all-MiniLM-L6-v30-pair_score

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the pairs_with_scores_v26 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: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- pairs_with_scores_v26
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 = [
    'professional lashes',
    'liquid ink eye liner',
    'hazelnut coffee',
]
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, 0.8147, 0.7553],
#         [0.8147, 1.0000, 0.8074],
#         [0.7553, 0.8074, 1.0000]])

Training Details

Training Dataset

pairs_with_scores_v26

Dataset: pairs_with_scores_v26 at 4732b20
Size: 90,177,341 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: 6.46 tokens
max: 48 tokens

min: 3 tokens
mean: 6.55 tokens
max: 43 tokens

min: 0.0
mean: 0.55
max: 1.0
Samples:

sentence1 sentence2 score

espresso cake crumble cake 0.5726045966148376

floral outfit chiffon outfit 0.8436050415039062

mim laptop adrenocortine ampoules 0.1853217184543609

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 6.46 tokens max: 48 tokens	min: 3 tokens mean: 6.55 tokens max: 43 tokens	min: 0.0 mean: 0.55 max: 1.0

sentence1	sentence2	score
`espresso cake`	`crumble cake`	`0.5726045966148376`
`floral outfit`	`chiffon outfit`	`0.8436050415039062`
`mim laptop`	`adrenocortine ampoules`	`0.1853217184543609`

Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

pairs_with_scores_v26

Dataset: pairs_with_scores_v26 at 4732b20
Size: 453,153 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: 6.59 tokens
max: 60 tokens

min: 3 tokens
mean: 6.65 tokens
max: 80 tokens

min: 0.0
mean: 0.54
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 6.59 tokens max: 60 tokens	min: 3 tokens mean: 6.65 tokens max: 80 tokens	min: 0.0 mean: 0.54 max: 1.0

Samples:

sentence1	sentence2	score
`ruffles shirt`	`palms kimono`	`0.6063277721405029`
`babyblue hoodie`	`pullover hoodie`	`0.9046977758407592`
`mandarin collar shirt`	`back print sweatshirt`	`0.5850359201431274`

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	Validation Loss
0.0001	100	14.8658	-
0.0003	200	14.7317	-
0.0004	300	14.9403	-
0.0006	400	14.6755	-
0.0007	500	14.5866	-
0.0009	600	14.3954	-
0.0010	700	14.6172	-
0.0011	800	14.2701	-
0.0013	900	14.165	-
0.0014	1000	13.6132	-
0.0016	1100	13.5502	-
0.0017	1200	13.1934	-
0.0018	1300	12.7373	-
0.0020	1400	12.3677	-
0.0021	1500	12.1784	-
0.0023	1600	11.7158	-
0.0024	1700	11.3799	-
0.0026	1800	11.1483	-
0.0027	1900	10.8595	-
0.0028	2000	10.5913	-
0.0030	2100	10.2696	-
0.0031	2200	10.0932	-
0.0033	2300	9.888	-
0.0034	2400	9.6627	-
0.0035	2500	9.4667	-
0.0037	2600	9.3377	-
0.0038	2700	9.1901	-
0.0040	2800	9.1015	-
0.0041	2900	9.0266	-
0.0043	3000	8.9711	-
0.0044	3100	8.9337	-
0.0045	3200	8.9165	-
0.0047	3300	8.8976	-
0.0048	3400	8.8864	-
0.0050	3500	8.8718	-
0.0051	3600	8.8587	-
0.0053	3700	8.8487	-
0.0054	3800	8.8376	-
0.0055	3900	8.829	-
0.0057	4000	8.8237	-
0.0058	4100	8.8326	-
0.0060	4200	8.8167	-
0.0061	4300	8.8073	-
0.0062	4400	8.8089	-
0.0064	4500	8.8045	-
0.0065	4600	8.803	-
0.0067	4700	8.7881	-
0.0068	4800	8.7871	-
0.0070	4900	8.7892	-
0.0071	5000	8.787	-
0.0072	5100	8.7829	-
0.0074	5200	8.7721	-
0.0075	5300	8.7814	-
0.0077	5400	8.7741	-
0.0078	5500	8.7712	-
0.0079	5600	8.7695	-
0.0081	5700	8.7656	-
0.0082	5800	8.7579	-
0.0084	5900	8.7548	-
0.0085	6000	8.763	-
0.0087	6100	8.7648	-
0.0088	6200	8.7605	-
0.0089	6300	8.7462	-
0.0091	6400	8.7418	-
0.0092	6500	8.7601	-
0.0094	6600	8.7434	-
0.0095	6700	8.7417	-
0.0097	6800	8.7373	-
0.0098	6900	8.7521	-
0.0099	7000	8.7361	-
0.0101	7100	8.7324	-
0.0102	7200	8.7355	-
0.0104	7300	8.7385	-
0.0105	7400	8.7353	-
0.0106	7500	8.7335	-
0.0108	7600	8.7277	-
0.0109	7700	8.7254	-
0.0111	7800	8.7212	-
0.0112	7900	8.7411	-
0.0114	8000	8.71	-
0.0115	8100	8.7157	-
0.0116	8200	8.7178	-
0.0118	8300	8.7154	-
0.0119	8400	8.7178	-
0.0121	8500	8.7167	-
0.0122	8600	8.7048	-
0.0123	8700	8.7056	-
0.0125	8800	8.7074	-
0.0126	8900	8.7053	-
0.0128	9000	8.7099	-
0.0129	9100	8.7043	-
0.0131	9200	8.7078	-
0.0132	9300	8.6975	-
0.0133	9400	8.7033	-
0.0135	9500	8.6972	-
0.0136	9600	8.6968	-
0.0138	9700	8.6955	-
0.0139	9800	8.6914	-
0.0141	9900	8.6945	-
0.0142	10000	8.6868	-
0.0143	10100	8.6913	-
0.0145	10200	8.6899	-
0.0146	10300	8.6865	-
0.0148	10400	8.6873	-
0.0149	10500	8.6881	-
0.0150	10600	8.6885	-
0.0152	10700	8.6768	-
0.0153	10800	8.6706	-
0.0155	10900	8.6862	-
0.0156	11000	8.6791	-
0.0158	11100	8.6781	-
0.0159	11200	8.6811	-
0.0160	11300	8.6792	-
0.0162	11400	8.6697	-
0.0163	11500	8.665	-
0.0165	11600	8.6667	-
0.0166	11700	8.6654	-
0.0167	11800	8.674	-
0.0169	11900	8.6634	-
0.0170	12000	8.6656	-
0.0172	12100	8.6656	-
0.0173	12200	8.6619	-
0.0175	12300	8.6576	-
0.0176	12400	8.6674	-
0.0177	12500	8.667	-
0.0179	12600	8.6639	-
0.0180	12700	8.667	-
0.0182	12800	8.6581	-
0.0183	12900	8.6591	-
0.0185	13000	8.6553	-
0.0186	13100	8.6556	-
0.0187	13200	8.6627	-
0.0189	13300	8.6485	-
0.0190	13400	8.6471	-
0.0192	13500	8.6503	-
0.0193	13600	8.6531	-
0.0194	13700	8.6541	-
0.0196	13800	8.6518	-
0.0197	13900	8.6394	-
0.0199	14000	8.6395	-
0.0200	14100	8.6388	-
0.0202	14200	8.6376	-
0.0203	14300	8.6294	-
0.0204	14400	8.6414	-
0.0206	14500	8.6332	-
0.0207	14600	8.6369	-
0.0209	14700	8.6513	-
0.0210	14800	8.6273	-
0.0211	14900	8.6372	-
0.0213	15000	8.6238	-
0.0214	15100	8.6345	-
0.0216	15200	8.6477	-
0.0217	15300	8.6319	-
0.0219	15400	8.6312	-
0.0220	15500	8.6344	-
0.0221	15600	8.6261	-
0.0223	15700	8.6162	-
0.0224	15800	8.6231	-
0.0226	15900	8.6323	-
0.0227	16000	8.6174	-
0.0229	16100	8.626	-
0.0230	16200	8.6269	-
0.0231	16300	8.626	-
0.0233	16400	8.621	-
0.0234	16500	8.6141	-
0.0236	16600	8.6245	-
0.0237	16700	8.6279	-
0.0238	16800	8.6088	-
0.0240	16900	8.618	-
0.0241	17000	8.621	-
0.0243	17100	8.628	-
0.0244	17200	8.6111	-
0.0246	17300	8.6099	-
0.0247	17400	8.601	-
0.0248	17500	8.6106	-
0.0250	17600	8.6202	-
0.0251	17700	8.6058	-
0.0253	17800	8.6068	-
0.0254	17900	8.6111	-
0.0255	18000	8.6049	-
0.0257	18100	8.6033	-
0.0258	18200	8.609	-
0.0260	18300	8.5985	-
0.0261	18400	8.5918	-
0.0263	18500	8.6045	-
0.0264	18600	8.6075	-
0.0265	18700	8.6001	-
0.0267	18800	8.5988	-
0.0268	18900	8.5968	-
0.0270	19000	8.603	-
0.0271	19100	8.6121	-
0.0273	19200	8.6038	-
0.0274	19300	8.5948	-
0.0275	19400	8.5943	-
0.0277	19500	8.5937	-
0.0278	19600	8.5905	-
0.0280	19700	8.595	-
0.0281	19800	8.5877	-
0.0282	19900	8.5938	-
0.0284	20000	8.5808	-
0.0285	20100	8.5917	-
0.0287	20200	8.5919	-
0.0288	20300	8.5986	-
0.0290	20400	8.5973	-
0.0291	20500	8.5912	-
0.0292	20600	8.5788	-
0.0294	20700	8.5899	-
0.0295	20800	8.59	-
0.0297	20900	8.578	-
0.0298	21000	8.5879	-
0.0299	21100	8.5845	-
0.0301	21200	8.5829	-
0.0302	21300	8.5801	-
0.0304	21400	8.5837	-
0.0305	21500	8.5782	-
0.0307	21600	8.5802	-
0.0308	21700	8.579	-
0.0309	21800	8.578	-
0.0311	21900	8.5601	-
0.0312	22000	8.5741	-
0.0314	22100	8.5777	-
0.0315	22200	8.5779	-
0.0317	22300	8.5716	-
0.0318	22400	8.5705	-
0.0319	22500	8.5852	-
0.0321	22600	8.5786	-
0.0322	22700	8.5733	-
0.0324	22800	8.5694	-
0.0325	22900	8.5745	-
0.0326	23000	8.5743	-
0.0328	23100	8.5675	-
0.0329	23200	8.5748	-
0.0331	23300	8.5683	-
0.0332	23400	8.5733	-
0.0334	23500	8.5603	-
0.0335	23600	8.5697	-
0.0336	23700	8.5702	-
0.0338	23800	8.5699	-
0.0339	23900	8.5618	-
0.0341	24000	8.5555	-
0.0342	24100	8.5705	-
0.0344	24200	8.5666	-
0.0345	24300	8.5612	-
0.0346	24400	8.5538	-
0.0348	24500	8.5582	-
0.0349	24600	8.5498	-
0.0351	24700	8.5595	-
0.0352	24800	8.5672	-
0.0353	24900	8.5485	-
0.0355	25000	8.5586	-
0.0356	25100	8.5635	-
0.0358	25200	8.554	-
0.0359	25300	8.5577	-
0.0361	25400	8.5538	-
0.0362	25500	8.5517	-
0.0363	25600	8.5547	-
0.0365	25700	8.5465	-
0.0366	25800	8.5649	-
0.0368	25900	8.5601	-
0.0369	26000	8.5511	-
0.0370	26100	8.5599	-
0.0372	26200	8.5421	-
0.0373	26300	8.5434	-
0.0375	26400	8.5536	-
0.0376	26500	8.5408	-
0.0378	26600	8.5589	-
0.0379	26700	8.5387	-
0.0380	26800	8.5367	-
0.0382	26900	8.546	-
0.0383	27000	8.5493	-
0.0385	27100	8.5676	-
0.0386	27200	8.5436	-
0.0388	27300	8.5446	-
0.0389	27400	8.5406	-
0.0390	27500	8.5359	-
0.0392	27600	8.5332	-
0.0393	27700	8.5316	-
0.0395	27800	8.5436	-
0.0396	27900	8.5422	-
0.0397	28000	8.5445	-
0.0399	28100	8.5422	-
0.0400	28200	8.5443	-
0.0402	28300	8.5372	-
0.0403	28400	8.5458	-
0.0405	28500	8.5316	-
0.0406	28600	8.5406	-
0.0407	28700	8.5395	-
0.0409	28800	8.5402	-
0.0410	28900	8.5337	-
0.0412	29000	8.5392	-
0.0413	29100	8.5262	-
0.0414	29200	8.535	-
0.0416	29300	8.5238	-
0.0417	29400	8.5395	-
0.0419	29500	8.5337	-
0.0420	29600	8.5371	-
0.0422	29700	8.545	-
0.0423	29800	8.5438	-
0.0424	29900	8.5434	-
0.0426	30000	8.5252	-
0.0427	30100	8.5307	-
0.0429	30200	8.5341	-
0.0430	30300	8.5187	-
0.0432	30400	8.5281	-
0.0433	30500	8.5257	-
0.0434	30600	8.5327	-
0.0436	30700	8.5236	-
0.0437	30800	8.529	-
0.0439	30900	8.5198	-
0.0440	31000	8.5272	-
0.0441	31100	8.5282	-
0.0443	31200	8.5271	-
0.0444	31300	8.5174	-
0.0446	31400	8.5247	-
0.0447	31500	8.5207	-
0.0449	31600	8.52	-
0.0450	31700	8.5133	-
0.0451	31800	8.5197	-
0.0453	31900	8.526	-
0.0454	32000	8.5167	-
0.0456	32100	8.5187	-
0.0457	32200	8.5292	-
0.0458	32300	8.5232	-
0.0460	32400	8.5195	-
0.0461	32500	8.5246	-
0.0463	32600	8.5146	-
0.0464	32700	8.5001	-
0.0466	32800	8.5137	-
0.0467	32900	8.5061	-
0.0468	33000	8.5134	-
0.0470	33100	8.511	-
0.0471	33200	8.511	-
0.0473	33300	8.5238	-
0.0474	33400	8.5036	-
0.0476	33500	8.5153	-
0.0477	33600	8.5123	-
0.0478	33700	8.5156	-
0.0480	33800	8.5143	-
0.0481	33900	8.5018	-
0.0483	34000	8.5157	-
0.0484	34100	8.5107	-
0.0485	34200	8.516	-
0.0487	34300	8.514	-
0.0488	34400	8.5083	-
0.0490	34500	8.5015	-
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0.1912	134700	8.2427	-
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0.1919	135200	8.2429	-
0.1920	135300	8.2429	-
0.1922	135400	8.2646	-
0.1923	135500	8.2529	-
0.1925	135600	8.2275	-
0.1926	135700	8.2326	-
0.1928	135800	8.231	-
0.1929	135900	8.2593	-
0.1930	136000	8.2476	-
0.1932	136100	8.2561	-
0.1933	136200	8.252	-
0.1935	136300	8.2354	-
0.1936	136400	8.2607	-
0.1938	136500	8.2344	-
0.1939	136600	8.2527	-
0.1940	136700	8.2279	-
0.1942	136800	8.2431	-
0.1943	136900	8.2536	-
0.1945	137000	8.2509	-
0.1946	137100	8.2401	-
0.1947	137200	8.2305	-
0.1949	137300	8.2576	-
0.1950	137400	8.2709	-
0.1952	137500	8.2441	-
0.1953	137600	8.2602	-
0.1955	137700	8.2625	-
0.1956	137800	8.2607	-
0.1957	137900	8.2442	-
0.1959	138000	8.2399	-
0.1960	138100	8.2349	-
0.1962	138200	8.2591	-
0.1963	138300	8.2515	-
0.1964	138400	8.2427	-
0.1966	138500	8.2448	-
0.1967	138600	8.2489	-
0.1969	138700	8.2314	-
0.1970	138800	8.233	-
0.1972	138900	8.2715	-
0.1973	139000	8.2582	-
0.1974	139100	8.2449	-
0.1976	139200	8.2274	-
0.1977	139300	8.2479	-
0.1979	139400	8.2537	-
0.1980	139500	8.2487	-
0.1982	139600	8.2355	-
0.1983	139700	8.2515	-
0.1984	139800	8.2396	-
0.1986	139900	8.2382	-
0.1987	140000	8.2339	-
0.1989	140100	8.2491	-
0.1990	140200	8.2288	-
0.1991	140300	8.2467	-
0.1993	140400	8.2399	-
0.1994	140500	8.2519	-
0.1996	140600	8.221	-
0.1997	140700	8.2496	-
0.1999	140800	8.2508	-
0.2000	140900	8.2508	-
0.2001	141000	8.2469	-
0.2003	141100	8.244	-
0.2004	141200	8.2474	-
0.2006	141300	8.2504	-
0.2007	141400	8.2326	-
0.2008	141500	8.2439	-
0.2010	141600	8.2362	-
0.2011	141700	8.2401	-
0.2013	141800	8.2534	-
0.2014	141900	8.2461	-
0.2016	142000	8.2415	-
0.2017	142100	8.2382	-
0.2018	142200	8.2742	-
0.2020	142300	8.2399	-
0.2021	142400	8.2265	-
0.2023	142500	8.2395	-
0.2024	142600	8.2386	-
0.2026	142700	8.2449	-
0.2027	142800	8.2407	-
0.2028	142900	8.2279	-
0.2030	143000	8.2499	-
0.2031	143100	8.2374	-
0.2033	143200	8.2397	-
0.2034	143300	8.2449	-
0.2035	143400	8.2212	-
0.2037	143500	8.2416	-
0.2038	143600	8.2384	-
0.2040	143700	8.2366	-
0.2041	143800	8.2238	-
0.2043	143900	8.2283	-
0.2044	144000	8.2355	-
0.2045	144100	8.2194	-
0.2047	144200	8.251	-
0.2048	144300	8.2413	-
0.2050	144400	8.2653	-
0.2051	144500	8.2359	-
0.2052	144600	8.2454	-
0.2054	144700	8.2328	-
0.2055	144800	8.2334	-
0.2057	144900	8.2411	-
0.2058	145000	8.2234	-
0.2060	145100	8.2405	-
0.2061	145200	8.2378	-
0.2062	145300	8.2382	-
0.2064	145400	8.2253	-
0.2065	145500	8.2303	-
0.2067	145600	8.229	-
0.2068	145700	8.2163	-
0.2070	145800	8.2423	-
0.2071	145900	8.2188	-
0.2072	146000	8.2443	-
0.2074	146100	8.2314	-
0.2075	146200	8.2372	-
0.2077	146300	8.2401	-
0.2078	146400	8.2248	-
0.2079	146500	8.2242	-
0.2081	146600	8.2255	-
0.2082	146700	8.2316	-
0.2084	146800	8.244	-
0.2085	146900	8.2325	-
0.2087	147000	8.2552	-
0.2088	147100	8.2488	-
0.2089	147200	8.2332	-
0.2091	147300	8.2308	-
0.2092	147400	8.2316	-
0.2094	147500	8.2188	-
0.2095	147600	8.2579	-
0.2096	147700	8.2369	-
0.2098	147800	8.2341	-
0.2099	147900	8.2475	-
0.2101	148000	8.2114	-
0.2102	148100	8.2359	-
0.2104	148200	8.2468	-
0.2105	148300	8.2276	-
0.2106	148400	8.2199	-
0.2108	148500	8.2154	-
0.2109	148600	8.211	-
0.2111	148700	8.2315	-
0.2112	148800	8.2466	-
0.2114	148900	8.2235	-
0.2115	149000	8.2444	-
0.2116	149100	8.2345	-
0.2118	149200	8.2346	-
0.2119	149300	8.2224	-
0.2121	149400	8.2269	-
0.2122	149500	8.2484	-
0.2123	149600	8.2402	-
0.2125	149700	8.2223	-
0.2126	149800	8.2313	-
0.2128	149900	8.2354	-
0.2129	150000	8.2339	-
0.2131	150100	8.2286	-
0.2132	150200	8.2344	-
0.2133	150300	8.2319	-
0.2135	150400	8.2061	-
0.2136	150500	8.2496	-
0.2138	150600	8.2379	-
0.2139	150700	8.2258	-
0.2140	150800	8.2271	-
0.2142	150900	8.2289	-
0.2143	151000	8.2284	-
0.2145	151100	8.2238	-
0.2146	151200	8.2217	-
0.2148	151300	8.2295	-
0.2149	151400	8.2469	-
0.2150	151500	8.2362	-
0.2152	151600	8.2521	-
0.2153	151700	8.2177	-
0.2155	151800	8.2373	-
0.2156	151900	8.2393	-
0.2158	152000	8.2431	-
0.2159	152100	8.222	-
0.2160	152200	8.2318	-
0.2162	152300	8.2262	-
0.2163	152400	8.2351	-
0.2165	152500	8.2147	-
0.2166	152600	8.2281	-
0.2167	152700	8.2178	-
0.2169	152800	8.2411	-
0.2170	152900	8.2033	-
0.2172	153000	8.2316	-
0.2173	153100	8.2103	-
0.2175	153200	8.2358	-
0.2176	153300	8.2294	-
0.2177	153400	8.2398	-
0.2179	153500	8.209	-
0.2180	153600	8.2284	-
0.2182	153700	8.2317	-
0.2183	153800	8.2149	-
0.2184	153900	8.2429	-
0.2186	154000	8.2381	-
0.2187	154100	8.2179	-
0.2189	154200	8.2297	-
0.2190	154300	8.231	-
0.2192	154400	8.2173	-
0.2193	154500	8.2305	-
0.2194	154600	8.2306	-
0.2196	154700	8.2436	-
0.2197	154800	8.2377	-
0.2199	154900	8.2399	-
0.2200	155000	8.2157	-
0.2202	155100	8.2297	-
0.2203	155200	8.2276	-
0.2204	155300	8.227	-
0.2206	155400	8.2292	-
0.2207	155500	8.2248	-
0.2209	155600	8.2237	-
0.2210	155700	8.2092	-
0.2211	155800	8.213	-
0.2213	155900	8.2352	-
0.2214	156000	8.2198	-
0.2216	156100	8.2148	-
0.2217	156200	8.2128	-
0.2219	156300	8.1961	-
0.2220	156400	8.2389	-
0.2221	156500	8.2351	-
0.2223	156600	8.2283	-
0.2224	156700	8.2095	-
0.2226	156800	8.2101	-
0.2227	156900	8.2227	-
0.2228	157000	8.2305	-
0.2230	157100	8.212	-
0.2231	157200	8.24	-
0.2233	157300	8.217	-
0.2234	157400	8.2396	-
0.2236	157500	8.2388	-
0.2237	157600	8.2248	-
0.2238	157700	8.2408	-
0.2240	157800	8.2346	-
0.2241	157900	8.221	-
0.2243	158000	8.2286	-
0.2244	158100	8.2321	-
0.2246	158200	8.232	-
0.2247	158300	8.2303	-
0.2248	158400	8.2225	-
0.2250	158500	8.2131	-
0.2251	158600	8.2301	-
0.2253	158700	8.2119	-
0.2254	158800	8.2163	-
0.2255	158900	8.2364	-
0.2257	159000	8.2228	-
0.2258	159100	8.2215	-
0.2260	159200	8.2214	-
0.2261	159300	8.2292	-
0.2263	159400	8.2274	-
0.2264	159500	8.2197	-
0.2265	159600	8.2283	-
0.2267	159700	8.2459	-
0.2268	159800	8.2212	-
0.2270	159900	8.215	-
0.2271	160000	8.2173	-
0.2272	160100	8.208	-
0.2274	160200	8.2166	-
0.2275	160300	8.2064	-
0.2277	160400	8.2085	-
0.2278	160500	8.2184	-
0.2280	160600	8.2191	-
0.2281	160700	8.2089	-
0.2282	160800	8.2294	-
0.2284	160900	8.243	-
0.2285	161000	8.2342	-
0.2287	161100	8.2328	-
0.2288	161200	8.2387	-
0.2290	161300	8.2055	-
0.2291	161400	8.2122	-
0.2292	161500	8.2325	-
0.2294	161600	8.2004	-
0.2295	161700	8.228	-
0.2297	161800	8.2225	-
0.2298	161900	8.2216	-
0.2299	162000	8.2148	-
0.2301	162100	8.1804	-
0.2302	162200	8.2344	-
0.2304	162300	8.2114	-
0.2305	162400	8.2107	-
0.2307	162500	8.2062	-
0.2308	162600	8.2176	-
0.2309	162700	8.2184	-
0.2311	162800	8.211	-
0.2312	162900	8.227	-
0.2314	163000	8.2264	-
0.2315	163100	8.2192	-
0.2317	163200	8.2181	-
0.2318	163300	8.2001	-
0.2319	163400	8.2204	-
0.2321	163500	8.2218	-
0.2322	163600	8.2107	-
0.2324	163700	8.2022	-
0.2325	163800	8.2304	-
0.2326	163900	8.2309	-
0.2328	164000	8.2222	-
0.2329	164100	8.2024	-
0.2331	164200	8.2213	-
0.2332	164300	8.2043	-
0.2334	164400	8.2108	-
0.2335	164500	8.2121	-
0.2336	164600	8.2142	-
0.2338	164700	8.1931	-
0.2339	164800	8.2238	-
0.2341	164900	8.2194	-
0.2342	165000	8.2197	-
0.2343	165100	8.205	-
0.2345	165200	8.1952	-
0.2346	165300	8.2109	-
0.2348	165400	8.2056	-
0.2349	165500	8.2126	-
0.2351	165600	8.2122	-
0.2352	165700	8.2093	-
0.2353	165800	8.2318	-
0.2355	165900	8.1875	-
0.2356	166000	8.2088	-
0.2358	166100	8.1965	-
0.2359	166200	8.2021	-
0.2361	166300	8.2193	-
0.2362	166400	8.2237	-
0.2363	166500	8.2144	-
0.2365	166600	8.2164	-
0.2366	166700	8.2003	-
0.2368	166800	8.2147	-
0.2369	166900	8.1928	-
0.2370	167000	8.1986	-
0.2372	167100	8.2302	-
0.2373	167200	8.2221	-
0.2375	167300	8.211	-
0.2376	167400	8.2201	-
0.2378	167500	8.2143	-
0.2379	167600	8.1938	-
0.2380	167700	8.2121	-
0.2382	167800	8.2151	-
0.2383	167900	8.2198	-
0.2385	168000	8.2158	-
0.2386	168100	8.2179	-
0.2387	168200	8.2127	-
0.2389	168300	8.221	-
0.2390	168400	8.1815	-
0.2392	168500	8.2232	-
0.2393	168600	8.2055	-
0.2395	168700	8.2111	-
0.2396	168800	8.2237	-
0.2397	168900	8.2073	-
0.2399	169000	8.2172	-
0.2400	169100	8.2217	-
0.2402	169200	8.2412	-
0.2403	169300	8.211	-
0.2405	169400	8.2178	-
0.2406	169500	8.1837	-
0.2407	169600	8.2254	-
0.2409	169700	8.2071	-
0.2410	169800	8.2239	-
0.2412	169900	8.2046	-
0.2413	170000	8.2136	-
0.2414	170100	8.2091	-
0.2416	170200	8.2011	-
0.2417	170300	8.2019	-
0.2419	170400	8.2085	-
0.2420	170500	8.2127	-
0.2422	170600	8.2183	-
0.2423	170700	8.2074	-
0.2424	170800	8.1846	-
0.2426	170900	8.1994	-
0.2427	171000	8.2046	-
0.2429	171100	8.2156	-
0.2430	171200	8.2034	-
0.2431	171300	8.2109	-
0.2433	171400	8.2115	-
0.2434	171500	8.1909	-
0.2436	171600	8.1973	-
0.2437	171700	8.1989	-
0.2439	171800	8.2365	-
0.2440	171900	8.2152	-
0.2441	172000	8.2031	-
0.2443	172100	8.2052	-
0.2444	172200	8.2008	-
0.2446	172300	8.193	-
0.2447	172400	8.2022	-
0.2449	172500	8.2154	-
0.2450	172600	8.1975	-
0.2451	172700	8.2044	-
0.2453	172800	8.2071	-
0.2454	172900	8.1885	-
0.2456	173000	8.2169	-
0.2457	173100	8.1943	-
0.2458	173200	8.188	-
0.2460	173300	8.2096	-
0.2461	173400	8.1895	-
0.2463	173500	8.2102	-
0.2464	173600	8.2052	-
0.2466	173700	8.2094	-
0.2467	173800	8.1979	-
0.2468	173900	8.2407	-
0.2470	174000	8.2034	-
0.2471	174100	8.2251	-
0.2473	174200	8.2124	-
0.2474	174300	8.2089	-
0.2475	174400	8.1967	-
0.2477	174500	8.2177	-
0.2478	174600	8.1997	-
0.2480	174700	8.2073	-
0.2481	174800	8.1944	-
0.2483	174900	8.2012	-
0.2484	175000	8.2082	-
0.2485	175100	8.1914	-
0.2487	175200	8.2069	-
0.2488	175300	8.204	-
0.2490	175400	8.228	-
0.2491	175500	8.2075	-
0.2493	175600	8.1907	-
0.2494	175700	8.203	-
0.2495	175800	8.2135	-
0.2497	175900	8.2047	-
0.2498	176000	8.191	-
0.2500	176100	8.2161	-
0.2501	176200	8.2078	-
0.2502	176300	8.1894	-
0.2504	176400	8.2148	-
0.2505	176500	8.1959	-
0.2507	176600	8.2005	-
0.2508	176700	8.1905	-
0.2510	176800	8.211	-
0.2511	176900	8.1958	-
0.2512	177000	8.213	-
0.2514	177100	8.2107	-
0.2515	177200	8.2176	-
0.2517	177300	8.1861	-
0.2518	177400	8.1884	-
0.2519	177500	8.2004	-
0.2521	177600	8.2184	-
0.2522	177700	8.1985	-
0.2524	177800	8.1955	-
0.2525	177900	8.2079	-
0.2527	178000	8.1999	-
0.2528	178100	8.195	-
0.2529	178200	8.2081	-
0.2531	178300	8.193	-
0.2532	178400	8.2202	-
0.2534	178500	8.2212	-
0.2535	178600	8.2115	-
0.2537	178700	8.1897	-
0.2538	178800	8.2235	-
0.2539	178900	8.2194	-
0.2541	179000	8.1956	-
0.2542	179100	8.1994	-
0.2544	179200	8.1964	-
0.2545	179300	8.21	-
0.2546	179400	8.1926	-
0.2548	179500	8.2072	-
0.2549	179600	8.187	-
0.2551	179700	8.1928	-
0.2552	179800	8.2064	-
0.2554	179900	8.1895	-
0.2555	180000	8.2383	-
0.2556	180100	8.2155	-
0.2558	180200	8.2057	-
0.2559	180300	8.1972	-
0.2561	180400	8.1968	-
0.2562	180500	8.2002	-
0.2563	180600	8.1782	-
0.2565	180700	8.1928	-
0.2566	180800	8.1711	-
0.2568	180900	8.2044	-
0.2569	181000	8.1908	-
0.2571	181100	8.1745	-
0.2572	181200	8.1989	-
0.2573	181300	8.1904	-
0.2575	181400	8.1866	-
0.2576	181500	8.1982	-
0.2578	181600	8.197	-
0.2579	181700	8.1958	-
0.2581	181800	8.1799	-
0.2582	181900	8.1937	-
0.2583	182000	8.1942	-
0.2585	182100	8.211	-
0.2586	182200	8.1672	-
0.2588	182300	8.2115	-
0.2589	182400	8.2014	-
0.2590	182500	8.1567	-
0.2592	182600	8.187	-
0.2593	182700	8.1802	-
0.2595	182800	8.1859	-
0.2596	182900	8.1964	-
0.2598	183000	8.2142	-
0.2599	183100	8.1975	-
0.2600	183200	8.2068	-
0.2602	183300	8.2098	-
0.2603	183400	8.1944	-
0.2605	183500	8.1762	-
0.2606	183600	8.1963	-
0.2607	183700	8.1846	-
0.2609	183800	8.192	-
0.2610	183900	8.1683	-
0.2612	184000	8.2044	-
0.2613	184100	8.2049	-
0.2615	184200	8.2165	-
0.2616	184300	8.1845	-
0.2617	184400	8.2086	-
0.2619	184500	8.2185	-
0.2620	184600	8.187	-
0.2622	184700	8.1982	-
0.2623	184800	8.181	-
0.2625	184900	8.19	-
0.2626	185000	8.1919	-
0.2627	185100	8.1881	-
0.2629	185200	8.2069	-
0.2630	185300	8.2187	-
0.2632	185400	8.1608	-
0.2633	185500	8.1938	-
0.2634	185600	8.1936	-
0.2636	185700	8.1898	-
0.2637	185800	8.1877	-
0.2639	185900	8.2092	-
0.2640	186000	8.1699	-
0.2642	186100	8.1814	-
0.2643	186200	8.2108	-
0.2644	186300	8.1867	-
0.2646	186400	8.1846	-
0.2647	186500	8.1855	-
0.2649	186600	8.1886	-
0.2650	186700	8.2065	-
0.2651	186800	8.1791	-
0.2653	186900	8.1954	-
0.2654	187000	8.1912	-
0.2656	187100	8.1996	-
0.2657	187200	8.2194	-
0.2659	187300	8.1824	-
0.2660	187400	8.1911	-
0.2661	187500	8.1958	-
0.2663	187600	8.1762	-
0.2664	187700	8.1753	-
0.2666	187800	8.2064	-
0.2667	187900	8.1907	-
0.2669	188000	8.1856	-
0.2670	188100	8.1965	-
0.2671	188200	8.1976	-
0.2673	188300	8.1785	-
0.2674	188400	8.1668	-
0.2676	188500	8.2233	-
0.2677	188600	8.185	-
0.2678	188700	8.1941	-
0.2680	188800	8.1947	-
0.2681	188900	8.1922	-
0.2683	189000	8.1848	-
0.2684	189100	8.1887	-
0.2686	189200	8.1882	-
0.2687	189300	8.1889	-
0.2688	189400	8.169	-
0.2690	189500	8.1953	-
0.2691	189600	8.1952	-
0.2693	189700	8.1919	-
0.2694	189800	8.2081	-
0.2695	189900	8.1888	-
0.2697	190000	8.1892	-
0.2698	190100	8.1919	-
0.2700	190200	8.1999	-
0.2701	190300	8.1821	-
0.2703	190400	8.1868	-
0.2704	190500	8.1694	-
0.2705	190600	8.2179	-
0.2707	190700	8.1845	-
0.2708	190800	8.1916	-
0.2710	190900	8.1916	-
0.2711	191000	8.1594	-
0.2713	191100	8.1671	-
0.2714	191200	8.1852	-
0.2715	191300	8.1762	-
0.2717	191400	8.1757	-
0.2718	191500	8.1999	-
0.2720	191600	8.1851	-
0.2721	191700	8.1661	-
0.2722	191800	8.2108	-
0.2724	191900	8.2036	-
0.2725	192000	8.1859	-
0.2727	192100	8.1979	-
0.2728	192200	8.1769	-
0.2730	192300	8.1902	-
0.2731	192400	8.2051	-
0.2732	192500	8.162	-
0.2734	192600	8.1949	-
0.2735	192700	8.1831	-
0.2737	192800	8.1769	-
0.2738	192900	8.1916	-
0.2739	193000	8.1984	-
0.2741	193100	8.1879	-
0.2742	193200	8.19	-
0.2744	193300	8.1789	-
0.2745	193400	8.1904	-
0.2747	193500	8.1882	-
0.2748	193600	8.146	-
0.2749	193700	8.1887	-
0.2751	193800	8.1972	-
0.2752	193900	8.1936	-
0.2754	194000	8.1766	-
0.2755	194100	8.1745	-
0.2757	194200	8.1807	-
0.2758	194300	8.1731	-
0.2759	194400	8.1683	-
0.2761	194500	8.2083	-
0.2762	194600	8.1895	-
0.2764	194700	8.1692	-
0.2765	194800	8.1971	-
0.2766	194900	8.1587	-
0.2768	195000	8.2109	-
0.2769	195100	8.168	-
0.2771	195200	8.1839	-
0.2772	195300	8.1777	-
0.2774	195400	8.1801	-
0.2775	195500	8.1896	-
0.2776	195600	8.1734	-
0.2778	195700	8.1983	-
0.2779	195800	8.1615	-
0.2781	195900	8.1785	-
0.2782	196000	8.1883	-
0.2783	196100	8.1944	-
0.2785	196200	8.1956	-
0.2786	196300	8.1916	-
0.2788	196400	8.1804	-
0.2789	196500	8.1924	-
0.2791	196600	8.1828	-
0.2792	196700	8.1774	-
0.2793	196800	8.2039	-
0.2795	196900	8.169	-
0.2796	197000	8.2	-
0.2798	197100	8.1826	-
0.2799	197200	8.1968	-
0.2801	197300	8.1848	-
0.2802	197400	8.1795	-
0.2803	197500	8.1852	-
0.2805	197600	8.1962	-
0.2806	197700	8.1839	-
0.2808	197800	8.1864	-
0.2809	197900	8.1804	-
0.2810	198000	8.1825	-
0.2812	198100	8.1823	-
0.2813	198200	8.176	-
0.2815	198300	8.1834	-
0.2816	198400	8.2055	-
0.2818	198500	8.1791	-
0.2819	198600	8.212	-
0.2820	198700	8.1832	-
0.2822	198800	8.1773	-
0.2823	198900	8.1882	-
0.2825	199000	8.2013	-
0.2826	199100	8.178	-
0.2827	199200	8.2019	-
0.2829	199300	8.181	-
0.2830	199400	8.1844	-
0.2832	199500	8.1818	-
0.2833	199600	8.1853	-
0.2835	199700	8.1801	-
0.2836	199800	8.2054	-
0.2837	199900	8.1731	-
0.2839	200000	8.1769	8.1419
0.2840	200100	8.1738	-
0.2842	200200	8.178	-
0.2843	200300	8.181	-
0.2845	200400	8.192	-
0.2846	200500	8.1647	-
0.2847	200600	8.1926	-
0.2849	200700	8.1803	-
0.2850	200800	8.159	-
0.2852	200900	8.1766	-
0.2853	201000	8.1839	-
0.2854	201100	8.1849	-
0.2856	201200	8.1912	-
0.2857	201300	8.1984	-
0.2859	201400	8.1801	-
0.2860	201500	8.178	-
0.2862	201600	8.1585	-
0.2863	201700	8.1686	-
0.2864	201800	8.1861	-
0.2866	201900	8.1815	-
0.2867	202000	8.178	-
0.2869	202100	8.193	-
0.2870	202200	8.1923	-
0.2871	202300	8.1827	-
0.2873	202400	8.2	-
0.2874	202500	8.1715	-
0.2876	202600	8.1765	-
0.2877	202700	8.1859	-
0.2879	202800	8.1732	-
0.2880	202900	8.1918	-
0.2881	203000	8.191	-
0.2883	203100	8.1932	-
0.2884	203200	8.1885	-
0.2886	203300	8.16	-
0.2887	203400	8.1786	-
0.2889	203500	8.1815	-
0.2890	203600	8.1845	-
0.2891	203700	8.1869	-
0.2893	203800	8.1839	-
0.2894	203900	8.1612	-
0.2896	204000	8.1808	-
0.2897	204100	8.161	-
0.2898	204200	8.1929	-
0.2900	204300	8.1813	-
0.2901	204400	8.1579	-
0.2903	204500	8.1909	-
0.2904	204600	8.1743	-
0.2906	204700	8.1618	-
0.2907	204800	8.2	-
0.2908	204900	8.1777	-
0.2910	205000	8.1784	-
0.2911	205100	8.1794	-
0.2913	205200	8.1851	-
0.2914	205300	8.1752	-
0.2915	205400	8.1829	-
0.2917	205500	8.1625	-
0.2918	205600	8.2136	-
0.2920	205700	8.1667	-
0.2921	205800	8.1626	-
0.2923	205900	8.1934	-
0.2924	206000	8.1808	-
0.2925	206100	8.176	-
0.2927	206200	8.1905	-
0.2928	206300	8.1806	-
0.2930	206400	8.1804	-
0.2931	206500	8.1696	-
0.2933	206600	8.162	-
0.2934	206700	8.1732	-
0.2935	206800	8.1825	-
0.2937	206900	8.1844	-
0.2938	207000	8.1721	-
0.2940	207100	8.1881	-
0.2941	207200	8.2055	-
0.2942	207300	8.1805	-
0.2944	207400	8.1991	-
0.2945	207500	8.1591	-
0.2947	207600	8.1809	-
0.2948	207700	8.1672	-
0.2950	207800	8.1733	-
0.2951	207900	8.1827	-
0.2952	208000	8.1775	-
0.2954	208100	8.1844	-
0.2955	208200	8.1819	-
0.2957	208300	8.195	-
0.2958	208400	8.165	-
0.2959	208500	8.1712	-
0.2961	208600	8.1668	-
0.2962	208700	8.1955	-
0.2964	208800	8.1664	-
0.2965	208900	8.1619	-
0.2967	209000	8.1858	-
0.2968	209100	8.1565	-
0.2969	209200	8.1824	-
0.2971	209300	8.1865	-
0.2972	209400	8.1788	-
0.2974	209500	8.1895	-
0.2975	209600	8.187	-
0.2977	209700	8.1905	-
0.2978	209800	8.1777	-
0.2979	209900	8.1921	-
0.2981	210000	8.1748	-
0.2982	210100	8.1973	-
0.2984	210200	8.1727	-
0.2985	210300	8.1822	-
0.2986	210400	8.1737	-
0.2988	210500	8.1558	-
0.2989	210600	8.1777	-
0.2991	210700	8.172	-
0.2992	210800	8.1596	-
0.2994	210900	8.1773	-
0.2995	211000	8.1801	-
0.2996	211100	8.154	-
0.2998	211200	8.178	-
0.2999	211300	8.172	-
0.3001	211400	8.1731	-
0.3002	211500	8.1825	-
0.3004	211600	8.1825	-
0.3005	211700	8.1823	-
0.3006	211800	8.1827	-
0.3008	211900	8.181	-
0.3009	212000	8.1676	-
0.3011	212100	8.1569	-
0.3012	212200	8.167	-
0.3013	212300	8.1949	-
0.3015	212400	8.1822	-
0.3016	212500	8.1827	-
0.3018	212600	8.1719	-
0.3019	212700	8.1685	-
0.3021	212800	8.1767	-
0.3022	212900	8.1579	-
0.3023	213000	8.1726	-
0.3025	213100	8.1694	-
0.3026	213200	8.1657	-
0.3028	213300	8.1883	-
0.3029	213400	8.1447	-
0.3030	213500	8.173	-
0.3032	213600	8.1763	-
0.3033	213700	8.1813	-
0.3035	213800	8.1956	-
0.3036	213900	8.1719	-
0.3038	214000	8.1824	-
0.3039	214100	8.1724	-
0.3040	214200	8.2014	-
0.3042	214300	8.1657	-
0.3043	214400	8.1631	-
0.3045	214500	8.1721	-
0.3046	214600	8.1805	-
0.3048	214700	8.1887	-
0.3049	214800	8.1696	-
0.3050	214900	8.1719	-
0.3052	215000	8.1779	-
0.3053	215100	8.1517	-
0.3055	215200	8.1706	-
0.3056	215300	8.1587	-
0.3057	215400	8.15	-
0.3059	215500	8.1514	-
0.3060	215600	8.1537	-
0.3062	215700	8.1577	-
0.3063	215800	8.1531	-
0.3065	215900	8.1684	-
0.3066	216000	8.1681	-
0.3067	216100	8.1807	-
0.3069	216200	8.1749	-
0.3070	216300	8.1716	-
0.3072	216400	8.2036	-
0.3073	216500	8.1684	-
0.3074	216600	8.1697	-
0.3076	216700	8.155	-
0.3077	216800	8.1662	-
0.3079	216900	8.1659	-
0.3080	217000	8.1676	-
0.3082	217100	8.1448	-
0.3083	217200	8.182	-
0.3084	217300	8.1798	-
0.3086	217400	8.1573	-
0.3087	217500	8.1622	-
0.3089	217600	8.1778	-
0.3090	217700	8.1677	-
0.3092	217800	8.1694	-
0.3093	217900	8.1613	-
0.3094	218000	8.168	-
0.3096	218100	8.1663	-
0.3097	218200	8.1691	-
0.3099	218300	8.1766	-
0.3100	218400	8.1557	-
0.3101	218500	8.1763	-
0.3103	218600	8.1562	-
0.3104	218700	8.1656	-
0.3106	218800	8.1658	-
0.3107	218900	8.1715	-
0.3109	219000	8.1525	-
0.3110	219100	8.1819	-
0.3111	219200	8.1755	-
0.3113	219300	8.1922	-
0.3114	219400	8.1534	-
0.3116	219500	8.1668	-
0.3117	219600	8.178	-
0.3118	219700	8.1771	-
0.3120	219800	8.1564	-
0.3121	219900	8.1714	-
0.3123	220000	8.159	-
0.3124	220100	8.1837	-
0.3126	220200	8.1704	-
0.3127	220300	8.1865	-
0.3128	220400	8.182	-
0.3130	220500	8.1528	-
0.3131	220600	8.1799	-
0.3133	220700	8.1606	-
0.3134	220800	8.1564	-
0.3136	220900	8.1811	-
0.3137	221000	8.1764	-
0.3138	221100	8.1736	-
0.3140	221200	8.1525	-
0.3141	221300	8.1617	-
0.3143	221400	8.1924	-
0.3144	221500	8.1752	-
0.3145	221600	8.1671	-
0.3147	221700	8.1569	-
0.3148	221800	8.1489	-
0.3150	221900	8.1581	-
0.3151	222000	8.1783	-
0.3153	222100	8.1754	-
0.3154	222200	8.15	-
0.3155	222300	8.1797	-
0.3157	222400	8.1775	-
0.3158	222500	8.179	-
0.3160	222600	8.1643	-
0.3161	222700	8.1649	-
0.3162	222800	8.1574	-
0.3164	222900	8.1698	-
0.3165	223000	8.1583	-
0.3167	223100	8.1569	-
0.3168	223200	8.1835	-
0.3170	223300	8.1671	-
0.3171	223400	8.1431	-
0.3172	223500	8.1455	-
0.3174	223600	8.1627	-
0.3175	223700	8.1829	-
0.3177	223800	8.172	-
0.3178	223900	8.1662	-
0.3180	224000	8.1761	-
0.3181	224100	8.2014	-
0.3182	224200	8.1525	-
0.3184	224300	8.1488	-
0.3185	224400	8.1607	-
0.3187	224500	8.1857	-
0.3188	224600	8.1742	-
0.3189	224700	8.1593	-
0.3191	224800	8.1721	-
0.3192	224900	8.1856	-
0.3194	225000	8.151	-
0.3195	225100	8.1812	-
0.3197	225200	8.1692	-
0.3198	225300	8.1712	-
0.3199	225400	8.1659	-
0.3201	225500	8.1807	-
0.3202	225600	8.1402	-
0.3204	225700	8.1667	-
0.3205	225800	8.1728	-
0.3206	225900	8.1556	-
0.3208	226000	8.1557	-
0.3209	226100	8.1369	-
0.3211	226200	8.1695	-
0.3212	226300	8.1589	-
0.3214	226400	8.1767	-
0.3215	226500	8.1662	-
0.3216	226600	8.1559	-
0.3218	226700	8.1615	-
0.3219	226800	8.1748	-
0.3221	226900	8.1591	-
0.3222	227000	8.1648	-
0.3224	227100	8.1773	-
0.3225	227200	8.1611	-
0.3226	227300	8.1474	-
0.3228	227400	8.1592	-
0.3229	227500	8.1694	-
0.3231	227600	8.1523	-
0.3232	227700	8.1565	-
0.3233	227800	8.1597	-
0.3235	227900	8.1479	-
0.3236	228000	8.1481	-
0.3238	228100	8.1765	-
0.3239	228200	8.1614	-
0.3241	228300	8.177	-
0.3242	228400	8.1562	-
0.3243	228500	8.1734	-
0.3245	228600	8.1668	-
0.3246	228700	8.1787	-
0.3248	228800	8.1502	-
0.3249	228900	8.1553	-
0.3250	229000	8.1613	-
0.3252	229100	8.1569	-
0.3253	229200	8.1906	-
0.3255	229300	8.1834	-
0.3256	229400	8.1501	-
0.3258	229500	8.1473	-
0.3259	229600	8.1557	-
0.3260	229700	8.1904	-
0.3262	229800	8.1337	-
0.3263	229900	8.1774	-
0.3265	230000	8.1566	-
0.3266	230100	8.1618	-
0.3268	230200	8.171	-
0.3269	230300	8.1786	-
0.3270	230400	8.1686	-
0.3272	230500	8.149	-
0.3273	230600	8.1672	-
0.3275	230700	8.1704	-
0.3276	230800	8.16	-
0.3277	230900	8.1505	-
0.3279	231000	8.162	-
0.3280	231100	8.1515	-
0.3282	231200	8.149	-
0.3283	231300	8.1816	-
0.3285	231400	8.1501	-
0.3286	231500	8.1496	-
0.3287	231600	8.1592	-
0.3289	231700	8.1577	-
0.3290	231800	8.1509	-
0.3292	231900	8.1823	-
0.3293	232000	8.1784	-
0.3294	232100	8.165	-
0.3296	232200	8.158	-
0.3297	232300	8.1625	-
0.3299	232400	8.1663	-
0.3300	232500	8.1783	-
0.3302	232600	8.1812	-
0.3303	232700	8.1538	-
0.3304	232800	8.1943	-
0.3306	232900	8.163	-
0.3307	233000	8.1579	-
0.3309	233100	8.1609	-
0.3310	233200	8.1397	-
0.3312	233300	8.1473	-
0.3313	233400	8.1417	-
0.3314	233500	8.1568	-
0.3316	233600	8.1721	-
0.3317	233700	8.1546	-
0.3319	233800	8.1515	-
0.3320	233900	8.1231	-
0.3321	234000	8.1754	-
0.3323	234100	8.1432	-
0.3324	234200	8.1543	-
0.3326	234300	8.1795	-
0.3327	234400	8.1676	-
0.3329	234500	8.1727	-
0.3330	234600	8.1376	-
0.3331	234700	8.1856	-
0.3333	234800	8.1494	-
0.3334	234900	8.1852	-
0.3336	235000	8.1564	-
0.3337	235100	8.1595	-
0.3338	235200	8.176	-
0.3340	235300	8.1502	-
0.3341	235400	8.1789	-
0.3343	235500	8.1622	-
0.3344	235600	8.1517	-
0.3346	235700	8.1546	-
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0.4727	333000	8.1146	-
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0.4731	333300	8.0738	-
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0.4735	333600	8.1012	-
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0.4749	334600	8.1211	-
0.4751	334700	8.118	-
0.4752	334800	8.1023	-
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0.5399	380400	8.1	-
0.5401	380500	8.1028	-
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0.5436	383000	8.0659	-
0.5438	383100	8.1094	-
0.5439	383200	8.0656	-
0.5441	383300	8.1	-
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0.5445	383600	8.097	-
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0.5451	384000	8.0917	-
0.5452	384100	8.1043	-
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0.9715	684400	8.011	-
0.9716	684500	8.0625	-
0.9717	684600	8.0432	-
0.9719	684700	8.0467	-
0.9720	684800	8.0324	-
0.9722	684900	8.0182	-
0.9723	685000	8.0465	-
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0.9726	685200	8.0563	-
0.9727	685300	8.024	-
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0.9730	685500	8.0243	-
0.9732	685600	8.0276	-
0.9733	685700	8.0505	-
0.9734	685800	8.0539	-
0.9736	685900	8.0422	-
0.9737	686000	8.0466	-
0.9739	686100	8.0276	-
0.9740	686200	8.0217	-
0.9742	686300	8.0221	-
0.9743	686400	7.9935	-
0.9744	686500	8.0103	-
0.9746	686600	8.0292	-
0.9747	686700	8.0203	-
0.9749	686800	8.018	-
0.9750	686900	8.0292	-
0.9751	687000	8.0455	-
0.9753	687100	8.0631	-
0.9754	687200	8.0368	-
0.9756	687300	8.0284	-
0.9757	687400	8.0312	-
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0.9760	687600	8.072	-
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0.9764	687900	8.0251	-
0.9766	688000	8.0204	-
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0.9770	688300	8.0188	-
0.9771	688400	8.0542	-
0.9773	688500	8.0325	-
0.9774	688600	8.0247	-
0.9776	688700	8.0337	-
0.9777	688800	8.0553	-
0.9778	688900	8.0444	-
0.9780	689000	8.0304	-
0.9781	689100	8.0235	-
0.9783	689200	8.0259	-
0.9784	689300	8.0809	-
0.9786	689400	8.0264	-
0.9787	689500	7.9938	-
0.9788	689600	8.0141	-
0.9790	689700	8.043	-
0.9791	689800	8.0024	-
0.9793	689900	8.0492	-
0.9794	690000	8.0163	-
0.9795	690100	8.0386	-
0.9797	690200	8.035	-
0.9798	690300	8.0312	-
0.9800	690400	8.0016	-
0.9801	690500	8.0417	-
0.9803	690600	8.0216	-
0.9804	690700	8.0557	-
0.9805	690800	8.0108	-
0.9807	690900	8.0464	-
0.9808	691000	8.0449	-
0.9810	691100	8.0429	-
0.9811	691200	8.0091	-
0.9812	691300	8.0344	-
0.9814	691400	7.9862	-
0.9815	691500	8.041	-
0.9817	691600	8.0169	-
0.9818	691700	8.0094	-
0.9820	691800	8.0134	-
0.9821	691900	8.0253	-
0.9822	692000	8.0142	-
0.9824	692100	8.0351	-
0.9825	692200	8.0124	-
0.9827	692300	8.0414	-
0.9828	692400	8.0344	-
0.9830	692500	8.0492	-
0.9831	692600	8.0343	-
0.9832	692700	8.0514	-
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0.9835	692900	8.0269	-
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0.9838	693100	8.0227	-
0.9839	693200	8.0753	-
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0.9844	693500	8.0466	-
0.9845	693600	8.0345	-
0.9847	693700	8.0209	-
0.9848	693800	8.0352	-
0.9849	693900	8.0037	-
0.9851	694000	8.0119	-
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0.9864	694900	8.0432	-
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0.9871	695400	7.9973	-
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0.9875	695700	8.0195	-
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0.9879	696000	8.0143	-
0.9881	696100	8.0554	-
0.9882	696200	8.0501	-
0.9883	696300	8.0538	-
0.9885	696400	8.0686	-
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0.9888	696600	8.054	-
0.9889	696700	8.0524	-
0.9891	696800	8.0425	-
0.9892	696900	8.0673	-
0.9893	697000	8.0758	-
0.9895	697100	8.0552	-
0.9896	697200	7.9945	-
0.9898	697300	8.0196	-
0.9899	697400	8.0406	-
0.9900	697500	8.0491	-
0.9902	697600	8.052	-
0.9903	697700	8.0236	-
0.9905	697800	8.0379	-
0.9906	697900	8.0209	-
0.9908	698000	8.0085	-
0.9909	698100	8.025	-
0.9910	698200	8.0465	-
0.9912	698300	8.06	-
0.9913	698400	8.0254	-
0.9915	698500	8.0346	-
0.9916	698600	8.0326	-
0.9918	698700	8.0276	-
0.9919	698800	8.055	-
0.9920	698900	8.0462	-
0.9922	699000	8.0494	-
0.9923	699100	8.0268	-
0.9925	699200	8.0537	-
0.9926	699300	8.0398	-
0.9927	699400	8.0469	-
0.9929	699500	8.0713	-
0.9930	699600	8.029	-
0.9932	699700	8.0511	-
0.9933	699800	8.0897	-
0.9935	699900	8.0384	-
0.9936	700000	8.0535	-
0.9937	700100	8.0286	-
0.9939	700200	8.0389	-
0.9940	700300	7.993	-
0.9942	700400	8.033	-
0.9943	700500	8.0329	-
0.9944	700600	8.0259	-
0.9946	700700	8.0322	-
0.9947	700800	8.0247	-
0.9949	700900	8.0336	-
0.9950	701000	8.0545	-
0.9952	701100	8.0301	-
0.9953	701200	8.0519	-
0.9954	701300	8.0566	-
0.9956	701400	8.0068	-
0.9957	701500	8.0441	-
0.9959	701600	8.0642	-
0.9960	701700	8.0276	-
0.9962	701800	8.0362	-
0.9963	701900	8.0341	-
0.9964	702000	8.0606	-
0.9966	702100	8.0404	-
0.9967	702200	8.0357	-
0.9969	702300	8.0081	-
0.9970	702400	8.029	-
0.9971	702500	8.0383	-
0.9973	702600	8.0381	-
0.9974	702700	8.0255	-
0.9976	702800	8.0145	-
0.9977	702900	8.0404	-
0.9979	703000	8.0035	-
0.9980	703100	8.0388	-
0.9981	703200	8.0306	-
0.9983	703300	8.0054	-
0.9984	703400	8.0136	-
0.9986	703500	8.0695	-
0.9987	703600	8.0077	-
0.9988	703700	8.0227	-
0.9990	703800	8.04	-
0.9991	703900	8.0386	-
0.9993	704000	8.0313	-
0.9994	704100	8.0434	-
0.9996	704200	8.0533	-
0.9997	704300	8.0308	-
0.9998	704400	8.0218	-
1.0000	704500	8.0418	-

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},
}

Downloads last month: 8

Safetensors

Model size

22.7M params

Tensor type

F32

Model tree for KhaledReda/all-MiniLM-L6-v30-pair_score

Base model

sentence-transformers/all-MiniLM-L6-v2

Finetuned

(743)

this model

Dataset used to train KhaledReda/all-MiniLM-L6-v30-pair_score

Paper for KhaledReda/all-MiniLM-L6-v30-pair_score

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Paper • 1908.10084 • Published Aug 27, 2019 • 11