This is a sentence-transformers model finetuned from much1na/ettin-encoder-17m-gooaq on the gooaq dataset. It maps sentences & paragraphs to a 256-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: much1na/ettin-encoder-17m-gooaq
Maximum Sequence Length: 256 tokens
Output Dimensionality: 256 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- gooaq
Language: en

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': 'ModernBertModel'})
  (1): Pooling({'word_embedding_dimension': 256, '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})
)

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("much1na/ettin-encoder-17m-gooaq-gooaq")
# Run inference
queries = [
    "are you human korean novela?",
]
documents = [
    "Are You Human? (Korean: 너도 인간이니; RR: Neodo Inganini; lit. Are You Human Too?) is a 2018 South Korean television series starring Seo Kang-jun and Gong Seung-yeon. It aired on KBS2's Mondays and Tuesdays at 22:00 (KST) time slot, from June 4 to August 7, 2018.",
    'A relative of European pear varieties like Bartlett and Anjou, the Asian pear is great used in recipes or simply eaten out of hand. It retains a crispness that works well in slaws and salads, and it holds its shape better than European pears when baked and cooked.',
    'Many cysts heal on their own, which means that conservative treatments like rest and anti-inflammatory painkillers can often be enough to get rid of them. However, in some cases, routine drainage of the sac may be necessary to reduce symptoms.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 256] [3, 256]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.7030,  0.0526, -0.2249]])

Evaluation

Metrics

Information Retrieval

Datasets: NanoNQ, NanoMSMARCO, NanoNQ, NanoMSMARCO, NanoClimateFEVER, NanoDBPedia, NanoFEVER, NanoFiQA2018, NanoHotpotQA, NanoMSMARCO, NanoNFCorpus, NanoNQ, NanoQuoraRetrieval, NanoSCIDOCS, NanoArguAna, NanoSciFact and NanoTouche2020
Evaluated with InformationRetrievalEvaluator

Metric	NanoNQ	NanoMSMARCO	NanoClimateFEVER	NanoDBPedia	NanoFEVER	NanoFiQA2018	NanoHotpotQA	NanoNFCorpus	NanoQuoraRetrieval	NanoSCIDOCS	NanoArguAna	NanoSciFact	NanoTouche2020
cosine_accuracy@1	0.26	0.32	0.24	0.56	0.46	0.34	0.56	0.32	0.9	0.18	0.1	0.28	0.4898
cosine_accuracy@3	0.46	0.46	0.44	0.66	0.64	0.48	0.7	0.44	0.94	0.38	0.4	0.44	0.7551
cosine_accuracy@5	0.52	0.5	0.58	0.74	0.78	0.5	0.76	0.46	0.98	0.44	0.54	0.5	0.8571
cosine_accuracy@10	0.64	0.68	0.66	0.78	0.92	0.56	0.82	0.5	1.0	0.58	0.7	0.56	0.9388
cosine_precision@1	0.26	0.32	0.24	0.56	0.46	0.34	0.56	0.32	0.9	0.18	0.1	0.28	0.4898
cosine_precision@3	0.16	0.1533	0.16	0.42	0.22	0.1933	0.2867	0.2533	0.3867	0.1667	0.1333	0.16	0.4626
cosine_precision@5	0.108	0.1	0.132	0.38	0.16	0.128	0.192	0.196	0.248	0.132	0.108	0.112	0.4408
cosine_precision@10	0.068	0.068	0.082	0.306	0.094	0.076	0.106	0.144	0.132	0.106	0.07	0.066	0.3857
cosine_recall@1	0.26	0.32	0.1317	0.0575	0.4267	0.163	0.28	0.0138	0.7907	0.036	0.1	0.245	0.0343
cosine_recall@3	0.43	0.46	0.2207	0.0961	0.6067	0.2779	0.43	0.0552	0.8987	0.1037	0.4	0.41	0.0978
cosine_recall@5	0.48	0.5	0.3057	0.1266	0.7467	0.3059	0.48	0.0638	0.952	0.1357	0.54	0.475	0.151
cosine_recall@10	0.6	0.68	0.3607	0.1852	0.8767	0.3668	0.53	0.0774	0.9867	0.2177	0.7	0.55	0.2478
cosine_ndcg@10	0.4279	0.4732	0.2902	0.3861	0.6448	0.311	0.4968	0.1907	0.9313	0.1917	0.3848	0.4055	0.4267
cosine_mrr@10	0.379	0.4104	0.3685	0.6277	0.5929	0.416	0.6459	0.3799	0.9252	0.3007	0.285	0.3709	0.652
cosine_map@100	0.3802	0.427	0.2286	0.2419	0.5654	0.2584	0.4198	0.074	0.9083	0.1329	0.2981	0.3627	0.3177

Nano BEIR

Dataset: NanoBEIR_mean
Evaluated with NanoBEIREvaluator with these parameters:
json
1{ 2 "dataset_names": [ 3 "NQ", 4 "MSMARCO" 5 ], 6 "dataset_id": "sentence-transformers/NanoBEIR-en" 7}

Metric	Value
cosine_accuracy@1	0.32
cosine_accuracy@3	0.46
cosine_accuracy@5	0.5
cosine_accuracy@10	0.68
cosine_precision@1	0.32
cosine_precision@3	0.1567
cosine_precision@5	0.102
cosine_precision@10	0.07
cosine_recall@1	0.315
cosine_recall@3	0.455
cosine_recall@5	0.49
cosine_recall@10	0.66
cosine_ndcg@10	0.4743
cosine_mrr@10	0.4202
cosine_map@100	0.4272

Nano BEIR

Dataset: NanoBEIR_mean
Evaluated with NanoBEIREvaluator with these parameters:
json
1{ 2 "dataset_names": [ 3 "NQ", 4 "MSMARCO" 5 ], 6 "dataset_id": "sentence-transformers/NanoBEIR-en" 7}

Metric	Value
cosine_accuracy@1	0.29
cosine_accuracy@3	0.47
cosine_accuracy@5	0.51
cosine_accuracy@10	0.66
cosine_precision@1	0.29
cosine_precision@3	0.16
cosine_precision@5	0.104
cosine_precision@10	0.068
cosine_recall@1	0.29
cosine_recall@3	0.455
cosine_recall@5	0.49
cosine_recall@10	0.64
cosine_ndcg@10	0.451
cosine_mrr@10	0.3953
cosine_map@100	0.4041

Nano BEIR

Dataset: NanoBEIR_mean
Evaluated with NanoBEIREvaluator with these parameters:
json
1{ 2 "dataset_names": [ 3 "climatefever", 4 "dbpedia", 5 "fever", 6 "fiqa2018", 7 "hotpotqa", 8 "msmarco", 9 "nfcorpus", 10 "nq", 11 "quoraretrieval", 12 "scidocs", 13 "arguana", 14 "scifact", 15 "touche2020" 16 ], 17 "dataset_id": "sentence-transformers/NanoBEIR-en" 18}

Metric	Value
cosine_accuracy@1	0.3854
cosine_accuracy@3	0.5535
cosine_accuracy@5	0.6275
cosine_accuracy@10	0.7184
cosine_precision@1	0.3854
cosine_precision@3	0.2428
cosine_precision@5	0.1874
cosine_precision@10	0.1311
cosine_recall@1	0.2199
cosine_recall@3	0.3451
cosine_recall@5	0.4048
cosine_recall@10	0.4907
cosine_ndcg@10	0.4277
cosine_mrr@10	0.4888
cosine_map@100	0.355

Training Details

Training Dataset

gooaq

Dataset: gooaq at b089f72
Size: 3,011,496 training samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 12.11 tokens
max: 21 tokens
min: 17 tokens
mean: 58.29 tokens
max: 118 tokens

Samples:

question	answer
`what is the difference between butter fat and milk fat?`	`Modern cookbooks turn that around, referring to butter by its milk fat content. The difference is purely one of custom, rather than science, since milk and butter contain exactly the same kind of fat.`
`what i love u means?`	`Each time you say "I love you," you are really saying "I am here for you." "Being here" means being fully present in the relationship—physically, emotionally and spiritually. Saying "I love you" means "I am committed to you" and "I am committed to us." You are telling that person you are in this relationship.`
`what finger does wedding ring go on uk?`	`By wearing the ring on the fourth finger of the left hand, a married couple symbolically declares their eternal love for each other. In Britain, only women tended to wear a wedding ring until the 1st and 2nd World Wars, when married male soldiers started to wear rings to remind them of their partner.`

Loss: CachedMultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "mini_batch_size": 256, 5 "gather_across_devices": false, 6 "directions": [ 7 "query_to_doc" 8 ], 9 "partition_mode": "joint", 10 "hardness_mode": null, 11 "hardness_strength": 0.0 12}

Evaluation Dataset

gooaq

Dataset: gooaq at b089f72
Size: 1,000 evaluation samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 11.96 tokens
max: 24 tokens
min: 16 tokens
mean: 57.75 tokens
max: 125 tokens

Samples:

question	answer
`what db are train horns?`	`The rule mandates when locomotive engineers sound horns — at least 15, but no more than 20 seconds, in advance of all public crossings; how they're sounded — in a pattern of two long, one short, one long blast; and how loud the horns are — between 96 and 110 decibels.`
`is it ok to drink apple cider vinegar while intermittent fasting?`	`Drinking apple cider vinegar during your fast can help enhance your fast by helping you feel full and satiated. It also helps absorb minerals into your body from meals you had previously eaten before your fast which can help you feel healthy and full.`
`how long does cold sore symptoms last?`	`The first time you have a cold sore, symptoms may not start for up to 20 days after you were first exposed to the virus. The sores can last several days, and the blisters can take two to three weeks to heal completely.`

Loss: CachedMultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "mini_batch_size": 256, 5 "gather_across_devices": false, 6 "directions": [ 7 "query_to_doc" 8 ], 9 "partition_mode": "joint", 10 "hardness_mode": null, 11 "hardness_strength": 0.0 12}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 1024
per_device_eval_batch_size: 1024
learning_rate: 8e-05
num_train_epochs: 1
warmup_steps: 0.05
fp16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 1024
per_device_eval_batch_size: 1024
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 8e-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: None
warmup_ratio: None
warmup_steps: 0.05
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
enable_jit_checkpoint: False
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
use_cpu: False
seed: 42
data_seed: None
bf16: False
fp16: True
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: -1
ddp_backend: None
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
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_for_metrics: []
eval_do_concat_batches: True
auto_find_batch_size: False
full_determinism: False
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_num_input_tokens_seen: no
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: True
use_cache: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	NanoNQ_cosine_ndcg@10	NanoMSMARCO_cosine_ndcg@10	NanoBEIR_mean_cosine_ndcg@10	NanoClimateFEVER_cosine_ndcg@10	NanoDBPedia_cosine_ndcg@10	NanoFEVER_cosine_ndcg@10	NanoFiQA2018_cosine_ndcg@10	NanoHotpotQA_cosine_ndcg@10	NanoNFCorpus_cosine_ndcg@10	NanoQuoraRetrieval_cosine_ndcg@10	NanoSCIDOCS_cosine_ndcg@10	NanoArguAna_cosine_ndcg@10	NanoSciFact_cosine_ndcg@10	NanoTouche2020_cosine_ndcg@10
0.0068	10	0.3653	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0136	20	0.3670	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0204	30	0.3560	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0272	40	0.3503	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0340	50	0.3579	0.1703	0.3935	0.4993	0.4464	-	-	-	-	-	-	-	-	-	-	-
0.0408	60	0.3470	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0476	70	0.3437	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0544	80	0.3333	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0612	90	0.3277	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0680	100	0.3268	0.1730	0.4102	0.4633	0.4368	-	-	-	-	-	-	-	-	-	-	-
0.0748	110	0.3270	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0816	120	0.3301	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0884	130	0.3320	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0952	140	0.3253	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1020	150	0.3217	0.1770	0.3984	0.4861	0.4422	-	-	-	-	-	-	-	-	-	-	-
0.1088	160	0.3143	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1156	170	0.3197	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1224	180	0.3140	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1292	190	0.3165	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1360	200	0.3156	0.1693	0.4003	0.4968	0.4486	-	-	-	-	-	-	-	-	-	-	-
0.1428	210	0.3166	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1496	220	0.3093	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1564	230	0.3172	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1632	240	0.3064	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1700	250	0.3104	0.1688	0.3790	0.4799	0.4295	-	-	-	-	-	-	-	-	-	-	-
0.1768	260	0.3047	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1835	270	0.3021	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1903	280	0.3019	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1971	290	0.3003	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2039	300	0.3101	0.1626	0.4074	0.4941	0.4507	-	-	-	-	-	-	-	-	-	-	-
0.2107	310	0.3055	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2175	320	0.3105	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2243	330	0.2983	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2311	340	0.3022	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2379	350	0.3020	0.1615	0.4071	0.4857	0.4464	-	-	-	-	-	-	-	-	-	-	-
0.2447	360	0.3003	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2515	370	0.2960	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2583	380	0.2998	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2651	390	0.2949	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2719	400	0.2943	0.1567	0.3990	0.4857	0.4423	-	-	-	-	-	-	-	-	-	-	-
0.2787	410	0.3027	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2855	420	0.2909	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2923	430	0.2904	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2991	440	0.2995	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3059	450	0.2892	0.1546	0.3888	0.4869	0.4379	-	-	-	-	-	-	-	-	-	-	-
0.3127	460	0.3006	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3195	470	0.2991	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3263	480	0.2976	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3331	490	0.2871	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3399	500	0.2803	0.1481	0.4186	0.4894	0.4540	-	-	-	-	-	-	-	-	-	-	-
0.3467	510	0.2807	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3535	520	0.2959	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3603	530	0.2822	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3671	540	0.2929	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3739	550	0.2857	0.1499	0.4278	0.4749	0.4514	-	-	-	-	-	-	-	-	-	-	-
0.3807	560	0.2915	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3875	570	0.2833	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3943	580	0.2787	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4011	590	0.2835	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4079	600	0.2838	0.1487	0.4235	0.4704	0.4469	-	-	-	-	-	-	-	-	-	-	-
0.4147	610	0.2839	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4215	620	0.2875	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4283	630	0.2789	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4351	640	0.2838	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4419	650	0.2777	0.1493	0.4453	0.4678	0.4566	-	-	-	-	-	-	-	-	-	-	-
0.4487	660	0.2855	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4555	670	0.2832	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4623	680	0.2791	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4691	690	0.2752	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4759	700	0.2816	0.1469	0.4228	0.4924	0.4576	-	-	-	-	-	-	-	-	-	-	-
0.4827	710	0.2814	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4895	720	0.2849	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4963	730	0.2753	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5031	740	0.2979	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5099	750	0.2800	0.1472	0.4150	0.4825	0.4487	-	-	-	-	-	-	-	-	-	-	-
0.5167	760	0.2816	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5235	770	0.2880	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5303	780	0.2885	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5370	790	0.2803	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5438	800	0.2731	0.1432	0.4043	0.4673	0.4358	-	-	-	-	-	-	-	-	-	-	-
0.5506	810	0.2815	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5574	820	0.2670	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5642	830	0.2764	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5710	840	0.2780	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5778	850	0.2686	0.1426	0.4218	0.4939	0.4579	-	-	-	-	-	-	-	-	-	-	-
0.5846	860	0.2679	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5914	870	0.2724	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5982	880	0.2780	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6050	890	0.2718	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6118	900	0.2765	0.1404	0.4214	0.5051	0.4633	-	-	-	-	-	-	-	-	-	-	-
0.6186	910	0.2790	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6254	920	0.2777	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6322	930	0.2738	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6390	940	0.2687	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6458	950	0.2716	0.1392	0.4126	0.4960	0.4543	-	-	-	-	-	-	-	-	-	-	-
0.6526	960	0.2756	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6594	970	0.2771	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6662	980	0.2832	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6730	990	0.2864	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6798	1000	0.2789	0.1406	0.4145	0.4837	0.4491	-	-	-	-	-	-	-	-	-	-	-
0.6866	1010	0.2730	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6934	1020	0.2859	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7002	1030	0.2797	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7070	1040	0.2839	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7138	1050	0.2766	0.1393	0.4257	0.5026	0.4641	-	-	-	-	-	-	-	-	-	-	-
0.7206	1060	0.2714	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7274	1070	0.2891	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7342	1080	0.2802	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7410	1090	0.2826	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7478	1100	0.2818	0.1374	0.4293	0.5091	0.4692	-	-	-	-	-	-	-	-	-	-	-
0.7546	1110	0.2752	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7614	1120	0.2844	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7682	1130	0.2788	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7750	1140	0.2772	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7818	1150	0.2775	0.1375	0.4460	0.4985	0.4722	-	-	-	-	-	-	-	-	-	-	-
0.7886	1160	0.2773	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7954	1170	0.2793	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8022	1180	0.2823	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8090	1190	0.2805	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8158	1200	0.2834	0.1362	0.4380	0.4995	0.4688	-	-	-	-	-	-	-	-	-	-	-
0.8226	1210	0.2903	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8294	1220	0.2845	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8362	1230	0.2746	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8430	1240	0.2731	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8498	1250	0.2736	0.1355	0.4329	0.4998	0.4663	-	-	-	-	-	-	-	-	-	-	-
0.8566	1260	0.2793	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8634	1270	0.2784	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8702	1280	0.2831	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8770	1290	0.2811	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8838	1300	0.2790	0.1332	0.4378	0.5000	0.4689	-	-	-	-	-	-	-	-	-	-	-
0.8906	1310	0.2865	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8973	1320	0.2836	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9041	1330	0.2814	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9109	1340	0.2909	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9177	1350	0.2839	0.1336	0.4344	0.5067	0.4705	-	-	-	-	-	-	-	-	-	-	-
0.9245	1360	0.2832	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9313	1370	0.2781	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9381	1380	0.2846	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9449	1390	0.2864	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9517	1400	0.2830	0.1339	0.4356	0.5142	0.4749	-	-	-	-	-	-	-	-	-	-	-
0.9585	1410	0.2804	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9653	1420	0.2818	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9721	1430	0.2984	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9789	1440	0.2853	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9857	1450	0.2913	0.1335	0.4350	0.5135	0.4743	-	-	-	-	-	-	-	-	-	-	-
0.9925	1460	0.2823	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9993	1470	0.2759	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0034	10	0.1514	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0068	20	0.1348	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0102	30	0.1501	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0136	40	0.1469	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0170	50	0.1486	0.0395	0.4245	0.5081	0.4663	-	-	-	-	-	-	-	-	-	-	-
0.0204	60	0.1446	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0238	70	0.1472	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0272	80	0.1454	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0306	90	0.1464	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0340	100	0.1528	0.0420	0.4374	0.4943	0.4659	-	-	-	-	-	-	-	-	-	-	-
0.0374	110	0.1494	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0408	120	0.1590	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0442	130	0.1577	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0476	140	0.1531	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0510	150	0.1555	0.0421	0.4195	0.4606	0.4400	-	-	-	-	-	-	-	-	-	-	-
0.0544	160	0.1579	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0578	170	0.1604	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0612	180	0.1654	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0646	190	0.1666	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0680	200	0.1558	0.0427	0.4273	0.4996	0.4635	-	-	-	-	-	-	-	-	-	-	-
0.0714	210	0.1655	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0748	220	0.1669	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0782	230	0.1637	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0816	240	0.1656	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0850	250	0.1704	0.0417	0.4301	0.4775	0.4538	-	-	-	-	-	-	-	-	-	-	-
0.0884	260	0.1537	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0918	270	0.1650	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0952	280	0.1647	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0986	290	0.1710	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1020	300	0.1726	0.0436	0.4233	0.5005	0.4619	-	-	-	-	-	-	-	-	-	-	-
0.1054	310	0.1673	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1088	320	0.1697	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1122	330	0.1603	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1156	340	0.1721	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1190	350	0.1604	0.0431	0.4038	0.4461	0.4250	-	-	-	-	-	-	-	-	-	-	-
0.1224	360	0.1702	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1258	370	0.1627	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1292	380	0.1646	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1326	390	0.1722	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1360	400	0.1613	0.0444	0.4069	0.4990	0.4529	-	-	-	-	-	-	-	-	-	-	-
0.1394	410	0.1606	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1428	420	0.1563	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1462	430	0.1715	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1496	440	0.1635	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1530	450	0.1579	0.0415	0.4282	0.5051	0.4667	-	-	-	-	-	-	-	-	-	-	-
0.1564	460	0.1656	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1598	470	0.1581	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1632	480	0.1668	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1666	490	0.1534	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1700	500	0.1625	0.0433	0.4173	0.4824	0.4498	-	-	-	-	-	-	-	-	-	-	-
0.1734	510	0.1540	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1768	520	0.1619	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1802	530	0.1654	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1836	540	0.1647	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1870	550	0.1592	0.0419	0.4205	0.4947	0.4576	-	-	-	-	-	-	-	-	-	-	-
0.1904	560	0.1645	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1938	570	0.1527	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1972	580	0.1651	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2006	590	0.1588	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2040	600	0.1577	0.0426	0.4060	0.5054	0.4557	-	-	-	-	-	-	-	-	-	-	-
0.2074	610	0.1607	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2108	620	0.1662	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2142	630	0.1638	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2176	640	0.1602	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2210	650	0.1600	0.0420	0.4251	0.5394	0.4822	-	-	-	-	-	-	-	-	-	-	-
0.2244	660	0.1627	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2278	670	0.1568	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2312	680	0.1504	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2346	690	0.1564	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2380	700	0.1541	0.0418	0.4321	0.5041	0.4681	-	-	-	-	-	-	-	-	-	-	-
0.2414	710	0.1601	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2448	720	0.1593	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2482	730	0.1587	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2516	740	0.1637	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2550	750	0.1580	0.0421	0.4227	0.5081	0.4654	-	-	-	-	-	-	-	-	-	-	-
0.2584	760	0.1652	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2618	770	0.1602	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2652	780	0.1521	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2686	790	0.1525	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2720	800	0.1569	0.0388	0.4142	0.5084	0.4613	-	-	-	-	-	-	-	-	-	-	-
0.2754	810	0.1475	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2788	820	0.1540	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2822	830	0.1521	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2856	840	0.1614	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2890	850	0.1571	0.0405	0.4213	0.4906	0.4559	-	-	-	-	-	-	-	-	-	-	-
0.2924	860	0.1504	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2958	870	0.1479	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2992	880	0.1558	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3026	890	0.1612	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3060	900	0.1551	0.0421	0.4551	0.4597	0.4574	-	-	-	-	-	-	-	-	-	-	-
0.3094	910	0.1509	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3128	920	0.1588	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3162	930	0.1444	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3196	940	0.1593	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3230	950	0.1480	0.0402	0.4624	0.4901	0.4762	-	-	-	-	-	-	-	-	-	-	-
0.3264	960	0.1594	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3298	970	0.1576	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3332	980	0.1573	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3366	990	0.1466	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3400	1000	0.1590	0.0396	0.4517	0.4813	0.4665	-	-	-	-	-	-	-	-	-	-	-
0.3434	1010	0.1540	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3468	1020	0.1495	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3502	1030	0.1494	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3536	1040	0.1480	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3570	1050	0.1547	0.0406	0.4345	0.4964	0.4655	-	-	-	-	-	-	-	-	-	-	-
0.3604	1060	0.1504	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3638	1070	0.1541	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3672	1080	0.1499	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3706	1090	0.1496	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3740	1100	0.1605	0.0406	0.4423	0.4929	0.4676	-	-	-	-	-	-	-	-	-	-	-
0.3774	1110	0.1514	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3808	1120	0.1509	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3842	1130	0.1481	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3876	1140	0.1530	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3910	1150	0.1468	0.0399	0.4320	0.4844	0.4582	-	-	-	-	-	-	-	-	-	-	-
0.3944	1160	0.1500	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3978	1170	0.1470	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4012	1180	0.1468	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4046	1190	0.1540	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4080	1200	0.1585	0.0387	0.4395	0.4894	0.4644	-	-	-	-	-	-	-	-	-	-	-
0.4114	1210	0.1551	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4148	1220	0.1539	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4182	1230	0.1523	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4216	1240	0.1500	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4250	1250	0.1571	0.0385	0.4249	0.4970	0.4609	-	-	-	-	-	-	-	-	-	-	-
0.4284	1260	0.1488	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4318	1270	0.1550	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4352	1280	0.1448	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4386	1290	0.1480	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4420	1300	0.1506	0.0384	0.4314	0.4718	0.4516	-	-	-	-	-	-	-	-	-	-	-
0.4454	1310	0.1480	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4488	1320	0.1414	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4522	1330	0.1520	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4556	1340	0.1493	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4590	1350	0.1487	0.0391	0.4105	0.4874	0.4489	-	-	-	-	-	-	-	-	-	-	-
0.4624	1360	0.1462	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4658	1370	0.1535	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4692	1380	0.1490	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4726	1390	0.1433	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4760	1400	0.1443	0.0390	0.4310	0.4835	0.4573	-	-	-	-	-	-	-	-	-	-	-
0.4794	1410	0.1434	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4828	1420	0.1405	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4862	1430	0.1374	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4896	1440	0.1417	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4930	1450	0.1422	0.0384	0.4148	0.4860	0.4504	-	-	-	-	-	-	-	-	-	-	-
0.4964	1460	0.1526	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4998	1470	0.1501	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5032	1480	0.1408	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5066	1490	0.1443	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5100	1500	0.1498	0.0380	0.4215	0.4965	0.4590	-	-	-	-	-	-	-	-	-	-	-
0.5134	1510	0.1368	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5168	1520	0.1418	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5202	1530	0.1366	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5236	1540	0.1391	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5270	1550	0.1519	0.0371	0.4198	0.4728	0.4463	-	-	-	-	-	-	-	-	-	-	-
0.5304	1560	0.1460	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5338	1570	0.1359	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5372	1580	0.1449	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5406	1590	0.1360	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5440	1600	0.1371	0.0364	0.4248	0.4670	0.4459	-	-	-	-	-	-	-	-	-	-	-
0.5474	1610	0.1368	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5508	1620	0.1443	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5542	1630	0.1379	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5576	1640	0.1433	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5610	1650	0.1441	0.0363	0.4346	0.4803	0.4575	-	-	-	-	-	-	-	-	-	-	-
0.5644	1660	0.1481	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5678	1670	0.1450	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5712	1680	0.1476	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5746	1690	0.1373	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5780	1700	0.1424	0.0364	0.4261	0.4597	0.4429	-	-	-	-	-	-	-	-	-	-	-
0.5814	1710	0.1444	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5848	1720	0.1423	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5882	1730	0.1389	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5916	1740	0.1384	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5950	1750	0.1393	0.0371	0.4202	0.4794	0.4498	-	-	-	-	-	-	-	-	-	-	-
0.5984	1760	0.1418	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6018	1770	0.1308	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6052	1780	0.1402	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6086	1790	0.1452	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6120	1800	0.1457	0.0365	0.4243	0.4866	0.4555	-	-	-	-	-	-	-	-	-	-	-
0.6154	1810	0.1386	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6188	1820	0.1436	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6222	1830	0.1351	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6256	1840	0.1426	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6290	1850	0.1398	0.0363	0.4275	0.4710	0.4493	-	-	-	-	-	-	-	-	-	-	-
0.6324	1860	0.1412	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6358	1870	0.1373	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6392	1880	0.1380	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6426	1890	0.1423	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6460	1900	0.1403	0.0359	0.4200	0.4851	0.4526	-	-	-	-	-	-	-	-	-	-	-
0.6494	1910	0.1350	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6528	1920	0.1305	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6562	1930	0.1328	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6596	1940	0.1441	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6630	1950	0.1377	0.0366	0.4188	0.4762	0.4475	-	-	-	-	-	-	-	-	-	-	-
0.6664	1960	0.1332	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6698	1970	0.1359	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6732	1980	0.1320	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6766	1990	0.1341	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6800	2000	0.1365	0.0358	0.4311	0.4796	0.4554	-	-	-	-	-	-	-	-	-	-	-
0.6834	2010	0.1351	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6868	2020	0.1398	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6902	2030	0.1307	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6936	2040	0.1334	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6970	2050	0.1442	0.0356	0.4266	0.4807	0.4537	-	-	-	-	-	-	-	-	-	-	-
0.7004	2060	0.1389	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7038	2070	0.1313	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7072	2080	0.1357	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7106	2090	0.1410	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7140	2100	0.1419	0.0358	0.4309	0.4756	0.4533	-	-	-	-	-	-	-	-	-	-	-
0.7174	2110	0.1393	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7208	2120	0.1411	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7242	2130	0.1313	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7276	2140	0.1348	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7310	2150	0.1406	0.0346	0.4119	0.4853	0.4486	-	-	-	-	-	-	-	-	-	-	-
0.7344	2160	0.1352	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7378	2170	0.1323	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7412	2180	0.1362	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7446	2190	0.1321	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7480	2200	0.1354	0.0340	0.4267	0.4932	0.4599	-	-	-	-	-	-	-	-	-	-	-
0.7514	2210	0.1331	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7548	2220	0.1387	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7582	2230	0.1337	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7616	2240	0.1358	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7650	2250	0.1367	0.0340	0.4339	0.4821	0.4580	-	-	-	-	-	-	-	-	-	-	-
0.7684	2260	0.1373	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7718	2270	0.1359	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7752	2280	0.1387	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7786	2290	0.1354	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7820	2300	0.1324	0.0341	0.4201	0.4926	0.4563	-	-	-	-	-	-	-	-	-	-	-
0.7854	2310	0.1294	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7888	2320	0.1394	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7922	2330	0.1316	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7956	2340	0.1301	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7990	2350	0.1290	0.0342	0.4199	0.4884	0.4541	-	-	-	-	-	-	-	-	-	-	-
0.8024	2360	0.1329	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8058	2370	0.1322	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8092	2380	0.1367	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8126	2390	0.1315	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8160	2400	0.1304	0.0337	0.4277	0.4953	0.4615	-	-	-	-	-	-	-	-	-	-	-
0.8194	2410	0.1337	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8228	2420	0.1422	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8262	2430	0.1409	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8296	2440	0.1367	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8330	2450	0.1329	0.0338	0.4166	0.4967	0.4567	-	-	-	-	-	-	-	-	-	-	-
0.8365	2460	0.1367	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8399	2470	0.1326	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8433	2480	0.1411	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8467	2490	0.1247	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8501	2500	0.1252	0.0338	0.4113	0.4933	0.4523	-	-	-	-	-	-	-	-	-	-	-
0.8535	2510	0.1224	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8569	2520	0.1399	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8603	2530	0.1336	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8637	2540	0.1326	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8671	2550	0.1318	0.0335	0.4110	0.4805	0.4457	-	-	-	-	-	-	-	-	-	-	-
0.8705	2560	0.1424	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8739	2570	0.1419	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8773	2580	0.1283	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8807	2590	0.1314	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8841	2600	0.1331	0.0334	0.4216	0.4720	0.4468	-	-	-	-	-	-	-	-	-	-	-
0.8875	2610	0.1348	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8909	2620	0.1269	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8943	2630	0.1385	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8977	2640	0.1272	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9011	2650	0.1358	0.0332	0.4254	0.4727	0.4491	-	-	-	-	-	-	-	-	-	-	-
0.9045	2660	0.1298	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9079	2670	0.1406	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9113	2680	0.1329	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9147	2690	0.1332	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9181	2700	0.1333	0.0330	0.4274	0.4730	0.4502	-	-	-	-	-	-	-	-	-	-	-
0.9215	2710	0.1355	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9249	2720	0.1289	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9283	2730	0.1321	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9317	2740	0.1282	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9351	2750	0.1285	0.0329	0.4265	0.4737	0.4501	-	-	-	-	-	-	-	-	-	-	-
0.9385	2760	0.1236	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9419	2770	0.1374	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9453	2780	0.1336	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9487	2790	0.1308	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9521	2800	0.1265	0.0331	0.4340	0.4735	0.4537	-	-	-	-	-	-	-	-	-	-	-
0.9555	2810	0.1350	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9589	2820	0.1329	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9623	2830	0.1344	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9657	2840	0.1288	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9691	2850	0.1365	0.0329	0.4297	0.4738	0.4518	-	-	-	-	-	-	-	-	-	-	-
0.9725	2860	0.1299	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9759	2870	0.1302	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9793	2880	0.1326	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9827	2890	0.1256	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9861	2900	0.1348	0.0329	0.4284	0.4736	0.4510	-	-	-	-	-	-	-	-	-	-	-
0.9895	2910	0.1331	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9929	2920	0.1341	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9963	2930	0.1303	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9997	2940	0.1348	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
-1	-1	-	-	0.4279	0.4732	0.4277	0.2902	0.3861	0.6448	0.3110	0.4968	0.1907	0.9313	0.1917	0.3848	0.4055	0.4267

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.3.0
Transformers: 5.0.0
PyTorch: 2.10.0+cu128
Accelerate: 1.13.0
Datasets: 4.0.0
Tokenizers: 0.22.2

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",
}

CachedMultipleNegativesRankingLoss

bibtex

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

	question	answer
type	string	string
details	min: 8 tokens mean: 12.11 tokens max: 21 tokens	min: 17 tokens mean: 58.29 tokens max: 118 tokens

much1na/ettin-encoder-17m-gooaq-v2

SentenceTransformer based on much1na/ettin-encoder-17m-gooaq

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Nano BEIR

Nano BEIR

Nano BEIR

Training Details

Training Dataset

gooaq

Evaluation Dataset

gooaq

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

CachedMultipleNegativesRankingLoss

much1na/
ettin-encoder-17m-gooaq-v2