This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the pairs_three_scores_v8 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_three_scores_v8
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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'pasabah',
    'thermos mug',
    'sports tracksuit',
]
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, 0.7949, 0.5631],
#         [0.7949, 1.0000, 0.5810],
#         [0.5631, 0.5810, 1.0000]])

Training Details

Training Dataset

pairs_three_scores_v8

Dataset: pairs_three_scores_v8 at 7e8a1e6
Size: 9,164,180 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: 5.6 tokens
max: 20 tokens
min: 3 tokens
mean: 5.79 tokens
max: 24 tokens
min: 0.15
mean: 0.43
max: 1.0
Samples:
sentence1 sentence2 score
booster face cleanser pizza cutter 0.24
line sinker accessories 1.0
tricovel cot bed mattress protector 0.28
Loss: CoSENTLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

Evaluation Dataset

pairs_three_scores_v8

Dataset: pairs_three_scores_v8 at 7e8a1e6
Size: 46,052 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: 5.66 tokens
max: 21 tokens
min: 3 tokens
mean: 5.6 tokens
max: 41 tokens
min: 0.15
mean: 0.42
max: 1.0
Samples:
sentence1 sentence2 score
printed set crushed outfit 1.0
value eva cosmetics serum 0.23
zino shakes candy 0.27
Loss: CoSENTLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
learning_rate: 2e-05
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: 3
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
0.0014	100	12.1005	-
0.0028	200	11.7845	-
0.0042	300	11.9308	-
0.0056	400	11.5266	-
0.0070	500	11.2831	-
0.0084	600	11.1567	-
0.0098	700	11.1136	-
0.0112	800	10.8796	-
0.0126	900	10.7857	-
0.0140	1000	10.3013	-
0.0154	1100	10.219	-
0.0168	1200	9.9544	-
0.0182	1300	9.8849	-
0.0196	1400	9.6718	-
0.0210	1500	9.5719	-
0.0223	1600	9.4827	-
0.0237	1700	9.2958	-
0.0251	1800	9.1635	-
0.0265	1900	9.0311	-
0.0279	2000	8.9214	-
0.0293	2100	8.8294	-
0.0307	2200	8.7182	-
0.0321	2300	8.6947	-
0.0335	2400	8.6355	-
0.0349	2500	8.6105	-
0.0363	2600	8.5982	-
0.0377	2700	8.5766	-
0.0391	2800	8.5562	-
0.0405	2900	8.5614	-
0.0419	3000	8.5343	-
0.0433	3100	8.5315	-
0.0447	3200	8.5113	-
0.0461	3300	8.508	-
0.0475	3400	8.5111	-
0.0489	3500	8.5023	-
0.0503	3600	8.4961	-
0.0517	3700	8.4804	-
0.0531	3800	8.4744	-
0.0545	3900	8.4872	-
0.0559	4000	8.4743	-
0.0573	4100	8.4601	-
0.0587	4200	8.4476	-
0.0601	4300	8.4472	-
0.0615	4400	8.4452	-
0.0629	4500	8.434	-
0.0642	4600	8.4155	-
0.0656	4700	8.4443	-
0.0670	4800	8.4148	-
0.0684	4900	8.4154	-
0.0698	5000	8.4	-
0.0712	5100	8.3959	-
0.0726	5200	8.3963	-
0.0740	5300	8.3941	-
0.0754	5400	8.3874	-
0.0768	5500	8.3843	-
0.0782	5600	8.3778	-
0.0796	5700	8.4002	-
0.0810	5800	8.3779	-
0.0824	5900	8.3706	-
0.0838	6000	8.3633	-
0.0852	6100	8.3647	-
0.0866	6200	8.3573	-
0.0880	6300	8.3421	-
0.0894	6400	8.345	-
0.0908	6500	8.3541	-
0.0922	6600	8.3331	-
0.0936	6700	8.3296	-
0.0950	6800	8.3363	-
0.0964	6900	8.325	-
0.0978	7000	8.3184	-
0.0992	7100	8.3146	-
0.1006	7200	8.3078	-
0.1020	7300	8.3148	-
0.1034	7400	8.3018	-
0.1048	7500	8.2879	-
0.1062	7600	8.2917	-
0.1075	7700	8.2965	-
0.1089	7800	8.2832	-
0.1103	7900	8.2937	-
0.1117	8000	8.2808	-
0.1131	8100	8.2863	-
0.1145	8200	8.2815	-
0.1159	8300	8.2586	-
0.1173	8400	8.2683	-
0.1187	8500	8.282	-
0.1201	8600	8.2636	-
0.1215	8700	8.2504	-
0.1229	8800	8.2545	-
0.1243	8900	8.2618	-
0.1257	9000	8.2389	-
0.1271	9100	8.2281	-
0.1285	9200	8.2406	-
0.1299	9300	8.236	-
0.1313	9400	8.2101	-
0.1327	9500	8.2379	-
0.1341	9600	8.2486	-
0.1355	9700	8.213	-
0.1369	9800	8.2188	-
0.1383	9900	8.2148	-
0.1397	10000	8.217	-
0.1411	10100	8.2269	-
0.1425	10200	8.2071	-
0.1439	10300	8.2027	-
0.1453	10400	8.2123	-
0.1467	10500	8.1902	-
0.1481	10600	8.1857	-
0.1494	10700	8.2031	-
0.1508	10800	8.1905	-
0.1522	10900	8.1674	-
0.1536	11000	8.1822	-
0.1550	11100	8.1951	-
0.1564	11200	8.1812	-
0.1578	11300	8.1801	-
0.1592	11400	8.1739	-
0.1606	11500	8.1485	-
0.1620	11600	8.1783	-
0.1634	11700	8.1684	-
0.1648	11800	8.1841	-
0.1662	11900	8.1724	-
0.1676	12000	8.1539	-
0.1690	12100	8.1396	-
0.1704	12200	8.1431	-
0.1718	12300	8.1335	-
0.1732	12400	8.1508	-
0.1746	12500	8.157	-
0.1760	12600	8.131	-
0.1774	12700	8.1369	-
0.1788	12800	8.1295	-
0.1802	12900	8.1334	-
0.1816	13000	8.1227	-
0.1830	13100	8.1202	-
0.1844	13200	8.1328	-
0.1858	13300	8.1169	-
0.1872	13400	8.1193	-
0.1886	13500	8.1148	-
0.1900	13600	8.125	-
0.1914	13700	8.1154	-
0.1927	13800	8.1024	-
0.1941	13900	8.0967	-
0.1955	14000	8.0922	-
0.1969	14100	8.1017	-
0.1983	14200	8.1092	-
0.1997	14300	8.0821	-
0.2011	14400	8.106	-
0.2025	14500	8.0978	-
0.2039	14600	8.1022	-
0.2053	14700	8.1144	-
0.2067	14800	8.0766	-
0.2081	14900	8.105	-
0.2095	15000	8.083	-
0.2109	15100	8.114	-
0.2123	15200	8.0915	-
0.2137	15300	8.0945	-
0.2151	15400	8.0739	-
0.2165	15500	8.0576	-
0.2179	15600	8.0696	-
0.2193	15700	8.0516	-
0.2207	15800	8.0228	-
0.2221	15900	8.0818	-
0.2235	16000	8.0627	-
0.2249	16100	8.0521	-
0.2263	16200	8.0386	-
0.2277	16300	8.0256	-
0.2291	16400	8.0604	-
0.2305	16500	8.0458	-
0.2319	16600	8.0255	-
0.2333	16700	8.0688	-
0.2346	16800	8.0209	-
0.2360	16900	8.0165	-
0.2374	17000	8.0447	-
0.2388	17100	8.0458	-
0.2402	17200	8.0647	-
0.2416	17300	8.0337	-
0.2430	17400	8.0219	-
0.2444	17500	8.0141	-
0.2458	17600	8.0198	-
0.2472	17700	8.0181	-
0.2486	17800	8.0218	-
0.2500	17900	8.0031	-
0.2514	18000	8.0241	-
0.2528	18100	7.987	-
0.2542	18200	7.9941	-
0.2556	18300	7.9944	-
0.2570	18400	7.9978	-
0.2584	18500	7.9897	-
0.2598	18600	7.9967	-
0.2612	18700	7.9798	-
0.2626	18800	7.9816	-
0.2640	18900	7.9996	-
0.2654	19000	7.9991	-
0.2668	19100	7.9844	-
0.2682	19200	7.9576	-
0.2696	19300	7.9908	-
0.2710	19400	7.9852	-
0.2724	19500	7.9729	-
0.2738	19600	7.9691	-
0.2752	19700	7.976	-
0.2766	19800	7.968	-
0.2779	19900	7.9699	-
0.2793	20000	7.9607	-
0.2807	20100	7.9648	-
0.2821	20200	7.9689	-
0.2835	20300	7.982	-
0.2849	20400	7.9628	-
0.2863	20500	7.9514	-
0.2877	20600	7.9704	-
0.2891	20700	7.952	-
0.2905	20800	7.9726	-
0.2919	20900	7.9617	-
0.2933	21000	7.97	-
0.2947	21100	7.9527	-
0.2961	21200	7.9456	-
0.2975	21300	7.9628	-
0.2989	21400	7.9728	-
0.3003	21500	7.9592	-
0.3017	21600	7.9396	-
0.3031	21700	7.9401	-
0.3045	21800	7.9267	-
0.3059	21900	7.9322	-
0.3073	22000	7.9331	-
0.3087	22100	7.9249	-
0.3101	22200	7.9457	-
0.3115	22300	7.9214	-
0.3129	22400	7.9436	-
0.3143	22500	7.9321	-
0.3157	22600	7.9164	-
0.3171	22700	7.9046	-
0.3185	22800	7.9368	-
0.3199	22900	7.9467	-
0.3212	23000	7.9085	-
0.3226	23100	7.9117	-
0.3240	23200	7.9237	-
0.3254	23300	7.9059	-
0.3268	23400	7.9019	-
0.3282	23500	7.8986	-
0.3296	23600	7.9094	-
0.3310	23700	7.8899	-
0.3324	23800	7.9106	-
0.3338	23900	7.902	-
0.3352	24000	7.8866	-
0.3366	24100	7.8683	-
0.3380	24200	7.8898	-
0.3394	24300	7.8918	-
0.3408	24400	7.9175	-
0.3422	24500	7.9016	-
0.3436	24600	7.8805	-
0.3450	24700	7.8899	-
0.3464	24800	7.9016	-
0.3478	24900	7.8815	-
0.3492	25000	7.883	-
0.3506	25100	7.8714	-
0.3520	25200	7.8906	-
0.3534	25300	7.8446	-
0.3548	25400	7.852	-
0.3562	25500	7.8995	-
0.3576	25600	7.8283	-
0.3590	25700	7.8775	-
0.3604	25800	7.8504	-
0.3618	25900	7.8788	-
0.3631	26000	7.8664	-
0.3645	26100	7.8577	-
0.3659	26200	7.8549	-
0.3673	26300	7.861	-
0.3687	26400	7.8721	-
0.3701	26500	7.8619	-
0.3715	26600	7.8699	-
0.3729	26700	7.8148	-
0.3743	26800	7.8516	-
0.3757	26900	7.8059	-
0.3771	27000	7.8509	-
0.3785	27100	7.8588	-
0.3799	27200	7.8868	-
0.3813	27300	7.8543	-
0.3827	27400	7.8208	-
0.3841	27500	7.8413	-
0.3855	27600	7.8159	-
0.3869	27700	7.8739	-
0.3883	27800	7.8363	-
0.3897	27900	7.8484	-
0.3911	28000	7.8265	-
0.3925	28100	7.8583	-
0.3939	28200	7.8026	-
0.3953	28300	7.8474	-
0.3967	28400	7.8695	-
0.3981	28500	7.8252	-
0.3995	28600	7.8159	-
0.4009	28700	7.8278	-
0.4023	28800	7.8274	-
0.4037	28900	7.8252	-
0.4051	29000	7.8302	-
0.4064	29100	7.8157	-
0.4078	29200	7.8494	-
0.4092	29300	7.7891	-
0.4106	29400	7.8137	-
0.4120	29500	7.8091	-
0.4134	29600	7.7837	-
0.4148	29700	7.8028	-
0.4162	29800	7.7875	-
0.4176	29900	7.7941	-
0.4190	30000	7.8333	-
0.4204	30100	7.8283	-
0.4218	30200	7.8034	-
0.4232	30300	7.8076	-
0.4246	30400	7.8209	-
0.4260	30500	7.8049	-
0.4274	30600	7.787	-
0.4288	30700	7.7951	-
0.4302	30800	7.8115	-
0.4316	30900	7.8192	-
0.4330	31000	7.782	-
0.4344	31100	7.8092	-
0.4358	31200	7.7724	-
0.4372	31300	7.7957	-
0.4386	31400	7.7928	-
0.4400	31500	7.8104	-
0.4414	31600	7.7928	-
0.4428	31700	7.816	-
0.4442	31800	7.7623	-
0.4456	31900	7.8111	-
0.4470	32000	7.7921	-
0.4483	32100	7.7926	-
0.4497	32200	7.7777	-
0.4511	32300	7.7754	-
0.4525	32400	7.7869	-
0.4539	32500	7.7918	-
0.4553	32600	7.7607	-
0.4567	32700	7.794	-
0.4581	32800	7.776	-
0.4595	32900	7.7939	-
0.4609	33000	7.7565	-
0.4623	33100	7.8086	-
0.4637	33200	7.7745	-
0.4651	33300	7.7939	-
0.4665	33400	7.7478	-
0.4679	33500	7.8111	-
0.4693	33600	7.7373	-
0.4707	33700	7.7798	-
0.4721	33800	7.7513	-
0.4735	33900	7.7548	-
0.4749	34000	7.7722	-
0.4763	34100	7.7614	-
0.4777	34200	7.7517	-
0.4791	34300	7.7573	-
0.4805	34400	7.7792	-
0.4819	34500	7.7609	-
0.4833	34600	7.7377	-
0.4847	34700	7.7378	-
0.4861	34800	7.7328	-
0.4875	34900	7.7746	-
0.4889	35000	7.7583	-
0.4903	35100	7.7708	-
0.4916	35200	7.724	-
0.4930	35300	7.7482	-
0.4944	35400	7.7589	-
0.4958	35500	7.7265	-
0.4972	35600	7.7088	-
0.4986	35700	7.742	-
0.5000	35800	7.7555	-
0.5014	35900	7.7247	-
0.5028	36000	7.7487	-
0.5042	36100	7.7444	-
0.5056	36200	7.735	-
0.5070	36300	7.7383	-
0.5084	36400	7.7062	-
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1.2179	87200	7.4552	-
1.2193	87300	7.4575	-
1.2207	87400	7.4479	-
1.2221	87500	7.4836	-
1.2235	87600	7.4436	-
1.2249	87700	7.4922	-
1.2263	87800	7.49	-
1.2277	87900	7.4424	-
1.2291	88000	7.4672	-
1.2305	88100	7.4688	-
1.2319	88200	7.5013	-
1.2333	88300	7.4506	-
1.2347	88400	7.4515	-
1.2361	88500	7.4107	-
1.2375	88600	7.4517	-
1.2389	88700	7.5316	-
1.2403	88800	7.4636	-
1.2417	88900	7.4485	-
1.2431	89000	7.4474	-
1.2445	89100	7.4225	-
1.2459	89200	7.427	-
1.2473	89300	7.4565	-
1.2487	89400	7.4284	-
1.2501	89500	7.4866	-
1.2515	89600	7.497	-
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1.2543	89800	7.4419	-
1.2557	89900	7.4242	-
1.2571	90000	7.4507	-
1.2585	90100	7.4429	-
1.2598	90200	7.4389	-
1.2612	90300	7.4701	-
1.2626	90400	7.4235	-
1.2640	90500	7.4712	-
1.2654	90600	7.4864	-
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1.2682	90800	7.4671	-
1.2696	90900	7.4744	-
1.2710	91000	7.4341	-
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1.2752	91300	7.4595	-
1.2766	91400	7.4669	-
1.2780	91500	7.437	-
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1.2808	91700	7.4253	-
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1.2850	92000	7.4532	-
1.2864	92100	7.4957	-
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1.2906	92400	7.4204	-
1.2920	92500	7.405	-
1.2934	92600	7.4335	-
1.2948	92700	7.4262	-
1.2962	92800	7.4514	-
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1.2990	93000	7.4639	-
1.3004	93100	7.4389	-
1.3017	93200	7.4743	-
1.3031	93300	7.4258	-
1.3045	93400	7.5066	-
1.3059	93500	7.4396	-
1.3073	93600	7.4719	-
1.3087	93700	7.4434	-
1.3101	93800	7.4795	-
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1.3129	94000	7.4284	-
1.3143	94100	7.4735	-
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1.3185	94400	7.4142	-
1.3199	94500	7.4721	-
1.3213	94600	7.3615	-
1.3227	94700	7.5029	-
1.3241	94800	7.412	-
1.3255	94900	7.4186	-
1.3269	95000	7.4427	-
1.3283	95100	7.4455	-
1.3297	95200	7.4233	-
1.3311	95300	7.4392	-
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1.3339	95500	7.4351	-
1.3353	95600	7.4401	-
1.3367	95700	7.4206	-
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1.3395	95900	7.4423	-
1.3409	96000	7.4553	-
1.3423	96100	7.4528	-
1.3437	96200	7.3688	-
1.3450	96300	7.4475	-
1.3464	96400	7.4574	-
1.3478	96500	7.4671	-
1.3492	96600	7.4242	-
1.3506	96700	7.4453	-
1.3520	96800	7.4353	-
1.3534	96900	7.4617	-
1.3548	97000	7.4512	-
1.3562	97100	7.4276	-
1.3576	97200	7.4566	-
1.3590	97300	7.4069	-
1.3604	97400	7.4541	-
1.3618	97500	7.4262	-
1.3632	97600	7.4198	-
1.3646	97700	7.4582	-
1.3660	97800	7.4415	-
1.3674	97900	7.4277	-
1.3688	98000	7.4315	-
1.3702	98100	7.4644	-
1.3716	98200	7.4358	-
1.3730	98300	7.4393	-
1.3744	98400	7.4206	-
1.3758	98500	7.4476	-
1.3772	98600	7.4759	-
1.3786	98700	7.4512	-
1.3800	98800	7.4609	-
1.3814	98900	7.4133	-
1.3828	99000	7.4145	-
1.3842	99100	7.4171	-
1.3856	99200	7.4066	-
1.3869	99300	7.4319	-
1.3883	99400	7.4185	-
1.3897	99500	7.4609	-
1.3911	99600	7.4384	-
1.3925	99700	7.4265	-
1.3939	99800	7.4399	-
1.3953	99900	7.3966	-
1.3967	100000	7.445	-
1.3981	100100	7.4411	-
1.3995	100200	7.4247	-
1.4009	100300	7.4383	-
1.4023	100400	7.4688	-
1.4037	100500	7.4147	-
1.4051	100600	7.4168	-
1.4065	100700	7.4317	-
1.4079	100800	7.4577	-
1.4093	100900	7.398	-
1.4107	101000	7.4533	-
1.4121	101100	7.4591	-
1.4135	101200	7.4054	-
1.4149	101300	7.403	-
1.4163	101400	7.444	-
1.4177	101500	7.4272	-
1.4191	101600	7.4367	-
1.4205	101700	7.4425	-
1.4219	101800	7.42	-
1.4233	101900	7.3825	-
1.4247	102000	7.4212	-
1.4261	102100	7.487	-
1.4275	102200	7.4161	-
1.4289	102300	7.4106	-
1.4302	102400	7.4135	-
1.4316	102500	7.4595	-
1.4330	102600	7.4381	-
1.4344	102700	7.4174	-
1.4358	102800	7.3998	-
1.4372	102900	7.4119	-
1.4386	103000	7.4288	-
1.4400	103100	7.3832	-
1.4414	103200	7.4123	-
1.4428	103300	7.4162	-
1.4442	103400	7.4238	-
1.4456	103500	7.3999	-
1.4470	103600	7.4231	-
1.4484	103700	7.43	-
1.4498	103800	7.4279	-
1.4512	103900	7.4193	-
1.4526	104000	7.4176	-
1.4540	104100	7.4243	-
1.4554	104200	7.3879	-
1.4568	104300	7.4499	-
1.4582	104400	7.4222	-
1.4596	104500	7.4177	-
1.4610	104600	7.4432	-
1.4624	104700	7.4129	-
1.4638	104800	7.4177	-
1.4652	104900	7.4806	-
1.4666	105000	7.448	-
1.4680	105100	7.4534	-
1.4694	105200	7.4113	-
1.4708	105300	7.4865	-
1.4721	105400	7.4336	-
1.4735	105500	7.424	-
1.4749	105600	7.418	-
1.4763	105700	7.3815	-
1.4777	105800	7.3964	-
1.4791	105900	7.3963	-
1.4805	106000	7.3646	-
1.4819	106100	7.4319	-
1.4833	106200	7.4728	-
1.4847	106300	7.3873	-
1.4861	106400	7.425	-
1.4875	106500	7.374	-
1.4889	106600	7.4639	-
1.4903	106700	7.3714	-
1.4917	106800	7.4241	-
1.4931	106900	7.4198	-
1.4945	107000	7.4438	-
1.4959	107100	7.3889	-
1.4973	107200	7.3949	-
1.4987	107300	7.4022	-
1.5001	107400	7.4416	-
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1.5029	107600	7.3999	-
1.5043	107700	7.4162	-
1.5057	107800	7.4322	-
1.5071	107900	7.4508	-
1.5085	108000	7.4285	-
1.5099	108100	7.4393	-
1.5113	108200	7.3841	-
1.5127	108300	7.4485	-
1.5141	108400	7.3934	-
1.5154	108500	7.3981	-
1.5168	108600	7.39	-
1.5182	108700	7.4459	-
1.5196	108800	7.4584	-
1.5210	108900	7.4129	-
1.5224	109000	7.461	-
1.5238	109100	7.4603	-
1.5252	109200	7.4139	-
1.5266	109300	7.3722	-
1.5280	109400	7.419	-
1.5294	109500	7.3854	-
1.5308	109600	7.3911	-
1.5322	109700	7.4399	-
1.5336	109800	7.4364	-
1.5350	109900	7.4202	-
1.5364	110000	7.4052	-
1.5378	110100	7.4202	-
1.5392	110200	7.4059	-
1.5406	110300	7.3833	-
1.5420	110400	7.3971	-
1.5434	110500	7.373	-
1.5448	110600	7.4018	-
1.5462	110700	7.3865	-
1.5476	110800	7.3912	-
1.5490	110900	7.4175	-
1.5504	111000	7.4329	-
1.5518	111100	7.4096	-
1.5532	111200	7.4458	-
1.5546	111300	7.4334	-
1.5560	111400	7.4022	-
1.5573	111500	7.4181	-
1.5587	111600	7.3979	-
1.5601	111700	7.4064	-
1.5615	111800	7.3573	-
1.5629	111900	7.4133	-
1.5643	112000	7.3757	-
1.5657	112100	7.4534	-
1.5671	112200	7.4495	-
1.5685	112300	7.4078	-
1.5699	112400	7.3971	-
1.5713	112500	7.4276	-
1.5727	112600	7.4297	-
1.5741	112700	7.3766	-
1.5755	112800	7.3565	-
1.5769	112900	7.3467	-
1.5783	113000	7.4168	-
1.5797	113100	7.4206	-
1.5811	113200	7.4398	-
1.5825	113300	7.3519	-
1.5839	113400	7.4415	-
1.5853	113500	7.3865	-
1.5867	113600	7.3828	-
1.5881	113700	7.3751	-
1.5895	113800	7.4415	-
1.5909	113900	7.38	-
1.5923	114000	7.4003	-
1.5937	114100	7.3927	-
1.5951	114200	7.384	-
1.5965	114300	7.39	-
1.5979	114400	7.3415	-
1.5993	114500	7.4288	-
1.6006	114600	7.4034	-
1.6020	114700	7.3948	-
1.6034	114800	7.419	-
1.6048	114900	7.3942	-
1.6062	115000	7.4085	-
1.6076	115100	7.4153	-
1.6090	115200	7.3991	-
1.6104	115300	7.4066	-
1.6118	115400	7.3492	-
1.6132	115500	7.3952	-
1.6146	115600	7.4414	-
1.6160	115700	7.4042	-
1.6174	115800	7.3535	-
1.6188	115900	7.4301	-
1.6202	116000	7.4144	-
1.6216	116100	7.3757	-
1.6230	116200	7.417	-
1.6244	116300	7.3725	-
1.6258	116400	7.4406	-
1.6272	116500	7.3667	-
1.6286	116600	7.3532	-
1.6300	116700	7.4438	-
1.6314	116800	7.3993	-
1.6328	116900	7.438	-
1.6342	117000	7.4068	-
1.6356	117100	7.3955	-
1.6370	117200	7.4081	-
1.6384	117300	7.3654	-
1.6398	117400	7.3697	-
1.6412	117500	7.3898	-
1.6425	117600	7.4426	-
1.6439	117700	7.3885	-
1.6453	117800	7.3767	-
1.6467	117900	7.4112	-
1.6481	118000	7.3831	-
1.6495	118100	7.3565	-
1.6509	118200	7.3847	-
1.6523	118300	7.3874	-
1.6537	118400	7.3943	-
1.6551	118500	7.414	-
1.6565	118600	7.3707	-
1.6579	118700	7.4009	-
1.6593	118800	7.3709	-
1.6607	118900	7.4356	-
1.6621	119000	7.3932	-
1.6635	119100	7.3695	-
1.6649	119200	7.3969	-
1.6663	119300	7.3459	-
1.6677	119400	7.3879	-
1.6691	119500	7.3927	-
1.6705	119600	7.3585	-
1.6719	119700	7.3662	-
1.6733	119800	7.4078	-
1.6747	119900	7.4647	-
1.6761	120000	7.3923	-
1.6775	120100	7.3623	-
1.6789	120200	7.3941	-
1.6803	120300	7.3547	-
1.6817	120400	7.3531	-
1.6831	120500	7.3496	-
1.6845	120600	7.3569	-
1.6858	120700	7.3876	-
1.6872	120800	7.4051	-
1.6886	120900	7.3877	-
1.6900	121000	7.3928	-
1.6914	121100	7.389	-
1.6928	121200	7.3686	-
1.6942	121300	7.3667	-
1.6956	121400	7.3658	-
1.6970	121500	7.4087	-
1.6984	121600	7.3722	-
1.6998	121700	7.3575	-
1.7012	121800	7.3967	-
1.7026	121900	7.3178	-
1.7040	122000	7.4263	-
1.7054	122100	7.3673	-
1.7068	122200	7.3616	-
1.7082	122300	7.4166	-
1.7096	122400	7.3454	-
1.7110	122500	7.3895	-
1.7124	122600	7.3943	-
1.7138	122700	7.3966	-
1.7152	122800	7.3723	-
1.7166	122900	7.4161	-
1.7180	123000	7.3465	-
1.7194	123100	7.3948	-
1.7208	123200	7.3825	-
1.7222	123300	7.3742	-
1.7236	123400	7.3602	-
1.7250	123500	7.3564	-
1.7264	123600	7.3498	-
1.7278	123700	7.3562	-
1.7291	123800	7.4639	-
1.7305	123900	7.377	-
1.7319	124000	7.3774	-
1.7333	124100	7.3815	-
1.7347	124200	7.4188	-
1.7361	124300	7.3776	-
1.7375	124400	7.3955	-
1.7389	124500	7.3762	-
1.7403	124600	7.3787	-
1.7417	124700	7.3714	-
1.7431	124800	7.4175	-
1.7445	124900	7.3283	-
1.7459	125000	7.3488	-
1.7473	125100	7.3798	-
1.7487	125200	7.4141	-
1.7501	125300	7.3505	-
1.7515	125400	7.4051	-
1.7529	125500	7.3426	-
1.7543	125600	7.3838	-
1.7557	125700	7.382	-
1.7571	125800	7.3617	-
1.7585	125900	7.3345	-
1.7599	126000	7.3666	-
1.7613	126100	7.4314	-
1.7627	126200	7.415	-
1.7641	126300	7.4029	-
1.7655	126400	7.406	-
1.7669	126500	7.3539	-
1.7683	126600	7.3702	-
1.7697	126700	7.3517	-
1.7710	126800	7.3681	-
1.7724	126900	7.3852	-
1.7738	127000	7.3492	-
1.7752	127100	7.3482	-
1.7766	127200	7.3894	-
1.7780	127300	7.3819	-
1.7794	127400	7.3703	-
1.7808	127500	7.3784	-
1.7822	127600	7.3369	-
1.7836	127700	7.3665	-
1.7850	127800	7.4265	-
1.7864	127900	7.3536	-
1.7878	128000	7.3589	-
1.7892	128100	7.4297	-
1.7906	128200	7.3736	-
1.7920	128300	7.3788	-
1.7934	128400	7.398	-
1.7948	128500	7.3437	-
1.7962	128600	7.384	-
1.7976	128700	7.3957	-
1.7990	128800	7.404	-
1.8004	128900	7.3623	-
1.8018	129000	7.345	-
1.8032	129100	7.3505	-
1.8046	129200	7.3549	-
1.8060	129300	7.4113	-
1.8074	129400	7.3441	-
1.8088	129500	7.4164	-
1.8102	129600	7.367	-
1.8116	129700	7.3781	-
1.8130	129800	7.3133	-
1.8143	129900	7.3499	-
1.8157	130000	7.3501	-
1.8171	130100	7.3525	-
1.8185	130200	7.3673	-
1.8199	130300	7.3219	-
1.8213	130400	7.3453	-
1.8227	130500	7.3254	-
1.8241	130600	7.319	-
1.8255	130700	7.4377	-
1.8269	130800	7.3988	-
1.8283	130900	7.3669	-
1.8297	131000	7.3196	-
1.8311	131100	7.3544	-
1.8325	131200	7.4226	-
1.8339	131300	7.3527	-
1.8353	131400	7.3607	-
1.8367	131500	7.3535	-
1.8381	131600	7.4416	-
1.8395	131700	7.3974	-
1.8409	131800	7.3579	-
1.8423	131900	7.3543	-
1.8437	132000	7.4034	-
1.8451	132100	7.3807	-
1.8465	132200	7.3491	-
1.8479	132300	7.3643	-
1.8493	132400	7.3705	-
1.8507	132500	7.3424	-
1.8521	132600	7.4091	-
1.8535	132700	7.3487	-
1.8549	132800	7.3754	-
1.8562	132900	7.3614	-
1.8576	133000	7.3706	-
1.8590	133100	7.3241	-
1.8604	133200	7.3563	-
1.8618	133300	7.375	-
1.8632	133400	7.39	-
1.8646	133500	7.339	-
1.8660	133600	7.3124	-
1.8674	133700	7.3451	-
1.8688	133800	7.4036	-
1.8702	133900	7.4332	-
1.8716	134000	7.3595	-
1.8730	134100	7.3304	-
1.8744	134200	7.3517	-
1.8758	134300	7.3464	-
1.8772	134400	7.3736	-
1.8786	134500	7.3655	-
1.8800	134600	7.3531	-
1.8814	134700	7.3719	-
1.8828	134800	7.358	-
1.8842	134900	7.3771	-
1.8856	135000	7.3676	-
1.8870	135100	7.4358	-
1.8884	135200	7.3816	-
1.8898	135300	7.3698	-
1.8912	135400	7.4109	-
1.8926	135500	7.3437	-
1.8940	135600	7.3626	-
1.8954	135700	7.3019	-
1.8968	135800	7.3109	-
1.8982	135900	7.4189	-
1.8995	136000	7.379	-
1.9009	136100	7.2834	-
1.9023	136200	7.3659	-
1.9037	136300	7.301	-
1.9051	136400	7.3694	-
1.9065	136500	7.4134	-
1.9079	136600	7.3613	-
1.9093	136700	7.3723	-
1.9107	136800	7.359	-
1.9121	136900	7.3488	-
1.9135	137000	7.3703	-
1.9149	137100	7.373	-
1.9163	137200	7.3536	-
1.9177	137300	7.3622	-
1.9191	137400	7.3475	-
1.9205	137500	7.3497	-
1.9219	137600	7.3156	-
1.9233	137700	7.3936	-
1.9247	137800	7.3661	-
1.9261	137900	7.3823	-
1.9275	138000	7.3704	-
1.9289	138100	7.3324	-
1.9303	138200	7.3413	-
1.9317	138300	7.4154	-
1.9331	138400	7.38	-
1.9345	138500	7.372	-
1.9359	138600	7.3276	-
1.9373	138700	7.3933	-
1.9387	138800	7.3545	-
1.9401	138900	7.3212	-
1.9414	139000	7.385	-
1.9428	139100	7.3076	-
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1.9456	139300	7.3821	-
1.9470	139400	7.3561	-
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1.9512	139700	7.3862	-
1.9526	139800	7.3147	-
1.9540	139900	7.3153	-
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1.9568	140100	7.3512	-
1.9582	140200	7.3221	-
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1.9624	140500	7.418	-
1.9638	140600	7.3232	-
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1.9666	140800	7.3327	-
1.9680	140900	7.4102	-
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1.9722	141200	7.3593	-
1.9736	141300	7.3611	-
1.9750	141400	7.3889	-
1.9764	141500	7.3725	-
1.9778	141600	7.3545	-
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1.9820	141900	7.4105	-
1.9834	142000	7.3788	-
1.9847	142100	7.3548	-
1.9861	142200	7.3189	-
1.9875	142300	7.3751	-
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1.9903	142500	7.3409	-
1.9917	142600	7.3857	-
1.9931	142700	7.3424	-
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1.9973	143000	7.3281	-
1.9987	143100	7.349	-
2.0001	143200	7.3104	-
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2.0225	144800	7.3163	-
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2.0253	145000	7.2993	-
2.0266	145100	7.3124	-
2.0280	145200	7.2989	-
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2.0322	145500	7.3191	-
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2.0350	145700	7.3389	-
2.0364	145800	7.3943	-
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2.0406	146100	7.3273	-
2.0420	146200	7.3089	-
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2.0490	146700	7.3114	-
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2.0532	147000	7.3545	-
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2.0588	147400	7.3258	-
2.0602	147500	7.3218	-
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2.0630	147700	7.3125	-
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2.0699	148200	7.3358	-
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2.0769	148700	7.3584	-
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2.0811	149000	7.2871	-
2.0825	149100	7.3121	-
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2.0881	149500	7.3429	-
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2.0909	149700	7.3815	-
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2.0951	150000	7.3315	-
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2.1007	150400	7.3303	-
2.1021	150500	7.3025	-
2.1035	150600	7.3437	-
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2.1091	151000	7.314	-
2.1105	151100	7.3464	-
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2.1160	151500	7.3302	-
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2.1230	152000	7.3023	-
2.1244	152100	7.2937	-
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2.1300	152500	7.3939	-
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2.1328	152700	7.3048	-
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2.1370	153000	7.3228	-
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2.1440	153500	7.3807	-
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2.1510	154000	7.3041	-
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2.1551	154300	7.3208	-
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2.1621	154800	7.3107	-
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2.1691	155300	7.3192	-
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2.1747	155700	7.2876	-
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2.1789	156000	7.3048	-
2.1803	156100	7.2968	-
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2.1831	156300	7.2979	-
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2.1901	156800	7.282	-
2.1915	156900	7.3118	-
2.1929	157000	7.3027	-
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2.1957	157200	7.3048	-
2.1971	157300	7.2491	-
2.1984	157400	7.3033	-
2.1998	157500	7.2898	-
2.2012	157600	7.3246	-
2.2026	157700	7.308	-
2.2040	157800	7.289	-
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2.2110	158300	7.303	-
2.2124	158400	7.36	-
2.2138	158500	7.3017	-
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2.2166	158700	7.3325	-
2.2180	158800	7.3156	-
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2.2236	159200	7.3608	-
2.2250	159300	7.3074	-
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2.2306	159700	7.2576	-
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2.2390	160300	7.3345	-
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2.2417	160500	7.3287	-
2.2431	160600	7.3534	-
2.2445	160700	7.2882	-
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2.2487	161000	7.3321	-
2.2501	161100	7.3029	-
2.2515	161200	7.3154	-
2.2529	161300	7.3071	-
2.2543	161400	7.2898	-
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2.2571	161600	7.2799	-
2.2585	161700	7.3055	-
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2.2641	162100	7.3175	-
2.2655	162200	7.3058	-
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2.2711	162600	7.3143	-
2.2725	162700	7.3185	-
2.2739	162800	7.2857	-
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2.2795	163200	7.3945	-
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2.2850	163600	7.3571	-
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2.2920	164100	7.2971	-
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2.2948	164300	7.3488	-
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2.3004	164700	7.3247	-
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2.3060	165100	7.3051	-
2.3074	165200	7.3345	-
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2.3102	165400	7.3863	-
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2.3130	165600	7.2665	-
2.3144	165700	7.3614	-
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2.3186	166000	7.2881	-
2.3200	166100	7.3385	-
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2.3255	166500	7.2765	-
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2.3325	167000	7.3179	-
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2.3409	167600	7.3259	-
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2.3465	168000	7.3158	-
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2.3493	168200	7.2761	-
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2.3535	168500	7.3018	-
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2.3563	168700	7.3171	-
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2.3605	169000	7.3542	-
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2.3647	169300	7.2961	-
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2.3688	169600	7.3026	-
2.3702	169700	7.2861	-
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2.3730	169900	7.2482	-
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2.3772	170200	7.3291	-
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2.3870	170900	7.3249	-
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2.3982	171700	7.3371	-
2.3996	171800	7.3008	-
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2.4094	172500	7.3377	-
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2.4163	173000	7.3573	-
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2.4317	174100	7.2881	-
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2.4429	174900	7.2946	-
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2.4485	175300	7.3291	-
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2.4540	175700	7.3239	-
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2.4624	176300	7.3024	-
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2.4722	177000	7.2745	-
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2.4890	178200	7.3273	-
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2.4918	178400	7.2992	-
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2.5029	179200	7.2748	-
2.5043	179300	7.3157	-
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2.5071	179500	7.3562	-
2.5085	179600	7.3259	-
2.5099	179700	7.2998	-
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2.5141	180000	7.2841	-
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2.5169	180200	7.3038	-
2.5183	180300	7.3164	-
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2.5225	180600	7.2603	-
2.5239	180700	7.3197	-
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2.5281	181000	7.3215	-
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2.5337	181400	7.2607	-
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2.5365	181600	7.3084	-
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2.5420	182000	7.2937	-
2.5434	182100	7.3062	-
2.5448	182200	7.2687	-
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2.5490	182500	7.2938	-
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2.5518	182700	7.274	-
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2.5560	183000	7.3121	-
2.5574	183100	7.3066	-
2.5588	183200	7.2817	-
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2.5630	183500	7.2652	-
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2.5686	183900	7.2801	-
2.5700	184000	7.2579	-
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2.5770	184500	7.3052	-
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2.5798	184700	7.3175	-
2.5811	184800	7.3317	-
2.5825	184900	7.3527	-
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2.5867	185200	7.2636	-
2.5881	185300	7.2702	-
2.5895	185400	7.2714	-
2.5909	185500	7.3026	-
2.5923	185600	7.2877	-
2.5937	185700	7.2743	-
2.5951	185800	7.347	-
2.5965	185900	7.299	-
2.5979	186000	7.2587	-
2.5993	186100	7.2749	-
2.6007	186200	7.2782	-
2.6021	186300	7.3153	-
2.6035	186400	7.2518	-
2.6049	186500	7.3186	-
2.6063	186600	7.2809	-
2.6077	186700	7.2761	-
2.6091	186800	7.3042	-
2.6105	186900	7.3012	-
2.6119	187000	7.3638	-
2.6133	187100	7.338	-
2.6147	187200	7.3274	-
2.6161	187300	7.3081	-
2.6175	187400	7.2949	-
2.6189	187500	7.2875	-
2.6203	187600	7.2622	-
2.6217	187700	7.3024	-
2.6231	187800	7.319	-
2.6244	187900	7.3148	-
2.6258	188000	7.3019	-
2.6272	188100	7.2976	-
2.6286	188200	7.241	-
2.6300	188300	7.3177	-
2.6314	188400	7.2504	-
2.6328	188500	7.3003	-
2.6342	188600	7.2712	-
2.6356	188700	7.3366	-
2.6370	188800	7.3183	-
2.6384	188900	7.2848	-
2.6398	189000	7.2839	-
2.6412	189100	7.2559	-
2.6426	189200	7.3602	-
2.6440	189300	7.3584	-
2.6454	189400	7.2723	-
2.6468	189500	7.313	-
2.6482	189600	7.2388	-
2.6496	189700	7.3167	-
2.6510	189800	7.2526	-
2.6524	189900	7.2833	-
2.6538	190000	7.2512	-
2.6552	190100	7.3198	-
2.6566	190200	7.3318	-
2.6580	190300	7.3513	-
2.6594	190400	7.3555	-
2.6608	190500	7.2456	-
2.6622	190600	7.3712	-
2.6636	190700	7.3138	-
2.6650	190800	7.2785	-
2.6664	190900	7.2745	-
2.6677	191000	7.2962	-
2.6691	191100	7.3215	-
2.6705	191200	7.3452	-
2.6719	191300	7.3123	-
2.6733	191400	7.2513	-
2.6747	191500	7.2572	-
2.6761	191600	7.2322	-
2.6775	191700	7.2792	-
2.6789	191800	7.2836	-
2.6803	191900	7.3999	-
2.6817	192000	7.2586	-
2.6831	192100	7.3538	-
2.6845	192200	7.2851	-
2.6859	192300	7.3246	-
2.6873	192400	7.2882	-
2.6887	192500	7.3315	-
2.6901	192600	7.3741	-
2.6915	192700	7.2929	-
2.6929	192800	7.3327	-
2.6943	192900	7.3244	-
2.6957	193000	7.3275	-
2.6971	193100	7.2758	-
2.6985	193200	7.2527	-
2.6999	193300	7.3216	-
2.7013	193400	7.2593	-
2.7027	193500	7.2751	-
2.7041	193600	7.3024	-
2.7055	193700	7.2553	-
2.7069	193800	7.2869	-
2.7083	193900	7.2515	-
2.7096	194000	7.242	-
2.7110	194100	7.25	-
2.7124	194200	7.2729	-
2.7138	194300	7.3367	-
2.7152	194400	7.2988	-
2.7166	194500	7.3031	-
2.7180	194600	7.2935	-
2.7194	194700	7.2687	-
2.7208	194800	7.3108	-
2.7222	194900	7.2569	-
2.7236	195000	7.2865	-
2.7250	195100	7.2696	-
2.7264	195200	7.2889	-
2.7278	195300	7.2981	-
2.7292	195400	7.2823	-
2.7306	195500	7.2855	-
2.7320	195600	7.2456	-
2.7334	195700	7.2228	-
2.7348	195800	7.3116	-
2.7362	195900	7.2661	-
2.7376	196000	7.2469	-
2.7390	196100	7.2889	-
2.7404	196200	7.2418	-
2.7418	196300	7.314	-
2.7432	196400	7.2841	-
2.7446	196500	7.3331	-
2.7460	196600	7.334	-
2.7474	196700	7.2825	-
2.7488	196800	7.3173	-
2.7502	196900	7.3266	-
2.7516	197000	7.3136	-
2.7529	197100	7.2432	-
2.7543	197200	7.3033	-
2.7557	197300	7.2665	-
2.7571	197400	7.2943	-
2.7585	197500	7.3214	-
2.7599	197600	7.2956	-
2.7613	197700	7.2683	-
2.7627	197800	7.2722	-
2.7641	197900	7.2536	-
2.7655	198000	7.284	-
2.7669	198100	7.2642	-
2.7683	198200	7.2795	-
2.7697	198300	7.2716	-
2.7711	198400	7.3057	-
2.7725	198500	7.2599	-
2.7739	198600	7.2635	-
2.7753	198700	7.3134	-
2.7767	198800	7.3346	-
2.7781	198900	7.2816	-
2.7795	199000	7.2219	-
2.7809	199100	7.2627	-
2.7823	199200	7.2471	-
2.7837	199300	7.2518	-
2.7851	199400	7.2611	-
2.7865	199500	7.3295	-
2.7879	199600	7.2581	-
2.7893	199700	7.2973	-
2.7907	199800	7.2628	-
2.7921	199900	7.2983	-
2.7935	200000	7.2916	7.1932
2.7948	200100	7.2727	-
2.7962	200200	7.2925	-
2.7976	200300	7.2853	-
2.7990	200400	7.2757	-
2.8004	200500	7.2834	-
2.8018	200600	7.3391	-
2.8032	200700	7.3253	-
2.8046	200800	7.2401	-
2.8060	200900	7.2734	-
2.8074	201000	7.3169	-
2.8088	201100	7.2849	-
2.8102	201200	7.2474	-
2.8116	201300	7.2554	-
2.8130	201400	7.3411	-
2.8144	201500	7.3799	-
2.8158	201600	7.3022	-
2.8172	201700	7.3073	-
2.8186	201800	7.276	-
2.8200	201900	7.3047	-
2.8214	202000	7.2872	-
2.8228	202100	7.2589	-
2.8242	202200	7.2912	-
2.8256	202300	7.314	-
2.8270	202400	7.2662	-
2.8284	202500	7.2632	-
2.8298	202600	7.2718	-
2.8312	202700	7.2618	-
2.8326	202800	7.3372	-
2.8340	202900	7.2682	-
2.8354	203000	7.2952	-
2.8368	203100	7.2387	-
2.8381	203200	7.2713	-
2.8395	203300	7.257	-
2.8409	203400	7.2863	-
2.8423	203500	7.2803	-
2.8437	203600	7.332	-
2.8451	203700	7.3228	-
2.8465	203800	7.2994	-
2.8479	203900	7.2788	-
2.8493	204000	7.2816	-
2.8507	204100	7.3265	-
2.8521	204200	7.2584	-
2.8535	204300	7.3064	-
2.8549	204400	7.2654	-
2.8563	204500	7.2383	-
2.8577	204600	7.2946	-
2.8591	204700	7.2904	-
2.8605	204800	7.2705	-
2.8619	204900	7.3591	-
2.8633	205000	7.2576	-
2.8647	205100	7.2923	-
2.8661	205200	7.2215	-
2.8675	205300	7.3828	-
2.8689	205400	7.3112	-
2.8703	205500	7.2934	-
2.8717	205600	7.2748	-
2.8731	205700	7.3172	-
2.8745	205800	7.3322	-
2.8759	205900	7.2914	-
2.8773	206000	7.2947	-
2.8787	206100	7.2877	-
2.8800	206200	7.3201	-
2.8814	206300	7.2855	-
2.8828	206400	7.2369	-
2.8842	206500	7.2389	-
2.8856	206600	7.2633	-
2.8870	206700	7.2558	-
2.8884	206800	7.2534	-
2.8898	206900	7.2581	-
2.8912	207000	7.308	-
2.8926	207100	7.3036	-
2.8940	207200	7.2383	-
2.8954	207300	7.2914	-
2.8968	207400	7.2914	-
2.8982	207500	7.2854	-
2.8996	207600	7.3364	-
2.9010	207700	7.2488	-
2.9024	207800	7.264	-
2.9038	207900	7.2776	-
2.9052	208000	7.28	-
2.9066	208100	7.2526	-
2.9080	208200	7.2958	-
2.9094	208300	7.273	-
2.9108	208400	7.2924	-
2.9122	208500	7.2882	-
2.9136	208600	7.3607	-
2.9150	208700	7.3232	-
2.9164	208800	7.3124	-
2.9178	208900	7.2363	-
2.9192	209000	7.2868	-
2.9206	209100	7.2967	-
2.9220	209200	7.3001	-
2.9233	209300	7.297	-
2.9247	209400	7.264	-
2.9261	209500	7.2676	-
2.9275	209600	7.2476	-
2.9289	209700	7.2872	-
2.9303	209800	7.2727	-
2.9317	209900	7.3294	-
2.9331	210000	7.3165	-
2.9345	210100	7.2726	-
2.9359	210200	7.2513	-
2.9373	210300	7.3356	-
2.9387	210400	7.3047	-
2.9401	210500	7.2277	-
2.9415	210600	7.2609	-
2.9429	210700	7.3206	-
2.9443	210800	7.2745	-
2.9457	210900	7.294	-
2.9471	211000	7.2756	-
2.9485	211100	7.2556	-
2.9499	211200	7.3683	-
2.9513	211300	7.2758	-
2.9527	211400	7.2821	-
2.9541	211500	7.2843	-
2.9555	211600	7.2722	-
2.9569	211700	7.2768	-
2.9583	211800	7.2147	-
2.9597	211900	7.2815	-
2.9611	212000	7.3042	-
2.9625	212100	7.2778	-
2.9639	212200	7.2861	-
2.9652	212300	7.2806	-
2.9666	212400	7.3319	-
2.9680	212500	7.265	-
2.9694	212600	7.3076	-
2.9708	212700	7.2925	-
2.9722	212800	7.222	-
2.9736	212900	7.2479	-
2.9750	213000	7.2675	-
2.9764	213100	7.3411	-
2.9778	213200	7.2452	-
2.9792	213300	7.3194	-
2.9806	213400	7.2683	-
2.9820	213500	7.3121	-
2.9834	213600	7.274	-
2.9848	213700	7.2328	-
2.9862	213800	7.2927	-
2.9876	213900	7.269	-
2.9890	214000	7.2376	-
2.9904	214100	7.2713	-
2.9918	214200	7.2834	-
2.9932	214300	7.258	-
2.9946	214400	7.2781	-
2.9960	214500	7.2466	-
2.9974	214600	7.2974	-
2.9988	214700	7.2487	-

Framework Versions

Python: 3.12.3
Sentence Transformers: 5.1.0
Transformers: 4.55.4
PyTorch: 2.5.1+cu121
Accelerate: 1.10.1
Datasets: 4.0.0
Tokenizers: 0.21.4

Citation

BibTeX

Sentence Transformers

bibtex

@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

bibtex

@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},
}

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 5.6 tokens max: 20 tokens	min: 3 tokens mean: 5.79 tokens max: 24 tokens	min: 0.15 mean: 0.43 max: 1.0

sentence1	sentence2	score
`booster face cleanser`	`pizza cutter`	`0.24`
`line sinker`	`accessories`	`1.0`
`tricovel`	`cot bed mattress protector`	`0.28`

sentence1	sentence2	score
`printed set`	`crushed outfit`	`1.0`
`value`	`eva cosmetics serum`	`0.23`
`zino shakes`	`candy`	`0.27`

KhaledReda/all-MiniLM-L6-v10-pair_score

all-MiniLM-L6-v10-pair_score

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

pairs_three_scores_v8

Evaluation Dataset

pairs_three_scores_v8

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

CoSENTLoss

KhaledReda/
all-MiniLM-L6-v10-pair_score