0.1937	29900	0.0042
0.1943	30000	0.0009
0.1950	30100	0.0007
0.1956	30200	0.0008
0.1963	30300	0.0006
0.1969	30400	0.0009
0.1976	30500	0.0021
0.1982	30600	0.001
0.1989	30700	0.0008
0.1995	30800	0.001
0.2002	30900	0.0009
0.2008	31000	0.0007
0.2015	31100	0.0007
0.2021	31200	0.0031
0.2028	31300	0.0011
0.2034	31400	0.0015
0.2040	31500	0.0014
0.2047	31600	0.0011
0.2053	31700	0.0009
0.2060	31800	0.0008
0.2066	31900	0.0056
0.2073	32000	0.0007
0.2079	32100	0.0013
0.2086	32200	0.0077
0.2092	32300	0.0014
0.2099	32400	0.0014
0.2105	32500	0.0018
0.2112	32600	0.0023
0.2118	32700	0.0013
0.2125	32800	0.0009
0.2131	32900	0.001
0.2138	33000	0.0015
0.2144	33100	0.0008
0.2151	33200	0.0008
0.2157	33300	0.0018
0.2164	33400	0.0026
0.2170	33500	0.0013
0.2177	33600	0.0026
0.2183	33700	0.0009
0.2189	33800	0.0009
0.2196	33900	0.0015
0.2202	34000	0.0031
0.2209	34100	0.0008
0.2215	34200	0.0006
0.2222	34300	0.0006
0.2228	34400	0.0007
0.2235	34500	0.0013
0.2241	34600	0.0083
0.2248	34700	0.0007
0.2254	34800	0.0008
0.2261	34900	0.0028
0.2267	35000	0.0008
0.2274	35100	0.0006
0.2280	35200	0.0022
0.2287	35300	0.0026
0.2293	35400	0.0008
0.2300	35500	0.0009
0.2306	35600	0.0014
0.2313	35700	0.0007
0.2319	35800	0.0013
0.2326	35900	0.001
0.2332	36000	0.0011
0.2338	36100	0.002
0.2345	36200	0.0011
0.2351	36300	0.0008
0.2358	36400	0.0008
0.2364	36500	0.0012
0.2371	36600	0.0007
0.2377	36700	0.001
0.2384	36800	0.0009
0.2390	36900	0.0007
0.2397	37000	0.0037
0.2403	37100	0.0048
0.2410	37200	0.0026
0.2416	37300	0.0342
0.2423	37400	0.0011
0.2429	37500	0.0154
0.2436	37600	0.0014
0.2442	37700	0.0044
0.2449	37800	0.0021
0.2455	37900	0.0007
0.2462	38000	0.0009
0.2468	38100	0.0013
0.2475	38200	0.001
0.2481	38300	0.001
0.2487	38400	0.0007
0.2494	38500	0.0021
0.2500	38600	0.0007
0.2507	38700	0.0006
0.2513	38800	0.0009
0.2520	38900	0.0009
0.2526	39000	0.0015
0.2533	39100	0.0011
0.2539	39200	0.0007
0.2546	39300	0.0008
0.2552	39400	0.0005
0.2559	39500	0.0007
0.2565	39600	0.0004
0.2572	39700	0.0027
0.2578	39800	0.0009
0.2585	39900	0.0008
0.2591	40000	0.0012
0.2598	40100	0.0006
0.2604	40200	0.001
0.2611	40300	0.0008
0.2617	40400	0.0024
0.2623	40500	0.0007
0.2630	40600	0.0012
0.2636	40700	0.0029
0.2643	40800	0.0011
0.2649	40900	0.0011
0.2656	41000	0.0013
0.2662	41100	0.0012
0.2669	41200	0.0006
0.2675	41300	0.0008
0.2682	41400	0.0011
0.2688	41500	0.0011
0.2695	41600	0.0015
0.2701	41700	0.0012
0.2708	41800	0.0011
0.2714	41900	0.001
0.2721	42000	0.0007
0.2727	42100	0.0013
0.2734	42200	0.0007
0.2740	42300	0.0009
0.2747	42400	0.0277
0.2753	42500	0.0022
0.2760	42600	0.0011
0.2766	42700	0.0008
0.2772	42800	0.0024
0.2779	42900	0.0007
0.2785	43000	0.001
0.2792	43100	0.0007
0.2798	43200	0.0007
0.2805	43300	0.0009
0.2811	43400	0.0008
0.2818	43500	0.0015
0.2824	43600	0.0006
0.2831	43700	0.0005
0.2837	43800	0.0014
0.2844	43900	0.0109
0.2850	44000	0.0007
0.2857	44100	0.0008
0.2863	44200	0.001
0.2870	44300	0.0011
0.2876	44400	0.0014
0.2883	44500	0.0015
0.2889	44600	0.0019
0.2896	44700	0.001
0.2902	44800	0.0013
0.2909	44900	0.0011
0.2915	45000	0.0009
0.2921	45100	0.0007
0.2928	45200	0.0011
0.2934	45300	0.0009
0.2941	45400	0.001
0.2947	45500	0.004
0.2954	45600	0.0012
0.2960	45700	0.0013
0.2967	45800	0.0009
0.2973	45900	0.0015
0.2980	46000	0.0008
0.2986	46100	0.0009
0.2993	46200	0.0008
0.2999	46300	0.0008
0.3006	46400	0.001
0.3012	46500	0.0009
0.3019	46600	0.0021
0.3025	46700	0.0008
0.3032	46800	0.001
0.3038	46900	0.013
0.3045	47000	0.0032
0.3051	47100	0.0032
0.3058	47200	0.0011
0.3064	47300	0.0039
0.3070	47400	0.0007
0.3077	47500	0.0007
0.3083	47600	0.0007
0.3090	47700	0.0006
0.3096	47800	0.001
0.3103	47900	0.0023
0.3109	48000	0.0009
0.3116	48100	0.0008
0.3122	48200	0.0007
0.3129	48300	0.0016
0.3135	48400	0.0012
0.3142	48500	0.0007
0.3148	48600	0.0063
0.3155	48700	0.0007
0.3161	48800	0.0006
0.3168	48900	0.0008
0.3174	49000	0.001
0.3181	49100	0.0043
0.3187	49200	0.0008
0.3194	49300	0.0008
0.3200	49400	0.0007
0.3206	49500	0.0011
0.3213	49600	0.0013
0.3219	49700	0.0006
0.3226	49800	0.0008
0.3232	49900	0.0011
0.3239	50000	0.0007
0.3245	50100	0.0007
0.3252	50200	0.0026
0.3258	50300	0.001
0.3265	50400	0.0005
0.3271	50500	0.0251
0.3278	50600	0.0009
0.3284	50700	0.0009
0.3291	50800	0.0034
0.3297	50900	0.0009
0.3304	51000	0.0008
0.3310	51100	0.0006
0.3317	51200	0.0037
0.3323	51300	0.0158
0.3330	51400	0.0015
0.3336	51500	0.0007
0.3343	51600	0.0018
0.3349	51700	0.0008
0.3355	51800	0.0011
0.3362	51900	0.0054
0.3368	52000	0.0006
0.3375	52100	0.0047
0.3381	52200	0.0012
0.3388	52300	0.0011
0.3394	52400	0.001
0.3401	52500	0.0012
0.3407	52600	0.0006
0.3414	52700	0.0009
0.3420	52800	0.0008
0.3427	52900	0.0148
0.3433	53000	0.0007
0.3440	53100	0.0008
0.3446	53200	0.0007
0.3453	53300	0.002
0.3459	53400	0.0007
0.3466	53500	0.0005
0.3472	53600	0.0006
0.3479	53700	0.0011
0.3485	53800	0.0022
0.3492	53900	0.0008
0.3498	54000	0.0022
0.3504	54100	0.0007
0.3511	54200	0.0015
0.3517	54300	0.0008
0.3524	54400	0.0042
0.3530	54500	0.0008
0.3537	54600	0.0006
0.3543	54700	0.0014
0.3550	54800	0.0009
0.3556	54900	0.0071
0.3563	55000	0.0005
0.3569	55100	0.0009
0.3576	55200	0.0006
0.3582	55300	0.0008
0.3589	55400	0.0012
0.3595	55500	0.0016
0.3602	55600	0.0007
0.3608	55700	0.001
0.3615	55800	0.0007
0.3621	55900	0.0006
0.3628	56000	0.0008
0.3634	56100	0.0007
0.3641	56200	0.0028
0.3647	56300	0.0019
0.3653	56400	0.0008
0.3660	56500	0.0011
0.3666	56600	0.0014
0.3673	56700	0.0007
0.3679	56800	0.0013
0.3686	56900	0.0008
0.3692	57000	0.0008
0.3699	57100	0.0008
0.3705	57200	0.0009
0.3712	57300	0.0009
0.3718	57400	0.0031
0.3725	57500	0.0009
0.3731	57600	0.0007
0.3738	57700	0.0009
0.3744	57800	0.0006
0.3751	57900	0.0005
0.3757	58000	0.0089
0.3764	58100	0.0011
0.3770	58200	0.0007
0.3777	58300	0.0015
0.3783	58400	0.0008
0.3789	58500	0.0013
0.3796	58600	0.001
0.3802	58700	0.0009
0.3809	58800	0.0006
0.3815	58900	0.0014
0.3822	59000	0.0008
0.3828	59100	0.0006
0.3835	59200	0.0011
0.3841	59300	0.0012
0.3848	59400	0.0013
0.3854	59500	0.0015
0.3861	59600	0.0019
0.3867	59700	0.0011
0.3874	59800	0.0006
0.3880	59900	0.0022
0.3887	60000	0.0015
0.3893	60100	0.0007
0.3900	60200	0.0008
0.3906	60300	0.0006
0.3913	60400	0.0011
0.3919	60500	0.0005
0.3926	60600	0.0013
0.3932	60700	0.0006
0.3938	60800	0.001
0.3945	60900	0.0018
0.3951	61000	0.0062
0.3958	61100	0.0009
0.3964	61200	0.0016
0.3971	61300	0.0006
0.3977	61400	0.0005
0.3984	61500	0.0017
0.3990	61600	0.0008
0.3997	61700	0.0011
0.4003	61800	0.0007
0.4010	61900	0.0009
0.4016	62000	0.0011
0.4023	62100	0.0005
0.4029	62200	0.0008
0.4036	62300	0.0009
0.4042	62400	0.001
0.4049	62500	0.0012
0.4055	62600	0.0008
0.4062	62700	0.0215
0.4068	62800	0.0011
0.4075	62900	0.0011
0.4081	63000	0.0008
0.4087	63100	0.0006
0.4094	63200	0.0008
0.4100	63300	0.0006
0.4107	63400	0.0006
0.4113	63500	0.0006
0.4120	63600	0.0012
0.4126	63700	0.0008
0.4133	63800	0.0008
0.4139	63900	0.002
0.4146	64000	0.0007
0.4152	64100	0.0007
0.4159	64200	0.0008
0.4165	64300	0.0008
0.4172	64400	0.0007
0.4178	64500	0.0014
0.4185	64600	0.001
0.4191	64700	0.0017
0.4198	64800	0.0428
0.4204	64900	0.0006
0.4211	65000	0.0007
0.4217	65100	0.0006
0.4224	65200	0.001
0.4230	65300	0.0008
0.4236	65400	0.0008
0.4243	65500	0.0014
0.4249	65600	0.0006
0.4256	65700	0.0013
0.4262	65800	0.0035
0.4269	65900	0.0009
0.4275	66000	0.0005
0.4282	66100	0.0005
0.4288	66200	0.0012
0.4295	66300	0.0031
0.4301	66400	0.0007
0.4308	66500	0.0006
0.4314	66600	0.0006
0.4321	66700	0.0006
0.4327	66800	0.0008
0.4334	66900	0.0012
0.4340	67000	0.0007
0.4347	67100	0.0011
0.4353	67200	0.0008
0.4360	67300	0.0008
0.4366	67400	0.0009
0.4372	67500	0.0015
0.4379	67600	0.0006
0.4385	67700	0.0019
0.4392	67800	0.0008
0.4398	67900	0.0008
0.4405	68000	0.0137
0.4411	68100	0.0006
0.4418	68200	0.0018
0.4424	68300	0.0007
0.4431	68400	0.0254
0.4437	68500	0.0008
0.4444	68600	0.0125
0.4450	68700	0.0006
0.4457	68800	0.0006
0.4463	68900	0.0007
0.4470	69000	0.0011
0.4476	69100	0.0008
0.4483	69200	0.0054
0.4489	69300	0.0008
0.4496	69400	0.0037
0.4502	69500	0.0032
0.4509	69600	0.0006
0.4515	69700	0.0043
0.4521	69800	0.0006
0.4528	69900	0.001
0.4534	70000	0.0043
0.4541	70100	0.0011
0.4547	70200	0.0022
0.4554	70300	0.0367
0.4560	70400	0.0014
0.4567	70500	0.0007
0.4573	70600	0.0008
0.4580	70700	0.001
0.4586	70800	0.0007
0.4593	70900	0.0008
0.4599	71000	0.0008
0.4606	71100	0.0012
0.4612	71200	0.0009
0.4619	71300	0.0006
0.4625	71400	0.0005
0.4632	71500	0.0008
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0.4645	71700	0.0007
0.4651	71800	0.0006
0.4658	71900	0.0009
0.4664	72000	0.0011
0.4670	72100	0.001
0.4677	72200	0.0014
0.4683	72300	0.001
0.4690	72400	0.0006
0.4696	72500	0.0008
0.4703	72600	0.001
0.4709	72700	0.0008
0.4716	72800	0.0007
0.4722	72900	0.0007
0.4729	73000	0.0009
0.4735	73100	0.0052
0.4742	73200	0.0008
0.4748	73300	0.0006
0.4755	73400	0.0005
0.4761	73500	0.0005
0.4768	73600	0.0006
0.4774	73700	0.0006
0.4781	73800	0.0015
0.4787	73900	0.0007
0.4794	74000	0.0009
0.4800	74100	0.0035
0.4807	74200	0.0007
0.4813	74300	0.0012
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0.4826	74500	0.0008
0.4832	74600	0.0006
0.4839	74700	0.0005
0.4845	74800	0.0011
0.4852	74900	0.0007
0.4858	75000	0.001
0.4865	75100	0.0007
0.4871	75200	0.0006
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0.4884	75400	0.001
0.4891	75500	0.0014
0.4897	75600	0.0008
0.4904	75700	0.0011
0.4910	75800	0.0014
0.4917	75900	0.0007
0.4923	76000	0.02
0.4930	76100	0.0021
0.4936	76200	0.0015
0.4943	76300	0.0007
0.4949	76400	0.0009
0.4955	76500	0.0009
0.4962	76600	0.0008
0.4968	76700	0.0012
0.4975	76800	0.0019
0.4981	76900	0.0008
0.4988	77000	0.0016
0.4994	77100	0.0015
0.5001	77200	0.002
0.5007	77300	0.0074
0.5014	77400	0.0005
0.5020	77500	0.0015
0.5027	77600	0.0011
0.5033	77700	0.0083
0.5040	77800	0.0009
0.5046	77900	0.001
0.5053	78000	0.0012
0.5059	78100	0.0007
0.5066	78200	0.0007
0.5072	78300	0.0007
0.5079	78400	0.0005
0.5085	78500	0.0015
0.5092	78600	0.0014
0.5098	78700	0.0009
0.5104	78800	0.0007
0.5111	78900	0.0008
0.5117	79000	0.0008
0.5124	79100	0.0006
0.5130	79200	0.0023
0.5137	79300	0.0007
0.5143	79400	0.001
0.5150	79500	0.0009
0.5156	79600	0.0015
0.5163	79700	0.0006
0.5169	79800	0.0018
0.5176	79900	0.0007
0.5182	80000	0.0092
0.5189	80100	0.0006
0.5195	80200	0.0102
0.5202	80300	0.0097
0.5208	80400	0.0012
0.5215	80500	0.0008
0.5221	80600	0.0007
0.5228	80700	0.0006
0.5234	80800	0.0009
0.5241	80900	0.0011
0.5247	81000	0.0011
0.5253	81100	0.0007
0.5260	81200	0.0008
0.5266	81300	0.0172
0.5273	81400	0.0008
0.5279	81500	0.0007
0.5286	81600	0.0014
0.5292	81700	0.0007
0.5299	81800	0.0009
0.5305	81900	0.0006
0.5312	82000	0.0007
0.5318	82100	0.0009
0.5325	82200	0.0009
0.5331	82300	0.0028
0.5338	82400	0.0038
0.5344	82500	0.0022
0.5351	82600	0.0005
0.5357	82700	0.0006
0.5364	82800	0.0006
0.5370	82900	0.0086
0.5377	83000	0.0008
0.5383	83100	0.0007
0.5390	83200	0.001
0.5396	83300	0.0031
0.5402	83400	0.0006
0.5409	83500	0.0011
0.5415	83600	0.0008
0.5422	83700	0.0006
0.5428	83800	0.0007
0.5435	83900	0.001
0.5441	84000	0.0005
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0.5454	84200	0.0011
0.5461	84300	0.0007
0.5467	84400	0.0008
0.5474	84500	0.0006
0.5480	84600	0.0007
0.5487	84700	0.0005
0.5493	84800	0.0008
0.5500	84900	0.0016
0.5506	85000	0.0009
0.5513	85100	0.0008
0.5519	85200	0.0006
0.5526	85300	0.0005
0.5532	85400	0.0006
0.5538	85500	0.0339
0.5545	85600	0.0106
0.5551	85700	0.0006
0.5558	85800	0.0008
0.5564	85900	0.0006
0.5571	86000	0.0007
0.5577	86100	0.0008
0.5584	86200	0.0009
0.5590	86300	0.0009
0.5597	86400	0.0008
0.5603	86500	0.0008
0.5610	86600	0.0014
0.5616	86700	0.0008
0.5623	86800	0.0007
0.5629	86900	0.0009
0.5636	87000	0.0008
0.5642	87100	0.0008
0.5649	87200	0.0007
0.5655	87300	0.0013
0.5662	87400	0.0012
0.5668	87500	0.0006
0.5675	87600	0.0006
0.5681	87700	0.0008
0.5687	87800	0.0013
0.5694	87900	0.0008
0.5700	88000	0.0006
0.5707	88100	0.0137
0.5713	88200	0.0009
0.5720	88300	0.001
0.5726	88400	0.0019
0.5733	88500	0.0008
0.5739	88600	0.0007
0.5746	88700	0.0011
0.5752	88800	0.0176
0.5759	88900	0.0006
0.5765	89000	0.0009
0.5772	89100	0.0007
0.5778	89200	0.0008
0.5785	89300	0.0006
0.5791	89400	0.001
0.5798	89500	0.0008
0.5804	89600	0.0006
0.5811	89700	0.0038
0.5817	89800	0.0007
0.5824	89900	0.0007
0.5830	90000	0.0155
0.5836	90100	0.0008
0.5843	90200	0.0027
0.5849	90300	0.0016
0.5856	90400	0.0017
0.5862	90500	0.0022
0.5869	90600	0.0145
0.5875	90700	0.0007
0.5882	90800	0.0013
0.5888	90900	0.0006
0.5895	91000	0.0009
0.5901	91100	0.0004
0.5908	91200	0.0006
0.5914	91300	0.0007
0.5921	91400	0.0009
0.5927	91500	0.0006
0.5934	91600	0.0005
0.5940	91700	0.0007
0.5947	91800	0.0006
0.5953	91900	0.0005
0.5960	92000	0.0007
0.5966	92100	0.0007
0.5973	92200	0.0074
0.5979	92300	0.0007
0.5985	92400	0.001
0.5992	92500	0.0015
0.5998	92600	0.0011
0.6005	92700	0.0011
0.6011	92800	0.0012
0.6018	92900	0.0007
0.6024	93000	0.0008
0.6031	93100	0.001
0.6037	93200	0.001
0.6044	93300	0.001
0.6050	93400	0.0008
0.6057	93500	0.0007
0.6063	93600	0.0007
0.6070	93700	0.0011
0.6076	93800	0.0084
0.6083	93900	0.0009
0.6089	94000	0.0009
0.6096	94100	0.0034
0.6102	94200	0.0007
0.6109	94300	0.002
0.6115	94400	0.001
0.6121	94500	0.0007
0.6128	94600	0.0006
0.6134	94700	0.0009
0.6141	94800	0.0007
0.6147	94900	0.0007
0.6154	95000	0.0008
0.6160	95100	0.0008
0.6167	95200	0.0006
0.6173	95300	0.0007
0.6180	95400	0.0005
0.6186	95500	0.0015
0.6193	95600	0.0015
0.6199	95700	0.0009
0.6206	95800	0.0009
0.6212	95900	0.0015
0.6219	96000	0.0048
0.6225	96100	0.0009
0.6232	96200	0.003
0.6238	96300	0.0009
0.6245	96400	0.0011
0.6251	96500	0.0013
0.6258	96600	0.0008
0.6264	96700	0.0009
0.6270	96800	0.0017
0.6277	96900	0.001
0.6283	97000	0.0006
0.6290	97100	0.0052
0.6296	97200	0.0008
0.6303	97300	0.0006
0.6309	97400	0.0006
0.6316	97500	0.0008
0.6322	97600	0.0008
0.6329	97700	0.0053
0.6335	97800	0.0008
0.6342	97900	0.0008
0.6348	98000	0.0009
0.6355	98100	0.0018
0.6361	98200	0.0009
0.6368	98300	0.0006
0.6374	98400	0.0013
0.6381	98500	0.0007
0.6387	98600	0.0005
0.6394	98700	0.0012
0.6400	98800	0.0007
0.6407	98900	0.0014
0.6413	99000	0.0035
0.6419	99100	0.0012
0.6426	99200	0.0007
0.6432	99300	0.0009
0.6439	99400	0.0007
0.6445	99500	0.0011
0.6452	99600	0.0005
0.6458	99700	0.001
0.6465	99800	0.0012
0.6471	99900	0.001
0.6478	100000	0.0015
0.6484	100100	0.0015
0.6491	100200	0.0009
0.6497	100300	0.0012
0.6504	100400	0.0016
0.6510	100500	0.0014
0.6517	100600	0.0015
0.6523	100700	0.0098
0.6530	100800	0.001
0.6536	100900	0.0012
0.6543	101000	0.0016
0.6549	101100	0.0009
0.6556	101200	0.0006
0.6562	101300	0.0328
0.6568	101400	0.0008
0.6575	101500	0.0011
0.6581	101600	0.0008
0.6588	101700	0.0007
0.6594	101800	0.0006
0.6601	101900	0.0029
0.6607	102000	0.001
0.6614	102100	0.0013
0.6620	102200	0.0008
0.6627	102300	0.0007
0.6633	102400	0.0007
0.6640	102500	0.0006
0.6646	102600	0.0006
0.6653	102700	0.004
0.6659	102800	0.0007
0.6666	102900	0.0007
0.6672	103000	0.0012
0.6679	103100	0.0014
0.6685	103200	0.001
0.6692	103300	0.0008
0.6698	103400	0.001
0.6704	103500	0.0009
0.6711	103600	0.0018
0.6717	103700	0.0052
0.6724	103800	0.0013
0.6730	103900	0.0039
0.6737	104000	0.0012
0.6743	104100	0.0008
0.6750	104200	0.0012
0.6756	104300	0.001
0.6763	104400	0.0008
0.6769	104500	0.0006
0.6776	104600	0.0006
0.6782	104700	0.0036
0.6789	104800	0.001
0.6795	104900	0.0008
0.6802	105000	0.0017
0.6808	105100	0.0014
0.6815	105200	0.0017
0.6821	105300	0.0007
0.6828	105400	0.0006
0.6834	105500	0.0007
0.6841	105600	0.0008
0.6847	105700	0.0008
0.6853	105800	0.0319
0.6860	105900	0.0007
0.6866	106000	0.0021
0.6873	106100	0.0027
0.6879	106200	0.0008
0.6886	106300	0.0005
0.6892	106400	0.0011
0.6899	106500	0.0007
0.6905	106600	0.0009
0.6912	106700	0.0006
0.6918	106800	0.001
0.6925	106900	0.0008
0.6931	107000	0.0006
0.6938	107100	0.0006
0.6944	107200	0.0008
0.6951	107300	0.0006
0.6957	107400	0.0006
0.6964	107500	0.0007
0.6970	107600	0.0007
0.6977	107700	0.0019
0.6983	107800	0.0008
0.6990	107900	0.002
0.6996	108000	0.0007
0.7002	108100	0.0008
0.7009	108200	0.0008
0.7015	108300	0.0005
0.7022	108400	0.0013
0.7028	108500	0.0023
0.7035	108600	0.002
0.7041	108700	0.0006
0.7048	108800	0.0007
0.7054	108900	0.0007
0.7061	109000	0.0006
0.7067	109100	0.0007
0.7074	109200	0.0007
0.7080	109300	0.0006
0.7087	109400	0.0006
0.7093	109500	0.0048
0.7100	109600	0.0009
0.7106	109700	0.0007
0.7113	109800	0.0006
0.7119	109900	0.001
0.7126	110000	0.0011
0.7132	110100	0.0009
0.7139	110200	0.0009
0.7145	110300	0.001
0.7151	110400	0.0007
0.7158	110500	0.0007
0.7164	110600	0.0009
0.7171	110700	0.0582
0.7177	110800	0.0011
0.7184	110900	0.0008
0.7190	111000	0.0008
0.7197	111100	0.0028
0.7203	111200	0.0009
0.7210	111300	0.0018
0.7216	111400	0.0006
0.7223	111500	0.0008
0.7229	111600	0.001
0.7236	111700	0.0074
0.7242	111800	0.0006
0.7249	111900	0.0014
0.7255	112000	0.0012
0.7262	112100	0.0013
0.7268	112200	0.003
0.7275	112300	0.0011
0.7281	112400	0.0009
0.7287	112500	0.0011
0.7294	112600	0.0007
0.7300	112700	0.0009
0.7307	112800	0.0005
0.7313	112900	0.0009
0.7320	113000	0.0008
0.7326	113100	0.0006
0.7333	113200	0.0005
0.7339	113300	0.0006
0.7346	113400	0.0007
0.7352	113500	0.0008
0.7359	113600	0.0008
0.7365	113700	0.0009
0.7372	113800	0.0005
0.7378	113900	0.0013
0.7385	114000	0.0006
0.7391	114100	0.0005
0.7398	114200	0.0014
0.7404	114300	0.0005
0.7411	114400	0.0007
0.7417	114500	0.0006
0.7424	114600	0.0013
0.7430	114700	0.0008
0.7436	114800	0.0005
0.7443	114900	0.0216
0.7449	115000	0.0008
0.7456	115100	0.0006
0.7462	115200	0.0033
0.7469	115300	0.0005
0.7475	115400	0.0009
0.7482	115500	0.0017
0.7488	115600	0.0007
0.7495	115700	0.0006
0.7501	115800	0.0006
0.7508	115900	0.0013
0.7514	116000	0.0014
0.7521	116100	0.0007
0.7527	116200	0.0014
0.7534	116300	0.0018
0.7540	116400	0.0007
0.7547	116500	0.0011
0.7553	116600	0.0006
0.7560	116700	0.0008
0.7566	116800	0.0013
0.7573	116900	0.0006
0.7579	117000	0.0005
0.7585	117100	0.0011
0.7592	117200	0.0007
0.7598	117300	0.0012
0.7605	117400	0.0022
0.7611	117500	0.0008
0.7618	117600	0.0014
0.7624	117700	0.0015
0.7631	117800	0.0008
0.7637	117900	0.0008
0.7644	118000	0.0135
0.7650	118100	0.0018
0.7657	118200	0.0005
0.7663	118300	0.0008
0.7670	118400	0.0006
0.7676	118500	0.0007
0.7683	118600	0.0013
0.7689	118700	0.002
0.7696	118800	0.0016
0.7702	118900	0.0011
0.7709	119000	0.001
0.7715	119100	0.0093
0.7722	119200	0.0015
0.7728	119300	0.0008
0.7734	119400	0.0007
0.7741	119500	0.0005
0.7747	119600	0.0007
0.7754	119700	0.0013
0.7760	119800	0.0009
0.7767	119900	0.0007
0.7773	120000	0.0008
0.7780	120100	0.0009
0.7786	120200	0.0262
0.7793	120300	0.0007
0.7799	120400	0.0007
0.7806	120500	0.001
0.7812	120600	0.0008
0.7819	120700	0.0006
0.7825	120800	0.0011
0.7832	120900	0.0007
0.7838	121000	0.0062
0.7845	121100	0.0007
0.7851	121200	0.0009
0.7858	121300	0.0009
0.7864	121400	0.0007
0.7870	121500	0.0008
0.7877	121600	0.0008
0.7883	121700	0.0008
0.7890	121800	0.0011
0.7896	121900	0.0006
0.7903	122000	0.0065
0.7909	122100	0.0015
0.7916	122200	0.0006
0.7922	122300	0.0009
0.7929	122400	0.0007
0.7935	122500	0.0005
0.7942	122600	0.0006
0.7948	122700	0.0005
0.7955	122800	0.001
0.7961	122900	0.0174
0.7968	123000	0.0007
0.7974	123100	0.0009
0.7981	123200	0.0006
0.7987	123300	0.0017
0.7994	123400	0.0009
0.8000	123500	0.0024
0.8007	123600	0.0015
0.8013	123700	0.0012
0.8019	123800	0.001
0.8026	123900	0.0053
0.8032	124000	0.0007
0.8039	124100	0.0008
0.8045	124200	0.0345
0.8052	124300	0.0018
0.8058	124400	0.001
0.8065	124500	0.0013
0.8071	124600	0.0005
0.8078	124700	0.0012
0.8084	124800	0.0005
0.8091	124900	0.0009
0.8097	125000	0.0008
0.8104	125100	0.0015
0.8110	125200	0.0013
0.8117	125300	0.0007
0.8123	125400	0.001
0.8130	125500	0.0012
0.8136	125600	0.0014
0.8143	125700	0.0013
0.8149	125800	0.0008
0.8156	125900	0.0006
0.8162	126000	0.001
0.8168	126100	0.0014
0.8175	126200	0.0006
0.8181	126300	0.0014
0.8188	126400	0.0013
0.8194	126500	0.0035
0.8201	126600	0.0006
0.8207	126700	0.0036
0.8214	126800	0.0007
0.8220	126900	0.0009
0.8227	127000	0.001
0.8233	127100	0.0025
0.8240	127200	0.0015
0.8246	127300	0.001
0.8253	127400	0.0021
0.8259	127500	0.0009
0.8266	127600	0.0006
0.8272	127700	0.0009
0.8279	127800	0.0026
0.8285	127900	0.0007
0.8292	128000	0.002
0.8298	128100	0.0009
0.8305	128200	0.0006
0.8311	128300	0.0009
0.8317	128400	0.0006
0.8324	128500	0.0006
0.8330	128600	0.0007
0.8337	128700	0.0007
0.8343	128800	0.001
0.8350	128900	0.003
0.8356	129000	0.0011
0.8363	129100	0.001
0.8369	129200	0.0011
0.8376	129300	0.0007
0.8382	129400	0.0015
0.8389	129500	0.0011
0.8395	129600	0.001
0.8402	129700	0.0046
0.8408	129800	0.002
0.8415	129900	0.0019
0.8421	130000	0.002
0.8428	130100	0.0009
0.8434	130200	0.0006
0.8441	130300	0.005
0.8447	130400	0.0007
0.8453	130500	0.0049
0.8460	130600	0.0007
0.8466	130700	0.0058
0.8473	130800	0.0031
0.8479	130900	0.0012
0.8486	131000	0.0012
0.8492	131100	0.001
0.8499	131200	0.0011
0.8505	131300	0.0015
0.8512	131400	0.0007
0.8518	131500	0.001
0.8525	131600	0.001
0.8531	131700	0.0008
0.8538	131800	0.0006
0.8544	131900	0.001
0.8551	132000	0.001
0.8557	132100	0.0058
0.8564	132200	0.0008
0.8570	132300	0.0009
0.8577	132400	0.0044
0.8583	132500	0.0006
0.8590	132600	0.0009
0.8596	132700	0.0026
0.8602	132800	0.0017
0.8609	132900	0.0007
0.8615	133000	0.0019
0.8622	133100	0.0147
0.8628	133200	0.001
0.8635	133300	0.0007
0.8641	133400	0.0113
0.8648	133500	0.0008
0.8654	133600	0.001
0.8661	133700	0.0013
0.8667	133800	0.0007
0.8674	133900	0.0014
0.8680	134000	0.0015
0.8687	134100	0.0011
0.8693	134200	0.0011
0.8700	134300	0.0009
0.8706	134400	0.0006
0.8713	134500	0.0011
0.8719	134600	0.0007
0.8726	134700	0.0007
0.8732	134800	0.0006
0.8739	134900	0.0011
0.8745	135000	0.0009
0.8751	135100	0.0009
0.8758	135200	0.0009
0.8764	135300	0.0009
0.8771	135400	0.0014
0.8777	135500	0.0007
0.8784	135600	0.001
0.8790	135700	0.0012
0.8797	135800	0.0007
0.8803	135900	0.001
0.8810	136000	0.0006
0.8816	136100	0.0111
0.8823	136200	0.0009
0.8829	136300	0.0007
0.8836	136400	0.001
0.8842	136500	0.0015
0.8849	136600	0.0007
0.8855	136700	0.0015
0.8862	136800	0.0009
0.8868	136900	0.0007
0.8875	137000	0.0009
0.8881	137100	0.001
0.8888	137200	0.0015
0.8894	137300	0.0017
0.8900	137400	0.0006
0.8907	137500	0.0015
0.8913	137600	0.0008
0.8920	137700	0.0016
0.8926	137800	0.0013
0.8933	137900	0.0024
0.8939	138000	0.0007
0.8946	138100	0.0009
0.8952	138200	0.0043
0.8959	138300	0.0109
0.8965	138400	0.0062
0.8972	138500	0.0008
0.8978	138600	0.001
0.8985	138700	0.0068
0.8991	138800	0.0018
0.8998	138900	0.0011
0.9004	139000	0.0012
0.9011	139100	0.0008
0.9017	139200	0.001
0.9024	139300	0.001
0.9030	139400	0.0006
0.9036	139500	0.0008
0.9043	139600	0.0007
0.9049	139700	0.0045
0.9056	139800	0.0009
0.9062	139900	0.0121
0.9069	140000	0.0006
0.9075	140100	0.0012
0.9082	140200	0.0007
0.9088	140300	0.0162
0.9095	140400	0.0014
0.9101	140500	0.0017
0.9108	140600	0.0013
0.9114	140700	0.0067
0.9121	140800	0.001
0.9127	140900	0.0015
0.9134	141000	0.0008
0.9140	141100	0.0011
0.9147	141200	0.0007
0.9153	141300	0.0019
0.9160	141400	0.0011
0.9166	141500	0.0024
0.9173	141600	0.0014
0.9179	141700	0.0006
0.9185	141800	0.0012
0.9192	141900	0.0021
0.9198	142000	0.0026
0.9205	142100	0.0008
0.9211	142200	0.0015
0.9218	142300	0.0011
0.9224	142400	0.0014
0.9231	142500	0.0006
0.9237	142600	0.0024
0.9244	142700	0.0031
0.9250	142800	0.0005
0.9257	142900	0.0068
0.9263	143000	0.0009
0.9270	143100	0.0011
0.9276	143200	0.0008
0.9283	143300	0.0008
0.9289	143400	0.0204
0.9296	143500	0.0011
0.9302	143600	0.0009
0.9309	143700	0.0009
0.9315	143800	0.0012
0.9322	143900	0.0045
0.9328	144000	0.0007
0.9334	144100	0.0008
0.9341	144200	0.0013
0.9347	144300	0.0008
0.9354	144400	0.0006
0.9360	144500	0.0057
0.9367	144600	0.0044
0.9373	144700	0.0015
0.9380	144800	0.0009
0.9386	144900	0.0007
0.9393	145000	0.0021
0.9399	145100	0.0006
0.9406	145200	0.0017
0.9412	145300	0.0008
0.9419	145400	0.0014
0.9425	145500	0.0012
0.9432	145600	0.0012
0.9438	145700	0.0011
0.9445	145800	0.0011
0.9451	145900	0.0011
0.9458	146000	0.0017
0.9464	146100	0.0011
0.9471	146200	0.0005
0.9477	146300	0.0011
0.9483	146400	0.0021
0.9490	146500	0.0012
0.9496	146600	0.0012
0.9503	146700	0.003
0.9509	146800	0.0546
0.9516	146900	0.0019
0.9522	147000	0.0013
0.9529	147100	0.0008
0.9535	147200	0.001
0.9542	147300	0.0013
0.9548	147400	0.0026
0.9555	147500	0.0012
0.9561	147600	0.001
0.9568	147700	0.0009
0.9574	147800	0.005
0.9581	147900	0.0022
0.9587	148000	0.0016
0.9594	148100	0.0013
0.9600	148200	0.0006
0.9607	148300	0.0013
0.9613	148400	0.0056
0.9619	148500	0.0017
0.9626	148600	0.0015
0.9632	148700	0.0029
0.9639	148800	0.0008
0.9645	148900	0.0008
0.9652	149000	0.0018
0.9658	149100	0.002
0.9665	149200	0.0012
0.9671	149300	0.001
0.9678	149400	0.0041
0.9684	149500	0.0006
0.9691	149600	0.0016
0.9697	149700	0.001
0.9704	149800	0.0008
0.9710	149900	0.003
0.9717	150000	0.0013
0.9723	150100	0.0017
0.9730	150200	0.0008
0.9736	150300	0.0018
0.9743	150400	0.0007
0.9749	150500	0.001
0.9756	150600	0.0103
0.9762	150700	0.0014
0.9768	150800	0.0007
0.9775	150900	0.0186
0.9781	151000	0.0015
0.9788	151100	0.0008
0.9794	151200	0.0006
0.9801	151300	0.0019
0.9807	151400	0.0008
0.9814	151500	0.0023
0.9820	151600	0.0076
0.9827	151700	0.0013
0.9833	151800	0.0036
0.9840	151900	0.0008
0.9846	152000	0.0025
0.9853	152100	0.0011
0.9859	152200	0.0008
0.9866	152300	0.0101
0.9872	152400	0.002
0.9879	152500	0.0011
0.9885	152600	0.0013
0.9892	152700	0.0023
0.9898	152800	0.0014
0.9905	152900	0.0017
0.9911	153000	0.0009
0.9917	153100	0.0015
0.9924	153200	0.0035
0.9930	153300	0.0054
0.9937	153400	0.0006
0.9943	153500	0.0021
0.9950	153600	0.0005
0.9956	153700	0.0007
0.9963	153800	0.0035
0.9969	153900	0.0014
0.9976	154000	0.0142
0.9982	154100	0.0008
0.9989	154200	0.0015
0.9995	154300	0.0017

Framework Versions

• Python: 3.12.3
• Sentence Transformers: 5.1.0
• Transformers: 4.55.4
• PyTorch: 2.6.0+cu124
• Accelerate: 1.10.1
• Datasets: 4.0.0
• Tokenizers: 0.21.4

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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}