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language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:60255832
  - loss:CoSENTLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
  - source_sentence: pomegranate manoucheh
    sentences:
      - renleek hanger rack
      - avocado chicken sandwich
      - metal bead necklace
  - source_sentence: stimulating hair growth gel
    sentences:
      - maple waffle
      - juahayna
      - nano treat shampoo
  - source_sentence: truffle potatoes
    sentences:
      - backless cover up
      - hand wash
      - phenoxyethanol eye cream
  - source_sentence: sulfatefree cream
    sentences:
      - off flowers
      - roasted beef salad
      - soft care silicone insole
  - source_sentence: juhayna happy kitchen
    sentences:
      - vichy densi solutions shampoo
      - hummus tartine
      - stainless pizza wheel
datasets:
  - KhaledReda/pairs_with_scores_v18_tag_true_positives
pipeline_tag: sentence-similarity
library_name: sentence-transformers

all-MiniLM-L6-v22-pair_score

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the pairs_with_scores_v18_tag_true_positives 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_v18_tag_true_positives
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 = [
    'juhayna happy kitchen',
    'hummus tartine',
    'vichy densi solutions shampoo',
]
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, 0.6268, 0.5520],
#         [0.6268, 1.0000, 0.5609],
#         [0.5520, 0.5609, 1.0000]])

Training Details

Training Dataset

pairs_with_scores_v18_tag_true_positives

Dataset: pairs_with_scores_v18_tag_true_positives at 8a57fef
Size: 60,255,832 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: 5.83 tokens
max: 15 tokens

min: 3 tokens
mean: 6.02 tokens
max: 18 tokens

min: 0.13
mean: 0.44
max: 1.0
Samples:

sentence1 sentence2 score

flamingo birds print tshirt racket sports tshirt 0.8214350938796997

tuna avocado essential 0.2139240205287933

nkd skn knife presto 0.3085910677909851

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 5.83 tokens max: 15 tokens	min: 3 tokens mean: 6.02 tokens max: 18 tokens	min: 0.13 mean: 0.44 max: 1.0

sentence1	sentence2	score
`flamingo birds print tshirt`	`racket sports tshirt`	`0.8214350938796997`
`tuna avocado`	`essential`	`0.2139240205287933`
`nkd skn`	`knife presto`	`0.3085910677909851`

Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

pairs_with_scores_v18_tag_true_positives

Dataset: pairs_with_scores_v18_tag_true_positives at 8a57fef
Size: 302,794 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: 5.83 tokens
max: 17 tokens

min: 3 tokens
mean: 6.04 tokens
max: 18 tokens

min: 0.14
mean: 0.43
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 5.83 tokens max: 17 tokens	min: 3 tokens mean: 6.04 tokens max: 18 tokens	min: 0.14 mean: 0.43 max: 1.0

Samples:

sentence1	sentence2	score
`soap making gift set`	`soft towel`	`0.2569767236709594`
`hummus appetizer`	`nude arch bowl minimalist design unique`	`0.2555197477340698`
`body lotion`	`nourish scrub`	`0.2833480834960937`

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
0.0002	100	11.9355	-
0.0004	200	11.9443	-
0.0006	300	11.8888	-
0.0008	400	11.7854	-
0.0011	500	11.956	-
0.0013	600	11.9422	-
0.0015	700	11.5319	-
0.0017	800	11.4144	-
0.0019	900	11.249	-
0.0021	1000	11.06	-
0.0023	1100	11.1879	-
0.0025	1200	10.7179	-
0.0028	1300	10.7543	-
0.0030	1400	10.3764	-
0.0032	1500	10.3119	-
0.0034	1600	10.052	-
0.0036	1700	9.9764	-
0.0038	1800	9.7559	-
0.0040	1900	9.6324	-
0.0042	2000	9.5302	-
0.0045	2100	9.4318	-
0.0047	2200	9.2945	-
0.0049	2300	9.1265	-
0.0051	2400	9.0153	-
0.0053	2500	8.8989	-
0.0055	2600	8.8649	-
0.0057	2700	8.7748	-
0.0059	2800	8.7246	-
0.0062	2900	8.6901	-
0.0064	3000	8.6639	-
0.0066	3100	8.6443	-
0.0068	3200	8.6443	-
0.0070	3300	8.6002	-
0.0072	3400	8.6047	-
0.0074	3500	8.5959	-
0.0076	3600	8.5673	-
0.0079	3700	8.5786	-
0.0081	3800	8.579	-
0.0083	3900	8.5793	-
0.0085	4000	8.5609	-
0.0087	4100	8.5394	-
0.0089	4200	8.5465	-
0.0091	4300	8.5587	-
0.0093	4400	8.5519	-
0.0096	4500	8.5332	-
0.0098	4600	8.5314	-
0.0100	4700	8.523	-
0.0102	4800	8.5315	-
0.0104	4900	8.5323	-
0.0106	5000	8.5198	-
0.0108	5100	8.5094	-
0.0110	5200	8.5085	-
0.0113	5300	8.5091	-
0.0115	5400	8.4985	-
0.0117	5500	8.5123	-
0.0119	5600	8.493	-
0.0121	5700	8.4985	-
0.0123	5800	8.496	-
0.0125	5900	8.496	-
0.0127	6000	8.4983	-
0.0130	6100	8.4959	-
0.0132	6200	8.4832	-
0.0134	6300	8.4632	-
0.0136	6400	8.4751	-
0.0138	6500	8.4718	-
0.0140	6600	8.4748	-
0.0142	6700	8.4655	-
0.0144	6800	8.4696	-
0.0147	6900	8.4684	-
0.0149	7000	8.4636	-
0.0151	7100	8.4447	-
0.0153	7200	8.4623	-
0.0155	7300	8.4637	-
0.0157	7400	8.4579	-
0.0159	7500	8.4579	-
0.0161	7600	8.452	-
0.0164	7700	8.4556	-
0.0166	7800	8.4286	-
0.0168	7900	8.4422	-
0.0170	8000	8.4435	-
0.0172	8100	8.4352	-
0.0174	8200	8.4501	-
0.0176	8300	8.4356	-
0.0178	8400	8.436	-
0.0181	8500	8.4397	-
0.0183	8600	8.4342	-
0.0185	8700	8.4298	-
0.0187	8800	8.4309	-
0.0189	8900	8.4257	-
0.0191	9000	8.4318	-
0.0193	9100	8.4331	-
0.0195	9200	8.4096	-
0.0198	9300	8.4232	-
0.0200	9400	8.4272	-
0.0202	9500	8.4236	-
0.0204	9600	8.4306	-
0.0206	9700	8.4242	-
0.0208	9800	8.4024	-
0.0210	9900	8.4275	-
0.0212	10000	8.42	-
0.0215	10100	8.4098	-
0.0217	10200	8.4036	-
0.0219	10300	8.4114	-
0.0221	10400	8.3996	-
0.0223	10500	8.4021	-
0.0225	10600	8.3959	-
0.0227	10700	8.4024	-
0.0229	10800	8.408	-
0.0232	10900	8.3889	-
0.0234	11000	8.4007	-
0.0236	11100	8.4091	-
0.0238	11200	8.4013	-
0.0240	11300	8.3921	-
0.0242	11400	8.4079	-
0.0244	11500	8.3897	-
0.0246	11600	8.3885	-
0.0249	11700	8.3996	-
0.0251	11800	8.3729	-
0.0253	11900	8.3965	-
0.0255	12000	8.3901	-
0.0257	12100	8.3811	-
0.0259	12200	8.387	-
0.0261	12300	8.3842	-
0.0263	12400	8.387	-
0.0266	12500	8.3823	-
0.0268	12600	8.3788	-
0.0270	12700	8.3767	-
0.0272	12800	8.3665	-
0.0274	12900	8.3781	-
0.0276	13000	8.3822	-
0.0278	13100	8.3734	-
0.0280	13200	8.3724	-
0.0283	13300	8.3869	-
0.0285	13400	8.3685	-
0.0287	13500	8.3775	-
0.0289	13600	8.3659	-
0.0291	13700	8.3547	-
0.0293	13800	8.3652	-
0.0295	13900	8.3621	-
0.0297	14000	8.3655	-
0.0300	14100	8.3502	-
0.0302	14200	8.35	-
0.0304	14300	8.3666	-
0.0306	14400	8.3669	-
0.0308	14500	8.3479	-
0.0310	14600	8.3556	-
0.0312	14700	8.3424	-
0.0314	14800	8.3638	-
0.0317	14900	8.3538	-
0.0319	15000	8.3638	-
0.0321	15100	8.3585	-
0.0323	15200	8.3435	-
0.0325	15300	8.3636	-
0.0327	15400	8.3454	-
0.0329	15500	8.337	-
0.0331	15600	8.3516	-
0.0334	15700	8.3527	-
0.0336	15800	8.3507	-
0.0338	15900	8.3586	-
0.0340	16000	8.3394	-
0.0342	16100	8.3352	-
0.0344	16200	8.3451	-
0.0346	16300	8.3387	-
0.0348	16400	8.3379	-
0.0351	16500	8.3375	-
0.0353	16600	8.3284	-
0.0355	16700	8.3253	-
0.0357	16800	8.3462	-
0.0359	16900	8.3227	-
0.0361	17000	8.3286	-
0.0363	17100	8.335	-
0.0365	17200	8.3254	-
0.0367	17300	8.3375	-
0.0370	17400	8.3225	-
0.0372	17500	8.3369	-
0.0374	17600	8.3209	-
0.0376	17700	8.3118	-
0.0378	17800	8.3154	-
0.0380	17900	8.316	-
0.0382	18000	8.3198	-
0.0384	18100	8.3183	-
0.0387	18200	8.3139	-
0.0389	18300	8.3258	-
0.0391	18400	8.3235	-
0.0393	18500	8.3221	-
0.0395	18600	8.3186	-
0.0397	18700	8.3188	-
0.0399	18800	8.3143	-
0.0401	18900	8.3182	-
0.0404	19000	8.3211	-
0.0406	19100	8.3181	-
0.0408	19200	8.3224	-
0.0410	19300	8.3157	-
0.0412	19400	8.3266	-
0.0414	19500	8.3084	-
0.0416	19600	8.308	-
0.0418	19700	8.3116	-
0.0421	19800	8.3094	-
0.0423	19900	8.2954	-
0.0425	20000	8.3053	-
0.0427	20100	8.3097	-
0.0429	20200	8.3217	-
0.0431	20300	8.3062	-
0.0433	20400	8.313	-
0.0435	20500	8.3083	-
0.0438	20600	8.2854	-
0.0440	20700	8.2922	-
0.0442	20800	8.3034	-
0.0444	20900	8.3009	-
0.0446	21000	8.3033	-
0.0448	21100	8.2918	-
0.0450	21200	8.2889	-
0.0452	21300	8.3089	-
0.0455	21400	8.2926	-
0.0457	21500	8.2895	-
0.0459	21600	8.2986	-
0.0461	21700	8.292	-
0.0463	21800	8.2933	-
0.0465	21900	8.2741	-
0.0467	22000	8.2849	-
0.0469	22100	8.2962	-
0.0472	22200	8.2991	-
0.0474	22300	8.2947	-
0.0476	22400	8.2814	-
0.0478	22500	8.3005	-
0.0480	22600	8.2837	-
0.0482	22700	8.2932	-
0.0484	22800	8.2896	-
0.0486	22900	8.2973	-
0.0489	23000	8.2853	-
0.0491	23100	8.3015	-
0.0493	23200	8.2739	-
0.0495	23300	8.2726	-
0.0497	23400	8.3011	-
0.0499	23500	8.2981	-
0.0501	23600	8.292	-
0.0503	23700	8.2874	-
0.0506	23800	8.2853	-
0.0508	23900	8.2777	-
0.0510	24000	8.2802	-
0.0512	24100	8.2652	-
0.0514	24200	8.2632	-
0.0516	24300	8.2637	-
0.0518	24400	8.2686	-
0.0520	24500	8.2751	-
0.0523	24600	8.2685	-
0.0525	24700	8.2906	-
0.0527	24800	8.2811	-
0.0529	24900	8.2918	-
0.0531	25000	8.2756	-
0.0533	25100	8.2651	-
0.0535	25200	8.2462	-
0.0537	25300	8.2702	-
0.0540	25400	8.2799	-
0.0542	25500	8.2665	-
0.0544	25600	8.2669	-
0.0546	25700	8.2607	-
0.0548	25800	8.2702	-
0.0550	25900	8.2703	-
0.0552	26000	8.2614	-
0.0554	26100	8.2726	-
0.0557	26200	8.2649	-
0.0559	26300	8.2708	-
0.0561	26400	8.2656	-
0.0563	26500	8.2521	-
0.0565	26600	8.2587	-
0.0567	26700	8.2516	-
0.0569	26800	8.2589	-
0.0571	26900	8.2679	-
0.0574	27000	8.2674	-
0.0576	27100	8.2472	-
0.0578	27200	8.2473	-
0.0580	27300	8.2369	-
0.0582	27400	8.2517	-
0.0584	27500	8.2576	-
0.0586	27600	8.265	-
0.0588	27700	8.2371	-
0.0591	27800	8.2472	-
0.0593	27900	8.2436	-
0.0595	28000	8.2448	-
0.0597	28100	8.253	-
0.0599	28200	8.2649	-
0.0601	28300	8.2498	-
0.0603	28400	8.2485	-
0.0605	28500	8.2479	-
0.0608	28600	8.2484	-
0.0610	28700	8.2577	-
0.0612	28800	8.2449	-
0.0614	28900	8.2405	-
0.0616	29000	8.2579	-
0.0618	29100	8.2549	-
0.0620	29200	8.2475	-
0.0622	29300	8.2465	-
0.0625	29400	8.2452	-
0.0627	29500	8.2516	-
0.0629	29600	8.2371	-
0.0631	29700	8.2459	-
0.0633	29800	8.2465	-
0.0635	29900	8.2394	-
0.0637	30000	8.2352	-
0.0639	30100	8.2371	-
0.0642	30200	8.2295	-
0.0644	30300	8.2533	-
0.0646	30400	8.2459	-
0.0648	30500	8.2373	-
0.0650	30600	8.241	-
0.0652	30700	8.2291	-
0.0654	30800	8.2316	-
0.0656	30900	8.2417	-
0.0659	31000	8.2275	-
0.0661	31100	8.2268	-
0.0663	31200	8.2305	-
0.0665	31300	8.2326	-
0.0667	31400	8.2387	-
0.0669	31500	8.2398	-
0.0671	31600	8.2468	-
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0.1759	82800	8.0646	-
0.1761	82900	8.0611	-
0.1763	83000	8.0973	-
0.1765	83100	8.041	-
0.1767	83200	8.062	-
0.1770	83300	8.0595	-
0.1772	83400	8.0819	-
0.1774	83500	8.0792	-
0.1776	83600	8.0638	-
0.1778	83700	8.0546	-
0.1780	83800	8.0675	-
0.1782	83900	8.0666	-
0.1784	84000	8.0604	-
0.1787	84100	8.056	-
0.1789	84200	8.0637	-
0.1791	84300	8.068	-
0.1793	84400	8.0741	-
0.1795	84500	8.0746	-
0.1797	84600	8.061	-
0.1799	84700	8.0653	-
0.1801	84800	8.0553	-
0.1804	84900	8.058	-
0.1806	85000	8.0604	-
0.1808	85100	8.0802	-
0.1810	85200	8.0524	-
0.1812	85300	8.0704	-
0.1814	85400	8.0862	-
0.1816	85500	8.0573	-
0.1818	85600	8.0644	-
0.1821	85700	8.0637	-
0.1823	85800	8.0572	-
0.1825	85900	8.074	-
0.1827	86000	8.0827	-
0.1829	86100	8.0665	-
0.1831	86200	8.0662	-
0.1833	86300	8.0907	-
0.1835	86400	8.0767	-
0.1837	86500	8.0755	-
0.1840	86600	8.076	-
0.1842	86700	8.0456	-
0.1844	86800	8.0675	-
0.1846	86900	8.0671	-
0.1848	87000	8.0685	-
0.1850	87100	8.0706	-
0.1852	87200	8.0551	-
0.1854	87300	8.0596	-
0.1857	87400	8.0575	-
0.1859	87500	8.066	-
0.1861	87600	8.0649	-
0.1863	87700	8.0383	-
0.1865	87800	8.0442	-
0.1867	87900	8.082	-
0.1869	88000	8.0396	-
0.1871	88100	8.0727	-
0.1874	88200	8.0542	-
0.1876	88300	8.0685	-
0.1878	88400	8.0396	-
0.1880	88500	8.0487	-
0.1882	88600	8.0305	-
0.1884	88700	8.0564	-
0.1886	88800	8.0493	-
0.1888	88900	8.048	-
0.1891	89000	8.0526	-
0.1893	89100	8.0487	-
0.1895	89200	8.0449	-
0.1897	89300	8.0721	-
0.1899	89400	8.061	-
0.1901	89500	8.0428	-
0.1903	89600	8.0527	-
0.1905	89700	8.0396	-
0.1908	89800	8.0541	-
0.1910	89900	8.0741	-
0.1912	90000	8.0463	-
0.1914	90100	8.0531	-
0.1916	90200	8.052	-
0.1918	90300	8.052	-
0.1920	90400	8.0518	-
0.1922	90500	8.0542	-
0.1925	90600	8.0419	-
0.1927	90700	8.0498	-
0.1929	90800	8.0834	-
0.1931	90900	8.0696	-
0.1933	91000	8.0533	-
0.1935	91100	8.0393	-
0.1937	91200	8.0458	-
0.1939	91300	8.0432	-
0.1942	91400	8.0494	-
0.1944	91500	8.0559	-
0.1946	91600	8.0405	-
0.1948	91700	8.0303	-
0.1950	91800	8.0509	-
0.1952	91900	8.0641	-
0.1954	92000	8.054	-
0.1956	92100	8.0619	-
0.1959	92200	8.0389	-
0.1961	92300	8.0702	-
0.1963	92400	8.0785	-
0.1965	92500	8.0512	-
0.1967	92600	8.0639	-
0.1969	92700	8.054	-
0.1971	92800	8.0512	-
0.1973	92900	8.0644	-
0.1976	93000	8.0631	-
0.1978	93100	8.0386	-
0.1980	93200	8.0662	-
0.1982	93300	8.0543	-
0.1984	93400	8.053	-
0.1986	93500	8.0502	-
0.1988	93600	8.0409	-
0.1990	93700	8.0348	-
0.1993	93800	8.0427	-
0.1995	93900	8.0433	-
0.1997	94000	8.0498	-
0.1999	94100	8.0371	-
0.2001	94200	8.0506	-
0.2003	94300	8.0511	-
0.2005	94400	8.0514	-
0.2007	94500	8.0571	-
0.2010	94600	8.0468	-
0.2012	94700	8.0447	-
0.2014	94800	8.0393	-
0.2016	94900	8.0534	-
0.2018	95000	8.0497	-
0.2020	95100	8.0407	-
0.2022	95200	8.0316	-
0.2024	95300	8.0371	-
0.2027	95400	8.0345	-
0.2029	95500	8.0435	-
0.2031	95600	8.0569	-
0.2033	95700	8.046	-
0.2035	95800	8.0505	-
0.2037	95900	8.0452	-
0.2039	96000	8.0643	-
0.2041	96100	8.047	-
0.2044	96200	8.0325	-
0.2046	96300	8.0428	-
0.2048	96400	8.0513	-
0.2050	96500	8.0449	-
0.2052	96600	8.0463	-
0.2054	96700	8.0713	-
0.2056	96800	8.0385	-
0.2058	96900	8.0477	-
0.2061	97000	8.0519	-
0.2063	97100	8.0536	-
0.2065	97200	8.0354	-
0.2067	97300	8.0381	-
0.2069	97400	8.0336	-
0.2071	97500	8.0404	-
0.2073	97600	8.0418	-
0.2075	97700	8.0484	-
0.2078	97800	8.0314	-
0.2080	97900	8.0402	-
0.2082	98000	8.0571	-
0.2084	98100	8.0334	-
0.2086	98200	8.0469	-
0.2088	98300	8.0468	-
0.2090	98400	8.0454	-
0.2092	98500	8.0306	-
0.2095	98600	8.0391	-
0.2097	98700	8.037	-
0.2099	98800	8.031	-
0.2101	98900	8.0257	-
0.2103	99000	8.0608	-
0.2105	99100	8.0339	-
0.2107	99200	8.0571	-
0.2109	99300	8.045	-
0.2112	99400	8.0208	-
0.2114	99500	8.0576	-
0.2116	99600	8.0462	-
0.2118	99700	8.033	-
0.2120	99800	8.0157	-
0.2122	99900	8.0607	-
0.2124	100000	8.0672	-
0.2126	100100	8.04	-
0.2129	100200	8.0422	-
0.2131	100300	8.039	-
0.2133	100400	8.0414	-
0.2135	100500	8.0585	-
0.2137	100600	8.0636	-
0.2139	100700	8.0498	-
0.2141	100800	8.0701	-
0.2143	100900	8.026	-
0.2146	101000	8.0443	-
0.2148	101100	8.0635	-
0.2150	101200	8.0396	-
0.2152	101300	8.0289	-
0.2154	101400	8.0314	-
0.2156	101500	8.0502	-
0.2158	101600	8.0377	-
0.2160	101700	8.0489	-
0.2163	101800	8.0281	-
0.2165	101900	8.0187	-
0.2167	102000	8.0341	-
0.2169	102100	8.0382	-
0.2171	102200	8.0356	-
0.2173	102300	8.0262	-
0.2175	102400	8.0751	-
0.2177	102500	8.0525	-
0.2180	102600	8.0274	-
0.2182	102700	8.0516	-
0.2184	102800	8.0326	-
0.2186	102900	8.0528	-
0.2188	103000	8.0112	-
0.2190	103100	8.0398	-
0.2192	103200	8.026	-
0.2194	103300	8.0457	-
0.2196	103400	8.0383	-
0.2199	103500	8.0319	-
0.2201	103600	8.0282	-
0.2203	103700	8.0438	-
0.2205	103800	8.0221	-
0.2207	103900	8.0291	-
0.2209	104000	8.0239	-
0.2211	104100	8.0194	-
0.2213	104200	8.0548	-
0.2216	104300	8.051	-
0.2218	104400	8.0269	-
0.2220	104500	7.9988	-
0.2222	104600	8.024	-
0.2224	104700	8.015	-
0.2226	104800	8.0153	-
0.2228	104900	8.041	-
0.2230	105000	8.0305	-
0.2233	105100	8.0319	-
0.2235	105200	8.0608	-
0.2237	105300	8.0189	-
0.2239	105400	8.0285	-
0.2241	105500	8.0285	-
0.2243	105600	8.0306	-
0.2245	105700	8.034	-
0.2247	105800	8.0364	-
0.2250	105900	8.0364	-
0.2252	106000	8.0437	-
0.2254	106100	8.0258	-
0.2256	106200	8.0143	-
0.2258	106300	8.0382	-
0.2260	106400	8.0253	-
0.2262	106500	8.0179	-
0.2264	106600	8.0492	-
0.2267	106700	8.0308	-
0.2269	106800	8.0138	-
0.2271	106900	8.0337	-
0.2273	107000	8.0296	-
0.2275	107100	8.0357	-
0.2277	107200	8.017	-
0.2279	107300	8.0489	-
0.2281	107400	8.0088	-
0.2284	107500	8.0245	-
0.2286	107600	8.0495	-
0.2288	107700	8.0061	-
0.2290	107800	8.0284	-
0.2292	107900	7.9953	-
0.2294	108000	8.0437	-
0.2296	108100	8.0313	-
0.2298	108200	8.0152	-
0.2301	108300	8.0369	-
0.2303	108400	8.0402	-
0.2305	108500	8.0023	-
0.2307	108600	8.0327	-
0.2309	108700	8.0161	-
0.2311	108800	8.0693	-
0.2313	108900	8.018	-
0.2315	109000	8.0373	-
0.2318	109100	8.0115	-
0.2320	109200	8.0331	-
0.2322	109300	8.0441	-
0.2324	109400	8.0163	-
0.2326	109500	8.021	-
0.2328	109600	8.0143	-
0.2330	109700	8.0112	-
0.2332	109800	8.0163	-
0.2335	109900	8.0313	-
0.2337	110000	8.0238	-
0.2339	110100	8.021	-
0.2341	110200	8.053	-
0.2343	110300	8.021	-
0.2345	110400	8.0318	-
0.2347	110500	8.0378	-
0.2349	110600	8.0154	-
0.2352	110700	8.017	-
0.2354	110800	8.043	-
0.2356	110900	8.0447	-
0.2358	111000	8.0188	-
0.2360	111100	8.0351	-
0.2362	111200	8.0095	-
0.2364	111300	8.0305	-
0.2366	111400	8.0348	-
0.2369	111500	8.0456	-
0.2371	111600	8.0271	-
0.2373	111700	8.0246	-
0.2375	111800	8.0232	-
0.2377	111900	8.0221	-
0.2379	112000	8.0129	-
0.2381	112100	8.033	-
0.2383	112200	8.0345	-
0.2386	112300	8.0283	-
0.2388	112400	8.0025	-
0.2390	112500	8.0311	-
0.2392	112600	8.0398	-
0.2394	112700	8.016	-
0.2396	112800	8.0092	-
0.2398	112900	8.0287	-
0.2400	113000	8.0101	-
0.2403	113100	8.0311	-
0.2405	113200	8.0185	-
0.2407	113300	8.0084	-
0.2409	113400	8.0297	-
0.2411	113500	8.0184	-
0.2413	113600	8.0252	-
0.2415	113700	8.018	-
0.2417	113800	8.0006	-
0.2420	113900	8.0147	-
0.2422	114000	8.0175	-
0.2424	114100	8.0331	-
0.2426	114200	8.0209	-
0.2428	114300	8.0146	-
0.2430	114400	8.0103	-
0.2432	114500	7.9957	-
0.2434	114600	8.0108	-
0.2437	114700	8.0084	-
0.2439	114800	8.0167	-
0.2441	114900	8.0107	-
0.2443	115000	8.0436	-
0.2445	115100	7.9949	-
0.2447	115200	8.032	-
0.2449	115300	8.0237	-
0.2451	115400	7.9932	-
0.2454	115500	8.0286	-
0.2456	115600	8.0369	-
0.2458	115700	8.0188	-
0.2460	115800	8.0188	-
0.2462	115900	8.0028	-
0.2464	116000	7.9963	-
0.2466	116100	8.013	-
0.2468	116200	8.0131	-
0.2471	116300	8.018	-
0.2473	116400	8.0207	-
0.2475	116500	8.0071	-
0.2477	116600	8.004	-
0.2479	116700	7.9987	-
0.2481	116800	8.0399	-
0.2483	116900	8.0204	-
0.2485	117000	8.0257	-
0.2488	117100	8.0227	-
0.2490	117200	8.0116	-
0.2492	117300	8.0291	-
0.2494	117400	8.039	-
0.2496	117500	8.0193	-
0.2498	117600	8.0261	-
0.2500	117700	8.0241	-
0.2502	117800	8.0186	-
0.2505	117900	8.0207	-
0.2507	118000	8.0168	-
0.2509	118100	8.0428	-
0.2511	118200	8.0357	-
0.2513	118300	8.0144	-
0.2515	118400	8.03	-
0.2517	118500	8.0297	-
0.2519	118600	8.0207	-
0.2522	118700	8.0039	-
0.2524	118800	8.0187	-
0.2526	118900	8.0241	-
0.2528	119000	8.0178	-
0.2530	119100	8.0069	-
0.2532	119200	7.9867	-
0.2534	119300	8.0329	-
0.2536	119400	8.0218	-
0.2539	119500	7.9971	-
0.2541	119600	8.0274	-
0.2543	119700	8.0075	-
0.2545	119800	8.0298	-
0.2547	119900	8.0064	-
0.2549	120000	8.0263	-
0.2551	120100	8.0138	-
0.2553	120200	8.0134	-
0.2556	120300	8.0158	-
0.2558	120400	8.0071	-
0.2560	120500	8.0088	-
0.2562	120600	8.0093	-
0.2564	120700	8.0104	-
0.2566	120800	8.0101	-
0.2568	120900	8.0142	-
0.2570	121000	8.0057	-
0.2572	121100	7.9966	-
0.2575	121200	8.0289	-
0.2577	121300	8.0016	-
0.2579	121400	8.0181	-
0.2581	121500	7.9943	-
0.2583	121600	7.9949	-
0.2585	121700	8.0179	-
0.2587	121800	8.0067	-
0.2589	121900	7.9957	-
0.2592	122000	8.0139	-
0.2594	122100	8.017	-
0.2596	122200	8.0	-
0.2598	122300	8.0075	-
0.2600	122400	7.9973	-
0.2602	122500	8.0111	-
0.2604	122600	8.0127	-
0.2606	122700	8.0213	-
0.2609	122800	7.9969	-
0.2611	122900	7.9981	-
0.2613	123000	8.0081	-
0.2615	123100	8.0217	-
0.2617	123200	8.0228	-
0.2619	123300	8.0222	-
0.2621	123400	7.9962	-
0.2623	123500	8.0037	-
0.2626	123600	7.992	-
0.2628	123700	8.0457	-
0.2630	123800	7.9998	-
0.2632	123900	8.0203	-
0.2634	124000	7.9996	-
0.2636	124100	8.0243	-
0.2638	124200	8.0433	-
0.2640	124300	8.017	-
0.2643	124400	8.0099	-
0.2645	124500	7.9914	-
0.2647	124600	8.0118	-
0.2649	124700	8.0185	-
0.2651	124800	8.0084	-
0.2653	124900	8.0312	-
0.2655	125000	7.998	-
0.2657	125100	8.0175	-
0.2660	125200	8.0073	-
0.2662	125300	7.9855	-
0.2664	125400	8.0175	-
0.2666	125500	8.0182	-
0.2668	125600	8.0067	-
0.2670	125700	8.0027	-
0.2672	125800	8.0124	-
0.2674	125900	8.0015	-
0.2677	126000	7.9954	-
0.2679	126100	8.0157	-
0.2681	126200	8.0097	-
0.2683	126300	8.0168	-
0.2685	126400	8.0036	-
0.2687	126500	7.9842	-
0.2689	126600	8.0365	-
0.2691	126700	8.0191	-
0.2694	126800	8.0106	-
0.2696	126900	7.9886	-
0.2698	127000	8.0107	-
0.2700	127100	8.0029	-
0.2702	127200	8.0032	-
0.2704	127300	8.0283	-
0.2706	127400	7.9872	-
0.2708	127500	7.9899	-
0.2711	127600	8.0254	-
0.2713	127700	8.0247	-
0.2715	127800	8.0107	-
0.2717	127900	8.0184	-
0.2719	128000	8.027	-
0.2721	128100	8.0068	-
0.2723	128200	7.9908	-
0.2725	128300	7.9939	-
0.2728	128400	8.0	-
0.2730	128500	7.999	-
0.2732	128600	7.9907	-
0.2734	128700	8.0002	-
0.2736	128800	8.0368	-
0.2738	128900	7.9785	-
0.2740	129000	8.0084	-
0.2742	129100	7.9889	-
0.2745	129200	8.0125	-
0.2747	129300	7.9749	-
0.2749	129400	8.0079	-
0.2751	129500	8.0144	-
0.2753	129600	8.0295	-
0.2755	129700	8.0002	-
0.2757	129800	7.9959	-
0.2759	129900	8.0316	-
0.2762	130000	7.9936	-
0.2764	130100	8.0251	-
0.2766	130200	7.9996	-
0.2768	130300	7.9835	-
0.2770	130400	8.0065	-
0.2772	130500	7.9983	-
0.2774	130600	8.0093	-
0.2776	130700	7.9912	-
0.2779	130800	7.9925	-
0.2781	130900	8.0023	-
0.2783	131000	7.9906	-
0.2785	131100	7.9951	-
0.2787	131200	7.9933	-
0.2789	131300	7.9994	-
0.2791	131400	7.9939	-
0.2793	131500	7.9934	-
0.2796	131600	7.9932	-
0.2798	131700	8.0059	-
0.2800	131800	8.0037	-
0.2802	131900	7.9894	-
0.2804	132000	8.005	-
0.2806	132100	7.9928	-
0.2808	132200	7.9959	-
0.2810	132300	7.9991	-
0.2813	132400	8.0008	-
0.2815	132500	7.9957	-
0.2817	132600	8.0043	-
0.2819	132700	7.999	-
0.2821	132800	7.9972	-
0.2823	132900	8.007	-
0.2825	133000	7.9947	-
0.2827	133100	8.0156	-
0.2830	133200	7.9857	-
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0.3873	182300	7.9574	-
0.3875	182400	7.9431	-
0.3877	182500	7.9519	-
0.3879	182600	7.9678	-
0.3881	182700	7.9503	-
0.3883	182800	7.9312	-
0.3885	182900	7.9246	-
0.3887	183000	7.9703	-
0.3890	183100	7.9784	-
0.3892	183200	7.9561	-
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0.3896	183400	7.9868	-
0.3898	183500	7.9587	-
0.3900	183600	7.926	-
0.3902	183700	7.9632	-
0.3904	183800	7.9818	-
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0.3909	184000	7.9488	-
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0.3913	184200	7.9719	-
0.3915	184300	7.9403	-
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0.3919	184500	7.9642	-
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0.3930	185000	7.9358	-
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0.3979	187300	7.953	-
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0.3992	187900	7.948	-
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0.4000	188300	7.9779	-
0.4002	188400	7.9462	-
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0.4006	188600	7.9627	-
0.4009	188700	7.9497	-
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0.4015	189000	7.9437	-
0.4017	189100	7.953	-
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0.4021	189300	7.9583	-
0.4023	189400	7.9873	-
0.4025	189500	7.9367	-
0.4028	189600	7.9479	-
0.4030	189700	7.9567	-
0.4032	189800	7.9845	-
0.4034	189900	7.9324	-
0.4036	190000	7.9616	-
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0.4040	190200	7.9593	-
0.4042	190300	7.9615	-
0.4045	190400	7.9478	-
0.4047	190500	7.9466	-
0.4049	190600	7.9571	-
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0.4059	191100	7.9448	-
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0.4064	191300	7.9626	-
0.4066	191400	7.9379	-
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0.4072	191700	7.9554	-
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0.4079	192000	7.9528	-
0.4081	192100	7.9522	-
0.4083	192200	7.9271	-
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0.4087	192400	7.971	-
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0.4098	192900	7.9777	-
0.4100	193000	7.9739	-
0.4102	193100	7.9492	-
0.4104	193200	7.9595	-
0.4106	193300	7.9636	-
0.4108	193400	7.9599	-
0.4110	193500	7.9386	-
0.4113	193600	7.9356	-
0.4115	193700	7.9723	-
0.4117	193800	7.9469	-
0.4119	193900	7.9108	-
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0.4125	194200	7.9473	-
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0.4130	194400	7.9477	-
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0.4140	194900	7.9449	-
0.4142	195000	7.9545	-
0.4144	195100	7.9461	-
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0.4149	195300	7.9313	-
0.4151	195400	7.938	-
0.4153	195500	7.9678	-
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0.4157	195700	7.9538	-
0.4159	195800	7.9615	-
0.4161	195900	7.9461	-
0.4164	196000	7.9752	-
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0.4168	196200	7.9638	-
0.4170	196300	7.9502	-
0.4172	196400	7.9608	-
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0.4176	196600	7.9431	-
0.4178	196700	7.9336	-
0.4181	196800	7.9677	-
0.4183	196900	7.9501	-
0.4185	197000	7.9394	-
0.4187	197100	7.9628	-
0.4189	197200	7.9478	-
0.4191	197300	7.945	-
0.4193	197400	7.9348	-
0.4195	197500	7.953	-
0.4198	197600	7.9477	-
0.4200	197700	7.9408	-
0.4202	197800	7.9558	-
0.4204	197900	7.9391	-
0.4206	198000	7.9264	-
0.4208	198100	7.9388	-
0.4210	198200	7.938	-
0.4212	198300	7.9357	-
0.4215	198400	7.9411	-
0.4217	198500	7.9301	-
0.4219	198600	7.9439	-
0.4221	198700	7.9593	-
0.4223	198800	7.9282	-
0.4225	198900	7.9637	-
0.4227	199000	7.9566	-
0.4229	199100	7.9561	-
0.4232	199200	7.9651	-
0.4234	199300	7.9323	-
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0.4238	199500	7.9352	-
0.4240	199600	7.9354	-
0.4242	199700	7.9403	-
0.4244	199800	7.9302	-
0.4246	199900	7.951	-
0.4249	200000	7.9431	7.8827
0.4251	200100	7.9602	-
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0.4255	200300	7.9382	-
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0.4259	200500	7.9392	-
0.4261	200600	7.9342	-
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0.4266	200800	7.953	-
0.4268	200900	7.9352	-
0.4270	201000	7.9437	-
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0.4274	201200	7.9533	-
0.4276	201300	7.9282	-
0.4278	201400	7.9468	-
0.4280	201500	7.931	-
0.4283	201600	7.9366	-
0.4285	201700	7.9255	-
0.4287	201800	7.9377	-
0.4289	201900	7.9517	-
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0.4928	232000	7.9264	-
0.4930	232100	7.9031	-
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0.9149	430700	7.8788	-
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0.9153	430900	7.88	-
0.9156	431000	7.864	-
0.9158	431100	7.8678	-
0.9160	431200	7.8475	-
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0.9168	431600	7.8611	-
0.9170	431700	7.8312	-
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0.9175	431900	7.8845	-
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0.9219	434000	7.8688	-
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0.9224	434200	7.8772	-
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0.9230	434500	7.87	-
0.9232	434600	7.8509	-
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0.9241	435000	7.8688	-
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0.9253	435600	7.8733	-
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0.9260	435900	7.9109	-
0.9262	436000	7.8611	-
0.9264	436100	7.8517	-
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0.9300	437800	7.8558	-
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0.9311	438300	7.882	-
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0.9317	438600	7.8581	-
0.9319	438700	7.877	-
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0.9332	439300	7.8767	-
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0.9402	442600	7.8653	-
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0.9411	443000	7.8762	-
0.9413	443100	7.8707	-
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0.9419	443400	7.8853	-
0.9421	443500	7.8421	-
0.9423	443600	7.8788	-
0.9425	443700	7.8924	-
0.9428	443800	7.87	-
0.9430	443900	7.8709	-
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0.9434	444100	7.882	-
0.9436	444200	7.8586	-
0.9438	444300	7.8598	-
0.9440	444400	7.8784	-
0.9442	444500	7.9082	-
0.9445	444600	7.8327	-
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0.9455	445100	7.8634	-
0.9457	445200	7.8614	-
0.9459	445300	7.8605	-
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0.9464	445500	7.8911	-
0.9466	445600	7.8788	-
0.9468	445700	7.8674	-
0.9470	445800	7.9046	-
0.9472	445900	7.8509	-
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0.9476	446100	7.8469	-
0.9479	446200	7.8362	-
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0.9485	446500	7.8821	-
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0.9500	447200	7.867	-
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0.9510	447700	7.8606	-
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0.9517	448000	7.8433	-
0.9519	448100	7.8585	-
0.9521	448200	7.8671	-
0.9523	448300	7.8627	-
0.9525	448400	7.8891	-
0.9527	448500	7.9054	-
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0.9532	448700	7.849	-
0.9534	448800	7.8781	-
0.9536	448900	7.843	-
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0.9555	449800	7.8548	-
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0.9578	450900	7.8762	-
0.9580	451000	7.8706	-
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0.9587	451300	7.8433	-
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0.9597	451800	7.8595	-
0.9600	451900	7.8741	-
0.9602	452000	7.8657	-
0.9604	452100	7.8722	-
0.9606	452200	7.8868	-
0.9608	452300	7.8626	-
0.9610	452400	7.8831	-
0.9612	452500	7.8853	-
0.9614	452600	7.8554	-
0.9617	452700	7.8409	-
0.9619	452800	7.887	-
0.9621	452900	7.8467	-
0.9623	453000	7.8584	-
0.9625	453100	7.8583	-
0.9627	453200	7.8757	-
0.9629	453300	7.8224	-
0.9631	453400	7.8476	-
0.9634	453500	7.8825	-
0.9636	453600	7.8784	-
0.9638	453700	7.859	-
0.9640	453800	7.8387	-
0.9642	453900	7.8916	-
0.9644	454000	7.8649	-
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0.9655	454500	7.8733	-
0.9657	454600	7.8875	-
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0.9665	455000	7.8457	-
0.9668	455100	7.8892	-
0.9670	455200	7.8625	-
0.9672	455300	7.8664	-
0.9674	455400	7.8403	-
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0.9680	455700	7.8487	-
0.9682	455800	7.8914	-
0.9685	455900	7.8806	-
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0.9699	456600	7.8657	-
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0.9710	457100	7.8655	-
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0.9716	457400	7.8604	-
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0.9723	457700	7.8643	-
0.9725	457800	7.8627	-
0.9727	457900	7.8455	-
0.9729	458000	7.8342	-
0.9731	458100	7.8508	-
0.9733	458200	7.8591	-
0.9736	458300	7.8457	-
0.9738	458400	7.8671	-
0.9740	458500	7.8826	-
0.9742	458600	7.8352	-
0.9744	458700	7.8472	-
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0.9748	458900	7.8843	-
0.9750	459000	7.8612	-
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0.9755	459200	7.8912	-
0.9757	459300	7.8682	-
0.9759	459400	7.8502	-
0.9761	459500	7.8533	-
0.9763	459600	7.8634	-
0.9765	459700	7.8753	-
0.9767	459800	7.8617	-
0.9770	459900	7.8343	-
0.9772	460000	7.8518	-
0.9774	460100	7.854	-
0.9776	460200	7.8804	-
0.9778	460300	7.8743	-
0.9780	460400	7.8704	-
0.9782	460500	7.8878	-
0.9784	460600	7.8755	-
0.9787	460700	7.8566	-
0.9789	460800	7.8476	-
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0.9793	461000	7.8702	-
0.9795	461100	7.8596	-
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0.9799	461300	7.8818	-
0.9801	461400	7.8685	-
0.9804	461500	7.8633	-
0.9806	461600	7.8536	-
0.9808	461700	7.8778	-
0.9810	461800	7.8442	-
0.9812	461900	7.86	-
0.9814	462000	7.8714	-
0.9816	462100	7.8469	-
0.9818	462200	7.867	-
0.9821	462300	7.8431	-
0.9823	462400	7.8457	-
0.9825	462500	7.8356	-
0.9827	462600	7.8465	-
0.9829	462700	7.8809	-
0.9831	462800	7.8601	-
0.9833	462900	7.8574	-
0.9835	463000	7.8598	-
0.9838	463100	7.8612	-
0.9840	463200	7.8577	-
0.9842	463300	7.8365	-
0.9844	463400	7.8576	-
0.9846	463500	7.8674	-
0.9848	463600	7.8567	-
0.9850	463700	7.8617	-
0.9852	463800	7.8766	-
0.9855	463900	7.8979	-
0.9857	464000	7.8738	-
0.9859	464100	7.8436	-
0.9861	464200	7.889	-
0.9863	464300	7.9005	-
0.9865	464400	7.8654	-
0.9867	464500	7.8706	-
0.9869	464600	7.8586	-
0.9872	464700	7.8323	-
0.9874	464800	7.8947	-
0.9876	464900	7.8685	-
0.9878	465000	7.8408	-
0.9880	465100	7.8657	-
0.9882	465200	7.8812	-
0.9884	465300	7.8689	-
0.9886	465400	7.8612	-
0.9888	465500	7.8471	-
0.9891	465600	7.8422	-
0.9893	465700	7.8894	-
0.9895	465800	7.8432	-
0.9897	465900	7.8792	-
0.9899	466000	7.858	-
0.9901	466100	7.8662	-
0.9903	466200	7.8633	-
0.9905	466300	7.8504	-
0.9908	466400	7.85	-
0.9910	466500	7.847	-
0.9912	466600	7.871	-
0.9914	466700	7.8849	-
0.9916	466800	7.8983	-
0.9918	466900	7.9018	-
0.9920	467000	7.8652	-
0.9922	467100	7.8598	-
0.9925	467200	7.8579	-
0.9927	467300	7.8475	-
0.9929	467400	7.8612	-
0.9931	467500	7.8665	-
0.9933	467600	7.8738	-
0.9935	467700	7.8971	-
0.9937	467800	7.8882	-
0.9939	467900	7.896	-
0.9942	468000	7.8478	-
0.9944	468100	7.8519	-
0.9946	468200	7.8439	-
0.9948	468300	7.8482	-
0.9950	468400	7.9124	-
0.9952	468500	7.8635	-
0.9954	468600	7.8667	-
0.9956	468700	7.8637	-
0.9959	468800	7.877	-
0.9961	468900	7.8695	-
0.9963	469000	7.8793	-
0.9965	469100	7.8841	-
0.9967	469200	7.8318	-
0.9969	469300	7.8682	-
0.9971	469400	7.8571	-
0.9973	469500	7.8689	-
0.9976	469600	7.8569	-
0.9978	469700	7.8549	-
0.9980	469800	7.8295	-
0.9982	469900	7.885	-
0.9984	470000	7.868	-
0.9986	470100	7.8637	-
0.9988	470200	7.8605	-
0.9990	470300	7.8355	-
0.9993	470400	7.8604	-
0.9995	470500	7.842	-
0.9997	470600	7.8765	-
0.9999	470700	7.855	-

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},
}