---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:12753278
- loss:MarginMSELoss
base_model: PaDaS-Lab/xlm-roberta-base-msmarco
widget:
- source_sentence: De combien de cm dois-je faire dépasser mon appui de fenêtre par
rapport à mon mur ?
sentences:
- La profondeur idéale dépend de la configuration de l’endroit où vous l’installez
et de vos goûts personnels. Si le radiateur doit être recouvert, un appui de fenêtre
plus profond est nécessaire, tandis qu'un rebord plus étroit convient parfaitement
à un style minimaliste. 5 centimètres constituent une excellente base et sont
suffisants dans la plupart des cas. Si la profondeur est plus importante, assurez-vous
qu'au moins la moitié du rebord de la fenêtre repose sur le mur afin d'offrir
une stabilité suffisante et de résister aux contraintes de la vie quotidienne.
En savoir plus sur les tailles disponibles.
- Pour les fenêtres sans jingot, il est obligatoire d'installer une bible qui couvre
toute l'épaisseur des murs. Un écart de 10 mm rectiligne et 15 mm rectiligne doit
être respecté entre le nez de la bavette et le mur. .
- För att registrera ett spelkonto och betta online måste du vara över 18 år i Sverige.
- source_sentence: Hvor kendt er Paulo Coelho som forfatter?
sentences:
- Paulo Coelho er en af verdens mest læste forfattere. Han er mest kendt for sin
bog “Alkymisten”, som er blevet solgt i over 65 millioner eksemplarer og oversat
til 71 forskellige sprog. Hans internationale succes har gjort ham til en af vor
tids mest indflydelsesrige personer.
- Sie spielen auf einem Raster von 5 Walzen und drei Reihen mit 25 Gewinnlinien.
Beim Blood Suckers Spiel gewinnen Sie von links nach rechts.
- Nogle af forfatterens andre populære bøger inkluderer “Broen til det andet menneske”
fra 2016, “Robuste børn” fra 2017 og “Opdragelse til livsmod og bæredygtighed”
fra 2020. Disse bøger udforsker emner som medmenneskelighed, børneopdragelse og
hvordan man kan skabe robuste og velfungerende børn.
- source_sentence: Condominio minimo di due unità di proprietà della stessa persona.
Una unità è posseduta dal proprietario e una dal locatario in virtù di giusto
contratto di locazione. Possono intervenire sulle parti comuni naturalmente ognuno
per le parti possedute?
sentences:
- Взносы и выплаты средств осуществляются в течение 1 рабочего дня.
- '04/09/2020
Il quesito non si specifica il tipo di agevolazione che si intende utilizzare.
Se ipotizziamo l''utilizzo del Superbonus 110% del DL Rilancio convertito in L.77/2020
la risposta è NO.
Infatti, in merito all''ambito soggettivo di applicazione dell''art.119 del DL
Rilancio, tenuto conto della locuzione utilizzata dal legislatore riferita espressamente
ai «condomini», ai fini dell’applicazione dell’agevolazione l’edificio oggetto
degli interventi deve essere costituito in condominio secondo la disciplina civilistica
prevista (secondo una consolidata giurisprudenza, la nascita del condominio si
determina automaticamente, senza che sia necessaria alcuna deliberazione, nel
momento in cui più soggetti costruiscono su un suolo comune ovvero quando l’unico
proprietario di un edificio ne cede a terzi, piani o porzioni di piano, in proprietà
esclusiva, realizzando l’oggettiva condizione del frazionamento, come chiarito
dalla prassi in materia).
Il locatario non può essere qualificato come condomino non essendo proprietario
(di conseguenza neanche delle parti comuni) e pertanto, nel caso in specie, non
costituendosi il Condominio, si ricade nel caso negativo dell''unico proprietario
richiamato dalla Circolare AdE n.24/E del 08/08/2020 e cioè che il Superbonus
non si applica agli interventi realizzati sulle parti comuni a due o più unità
immobiliari distintamente accatastate di un edificio interamente posseduto da
un unico proprietario o in comproprietà fra più soggetti.'
- "Su Marzo 18, 2013 la Legge sul diritto di Famiglia ha sostituito l'attuale Famiglia\
\ Relations Act, causando notevoli modifiche al diritto di famiglia in British\
\ Columbia. Uno dei maggiori cambiamenti è che di common law le coppie saranno\
\ governati dalla stessa divisione di beni, come le coppie sposate dopo la rottura\
\ della loro relazione. Diritto comune, le coppie, per le finalità di divisione\
\ di beni in il Diritto di Famiglia, Legge, comprende due persone che hanno vissuto\
\ in un matrimonio come rapporto per almeno due anni. La presunzione è che dopo\
\ un rapporto di ripartizione per ogni socio avrà diritto a uno la metà del â\x80\
di famiglia propertyâ\x80\x99 e aumenta di valore di â\x80escluso propertyâ\x80\
\x99. Se si desidera impostare un diverso accordo per la divisione dei beni, quindi\
\ un contratto di convivenza è il modo per farlo. Un contratto di convivenza può\
\ anche includere i dettagli di come ciascuna delle parti contribuirà finanziariamente\
\ durante il rapporto e i termini di supporto sponsale, se presente, il rapporto\
\ di composizione. Ogni contratto di convivenza deve essere specifico per la tua\
\ situazione, quindi, è essenziale rivolgersi ad un avvocato per un consiglio\
\ prima di stesura e stipula di eventuali accordi."
- source_sentence: क्या लॉजिकल रीजनिंग यूजीसी नेट परीक्षा का हिस्सा है?
sentences:
- यूजीसी नेट की परीक्षा साल में दो बार आयोजित की जाती है। प्रथम परीक्षा जून में
और द्वितीय परीक्षा दिसंबर महीने में नेशनल टेस्टिंग एजेंसी द्वारा आयोजित की जाती
है।
- जबकि मौलिक विश्लेषण किसी परिसंपत्ति के आंतरिक मूल्य का आकलन करता है, तकनीकी विश्लेषण
पूरी तरह से परिसंपत्ति की कीमत के सांख्यिकीय रुझानों पर केंद्रित होता है। मौलिक
विश्लेषण कंपनी की कमाई, आर्थिक संकेतक और प्रबंधन जैसे कारकों पर विचार करता है,
जबकि तकनीकी विश्लेषण चार्ट और सांख्यिकीय रुझानों का उपयोग करता है।
- हां, लॉजिकल रीजनिंग यूजीसी नेट परीक्षा का हिस्सा है।
- source_sentence: Impact of the physico-chemical properties of fen peat on the metal
accumulation patterns in mires of Latvia
sentences:
- Sì, EpiCURA ha 101 posizioni aperte. Prima di candidarsi presso EpiCURA, è opportuno
fare ricerche sull'azienda e leggere le recensioni dei dipendenti che vi lavorano.
- Wyckoff Method identifies accumulation and distribution phases by analyzing price
and volume patterns. The goal is to locate support and resistance levels and patterns
such as Springs and Upthrusts. Volume trends and moving averages provide confirmation.
- 'Santrauka
Abstract The article presents a study of the physico-chemical properties of fen
peat and their influence on the metal accumulation patterns in three Latvian fens:
Svētupes Mire, Elku Mire and Vīķu Mire. Full peat profiles were obtained at all
study sites and analysed with a multi-proxy approach. The content of metals in
fen peat was determined using the atomic absorption spectroscopy (AAS) and normalised
to the concentration of Ti in the studied peat profiles. Both the character of
deposits and agricultural land use in the mire catchment areas were taken into
account and the possible natural and anthropogenic metal supply sources were evaluated.
The content of metals in the studied fen peat significantly varied due to the
heterogeneity of fen environment; however, noticeable similarities were also traced
throughout all study sites. The results indicate an increased amount of transition
metals and Pb in the upper peat layer. This can be explained by a direct impact
from anthropogenic sources (agricultural land use, pollution, etc.). Metal binding
in fen peat profiles is directly related to the alkali and alkaline earth metal
content in peat, as Ca, Mg, Na and K ions are replaced by more tightly bound metal
ions. In raised bogs, in turn, metal binding is associated with the acidic functional
groups common to peat.
Doi 10.5200/baltica.2016.29.03Raktažodžiai fen peat, metals, peat physico-chemical
properties
Pilnas tekstas'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on PaDaS-Lab/xlm-roberta-base-msmarco
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [PaDaS-Lab/xlm-roberta-base-msmarco](https://huggingface.co/PaDaS-Lab/xlm-roberta-base-msmarco). It maps sentences & paragraphs to a 768-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:** [PaDaS-Lab/xlm-roberta-base-msmarco](https://huggingface.co/PaDaS-Lab/xlm-roberta-base-msmarco)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
(1): Pooling({'word_embedding_dimension': 768, '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:
```bash
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 = [
'Impact of the physico-chemical properties of fen peat on the metal accumulation patterns in mires of Latvia',
'Santrauka\nAbstract The article presents a study of the physico-chemical properties of fen peat and their influence on the metal accumulation patterns in three Latvian fens: Svētupes Mire, Elku Mire and Vīķu Mire. Full peat profiles were obtained at all study sites and analysed with a multi-proxy approach. The content of metals in fen peat was determined using the atomic absorption spectroscopy (AAS) and normalised to the concentration of Ti in the studied peat profiles. Both the character of deposits and agricultural land use in the mire catchment areas were taken into account and the possible natural and anthropogenic metal supply sources were evaluated. The content of metals in the studied fen peat significantly varied due to the heterogeneity of fen environment; however, noticeable similarities were also traced throughout all study sites. The results indicate an increased amount of transition metals and Pb in the upper peat layer. This can be explained by a direct impact from anthropogenic sources (agricultural land use, pollution, etc.). Metal binding in fen peat profiles is directly related to the alkali and alkaline earth metal content in peat, as Ca, Mg, Na and K ions are replaced by more tightly bound metal ions. In raised bogs, in turn, metal binding is associated with the acidic functional groups common to peat.\nDoi\xa010.5200/baltica.2016.29.03Raktažodžiai\xa0fen peat, metals, peat physico-chemical properties\nPilnas tekstas',
'Wyckoff Method identifies accumulation and distribution phases by analyzing price and volume patterns. The goal is to locate support and resistance levels and patterns such as Springs and Upthrusts. Volume trends and moving averages provide confirmation.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9981, 0.9960],
# [0.9981, 1.0000, 0.9978],
# [0.9960, 0.9978, 1.0000]])
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 12,753,278 training samples
* Columns: sentence_0, sentence_1, sentence_2, and label
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | sentence_2 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------|
| type | string | string | string | float |
| details | - min: 6 tokens
- mean: 15.49 tokens
- max: 119 tokens
| - min: 9 tokens
- mean: 74.63 tokens
- max: 512 tokens
| - min: 11 tokens
- mean: 107.12 tokens
- max: 512 tokens
| - min: -0.48
- mean: 0.16
- max: 0.82
|
* Samples:
| sentence_0 | sentence_1 | sentence_2 | label |
|:-----------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------|
| Как найти актуальное зеркало Betwinner? | Букмекер старается обеспечить доступ к сайту, поэтому ссылки на зеркала обновляются ежедневно. Чтобы быть в курсе всех новостей, рекомендуется подписаться на почтовую рассылку и соцсети. | Актуальные рабочие зеркала 1win можно найти на официальных страницах букмекерской конторы в социальных сетях или путем обращения в службу поддержки. | -0.253662109375 |
| Jakie są minimalne zakłady w bakaracie? | Minimalny zakład w bakaracie zależy od konkretnej gry, w którą grasz. Mini Baccarat ma zwykle niskie limity zakładów, co czyni go atrakcyjnym dla nowych graczy. Istnieją też wersje gry w bakarata dla high-rollerów, które nakładają wyższy minimalny zakład. | W czasie pisania tego tekstu, Cloudbet miał minimalny zakład w wysokości 0,01 BTC. Cloudbet ma jedne z najwyższych dostępnych maksymalnych zakładów, ale zazwyczaj różnią się one w zależności od płynności na dane zdarzenie. Ogólnie rzecz biorąc, im bliższe zdarzeniu zakłady, tym większa płynność i tym wyższe stawki można postawić. | 0.3369140625 |
| Come scegliere il massimale assicurazione professionale medici? | Per il momento, non sono ancora entrate in vigore sul massimale minimo per le polizze rc professionale medici. Teniamo conto però di una cosa: se si lavora (e si è lavorato nei dieci anni precedenti) esclusivamente come dipendenti o specializzandi presso l’SSN, dobbiamo sapere che la rivalsa massima dell’SSN sarà plafonata al triplo del reddito annuo lordo del medico.
Se invece si lavora in libera professione, non c’è alcun limite. Consigliamo comunque di scegliere massimali non inferiori al milione di euro. | Potete stipulare una nuova assicurazione di base entro il 31 dicembre, che inizierà a decorrere dal 1 gennaio dell’anno successivo. A tal fine, però, dovete aver disdetto la precedente assicurazione di base entro i termini previsti.
In linea di massima è possibile disdire l’assicurazione di base inviando una lettera di disdetta firmata per posta, via e-mail o via fax. Alla KPT potete comunicare la disdetta anche nel portale clienti KPTnet.
Attenzione: Per la disdetta non fa fede il timbro postale, bensì la data in cui l’assicuratore riceve la disdetta. Il termine di disdetta è rispettato se l’assicuratore riceve la disdetta l’ultimo giorno lavorativo del termine legale durante i normali orari di ufficio. Un invio raccomandato recapitato in una casella postale può essere considerato notificato solo nel momento in cui è ritirato alla Posta. Poiché non si possono escludere ritardi, vi consigliamo di inviare la disdetta per posta, mediante lettera raccomandata, entro la metà di novembre.
C... | 0.09228515625 |
* Loss: [MarginMSELoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#marginmseloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 1
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `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`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `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
- `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`: 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
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}
Click to expand
| Epoch | Step | Training Loss |
|:------:|:------:|:-------------:|
| 0.0025 | 500 | 4.076 |
| 0.0050 | 1000 | 0.0295 |
| 0.0075 | 1500 | 0.0259 |
| 0.0100 | 2000 | 0.0242 |
| 0.0125 | 2500 | 0.0226 |
| 0.0151 | 3000 | 0.022 |
| 0.0176 | 3500 | 0.0221 |
| 0.0201 | 4000 | 0.0211 |
| 0.0226 | 4500 | 0.0208 |
| 0.0251 | 5000 | 0.0204 |
| 0.0276 | 5500 | 0.0202 |
| 0.0301 | 6000 | 0.0197 |
| 0.0326 | 6500 | 0.0196 |
| 0.0351 | 7000 | 0.0194 |
| 0.0376 | 7500 | 0.0193 |
| 0.0401 | 8000 | 0.0191 |
| 0.0427 | 8500 | 0.0195 |
| 0.0452 | 9000 | 0.02 |
| 0.0477 | 9500 | 0.0189 |
| 0.0502 | 10000 | 0.0184 |
| 0.0527 | 10500 | 0.0185 |
| 0.0552 | 11000 | 0.0183 |
| 0.0577 | 11500 | 0.0191 |
| 0.0602 | 12000 | 0.018 |
| 0.0627 | 12500 | 0.0177 |
| 0.0652 | 13000 | 0.0177 |
| 0.0677 | 13500 | 0.0175 |
| 0.0703 | 14000 | 0.0174 |
| 0.0728 | 14500 | 0.0172 |
| 0.0753 | 15000 | 0.0175 |
| 0.0778 | 15500 | 0.0171 |
| 0.0803 | 16000 | 0.0174 |
| 0.0828 | 16500 | 0.0175 |
| 0.0853 | 17000 | 0.0169 |
| 0.0878 | 17500 | 0.0168 |
| 0.0903 | 18000 | 0.0167 |
| 0.0928 | 18500 | 0.0171 |
| 0.0953 | 19000 | 0.0169 |
| 0.0979 | 19500 | 0.0167 |
| 0.1004 | 20000 | 0.0163 |
| 0.1029 | 20500 | 0.0164 |
| 0.1054 | 21000 | 0.0168 |
| 0.1079 | 21500 | 0.0163 |
| 0.1104 | 22000 | 0.0167 |
| 0.1129 | 22500 | 0.0162 |
| 0.1154 | 23000 | 0.0163 |
| 0.1179 | 23500 | 0.0159 |
| 0.1204 | 24000 | 0.0163 |
| 0.1229 | 24500 | 0.0159 |
| 0.1255 | 25000 | 0.0161 |
| 0.1280 | 25500 | 0.0161 |
| 0.1305 | 26000 | 0.0159 |
| 0.1330 | 26500 | 0.0159 |
| 0.1355 | 27000 | 0.0159 |
| 0.1380 | 27500 | 0.0158 |
| 0.1405 | 28000 | 0.0157 |
| 0.1430 | 28500 | 0.0157 |
| 0.1455 | 29000 | 0.0156 |
| 0.1480 | 29500 | 0.0172 |
| 0.1505 | 30000 | 0.0155 |
| 0.1531 | 30500 | 0.0153 |
| 0.1556 | 31000 | 0.0152 |
| 0.1581 | 31500 | 0.0154 |
| 0.1606 | 32000 | 0.0153 |
| 0.1631 | 32500 | 0.0153 |
| 0.1656 | 33000 | 0.0153 |
| 0.1681 | 33500 | 0.0153 |
| 0.1706 | 34000 | 0.0151 |
| 0.1731 | 34500 | 0.015 |
| 0.1756 | 35000 | 0.0148 |
| 0.1782 | 35500 | 0.015 |
| 0.1807 | 36000 | 0.0148 |
| 0.1832 | 36500 | 0.0149 |
| 0.1857 | 37000 | 0.0147 |
| 0.1882 | 37500 | 0.0145 |
| 0.1907 | 38000 | 0.0145 |
| 0.1932 | 38500 | 0.0147 |
| 0.1957 | 39000 | 0.0149 |
| 0.1982 | 39500 | 0.0145 |
| 0.2007 | 40000 | 0.0145 |
| 0.2032 | 40500 | 0.0147 |
| 0.2058 | 41000 | 0.0147 |
| 0.2083 | 41500 | 0.0147 |
| 0.2108 | 42000 | 0.0145 |
| 0.2133 | 42500 | 0.0144 |
| 0.2158 | 43000 | 0.0147 |
| 0.2183 | 43500 | 0.0145 |
| 0.2208 | 44000 | 0.0147 |
| 0.2233 | 44500 | 0.0142 |
| 0.2258 | 45000 | 0.0145 |
| 0.2283 | 45500 | 0.0141 |
| 0.2308 | 46000 | 0.0143 |
| 0.2334 | 46500 | 0.0143 |
| 0.2359 | 47000 | 0.0141 |
| 0.2384 | 47500 | 0.0145 |
| 0.2409 | 48000 | 0.0142 |
| 0.2434 | 48500 | 0.0141 |
| 0.2459 | 49000 | 0.0142 |
| 0.2484 | 49500 | 0.0139 |
| 0.2509 | 50000 | 0.0141 |
| 0.2534 | 50500 | 0.0139 |
| 0.2559 | 51000 | 0.014 |
| 0.2584 | 51500 | 0.0139 |
| 0.2610 | 52000 | 0.014 |
| 0.2635 | 52500 | 0.0142 |
| 0.2660 | 53000 | 0.014 |
| 0.2685 | 53500 | 0.0138 |
| 0.2710 | 54000 | 0.0136 |
| 0.2735 | 54500 | 0.0138 |
| 0.2760 | 55000 | 0.0138 |
| 0.2785 | 55500 | 0.0137 |
| 0.2810 | 56000 | 0.0136 |
| 0.2835 | 56500 | 0.0138 |
| 0.2860 | 57000 | 0.0135 |
| 0.2886 | 57500 | 0.0135 |
| 0.2911 | 58000 | 0.0137 |
| 0.2936 | 58500 | 0.0136 |
| 0.2961 | 59000 | 0.0135 |
| 0.2986 | 59500 | 0.0143 |
| 0.3011 | 60000 | 0.0134 |
| 0.3036 | 60500 | 0.0135 |
| 0.3061 | 61000 | 0.0136 |
| 0.3086 | 61500 | 0.0134 |
| 0.3111 | 62000 | 0.0134 |
| 0.3136 | 62500 | 0.0132 |
| 0.3162 | 63000 | 0.0133 |
| 0.3187 | 63500 | 0.0133 |
| 0.3212 | 64000 | 0.0135 |
| 0.3237 | 64500 | 0.0133 |
| 0.3262 | 65000 | 0.0133 |
| 0.3287 | 65500 | 0.0134 |
| 0.3312 | 66000 | 0.0133 |
| 0.3337 | 66500 | 0.0132 |
| 0.3362 | 67000 | 0.0133 |
| 0.3387 | 67500 | 0.0133 |
| 0.3412 | 68000 | 0.0132 |
| 0.3438 | 68500 | 0.0131 |
| 0.3463 | 69000 | 0.0132 |
| 0.3488 | 69500 | 0.0131 |
| 0.3513 | 70000 | 0.013 |
| 0.3538 | 70500 | 0.0129 |
| 0.3563 | 71000 | 0.0127 |
| 0.3588 | 71500 | 0.0131 |
| 0.3613 | 72000 | 0.0129 |
| 0.3638 | 72500 | 0.0128 |
| 0.3663 | 73000 | 0.0129 |
| 0.3688 | 73500 | 0.0128 |
| 0.3714 | 74000 | 0.0128 |
| 0.3739 | 74500 | 0.0131 |
| 0.3764 | 75000 | 0.013 |
| 0.3789 | 75500 | 0.0127 |
| 0.3814 | 76000 | 0.0128 |
| 0.3839 | 76500 | 0.0127 |
| 0.3864 | 77000 | 0.0128 |
| 0.3889 | 77500 | 0.0129 |
| 0.3914 | 78000 | 0.0128 |
| 0.3939 | 78500 | 0.0127 |
| 0.3964 | 79000 | 0.0128 |
| 0.3990 | 79500 | 0.0126 |
| 0.4015 | 80000 | 0.0127 |
| 0.4040 | 80500 | 0.0126 |
| 0.4065 | 81000 | 0.0124 |
| 0.4090 | 81500 | 0.0126 |
| 0.4115 | 82000 | 0.0124 |
| 0.4140 | 82500 | 0.0124 |
| 0.4165 | 83000 | 0.0127 |
| 0.4190 | 83500 | 0.0123 |
| 0.4215 | 84000 | 0.0124 |
| 0.4240 | 84500 | 0.0125 |
| 0.4266 | 85000 | 0.0124 |
| 0.4291 | 85500 | 0.0124 |
| 0.4316 | 86000 | 0.0124 |
| 0.4341 | 86500 | 0.0124 |
| 0.4366 | 87000 | 0.0128 |
| 0.4391 | 87500 | 0.0124 |
| 0.4416 | 88000 | 0.0123 |
| 0.4441 | 88500 | 0.0123 |
| 0.4466 | 89000 | 0.0125 |
| 0.4491 | 89500 | 0.0125 |
| 0.4516 | 90000 | 0.0123 |
| 0.4542 | 90500 | 0.0124 |
| 0.4567 | 91000 | 0.0122 |
| 0.4592 | 91500 | 0.0122 |
| 0.4617 | 92000 | 0.0124 |
| 0.4642 | 92500 | 0.012 |
| 0.4667 | 93000 | 0.0122 |
| 0.4692 | 93500 | 0.0121 |
| 0.4717 | 94000 | 0.0121 |
| 0.4742 | 94500 | 0.0121 |
| 0.4767 | 95000 | 0.0123 |
| 0.4792 | 95500 | 0.0121 |
| 0.4818 | 96000 | 0.0121 |
| 0.4843 | 96500 | 0.0127 |
| 0.4868 | 97000 | 0.012 |
| 0.4893 | 97500 | 0.0122 |
| 0.4918 | 98000 | 0.012 |
| 0.4943 | 98500 | 0.0119 |
| 0.4968 | 99000 | 0.012 |
| 0.4993 | 99500 | 0.0121 |
| 0.5018 | 100000 | 0.012 |
| 0.5043 | 100500 | 0.0119 |
| 0.5069 | 101000 | 0.0121 |
| 0.5094 | 101500 | 0.0123 |
| 0.5119 | 102000 | 0.0117 |
| 0.5144 | 102500 | 0.0121 |
| 0.5169 | 103000 | 0.0118 |
| 0.5194 | 103500 | 0.0118 |
| 0.5219 | 104000 | 0.0118 |
| 0.5244 | 104500 | 0.0119 |
| 0.5269 | 105000 | 0.012 |
| 0.5294 | 105500 | 0.0117 |
| 0.5319 | 106000 | 0.0118 |
| 0.5345 | 106500 | 0.0118 |
| 0.5370 | 107000 | 0.0118 |
| 0.5395 | 107500 | 0.0119 |
| 0.5420 | 108000 | 0.0116 |
| 0.5445 | 108500 | 0.012 |
| 0.5470 | 109000 | 0.0116 |
| 0.5495 | 109500 | 0.0116 |
| 0.5520 | 110000 | 0.0116 |
| 0.5545 | 110500 | 0.0117 |
| 0.5570 | 111000 | 0.0117 |
| 0.5595 | 111500 | 0.0117 |
| 0.5621 | 112000 | 0.0116 |
| 0.5646 | 112500 | 0.0116 |
| 0.5671 | 113000 | 0.0116 |
| 0.5696 | 113500 | 0.0116 |
| 0.5721 | 114000 | 0.0116 |
| 0.5746 | 114500 | 0.012 |
| 0.5771 | 115000 | 0.0119 |
| 0.5796 | 115500 | 0.0115 |
| 0.5821 | 116000 | 0.0116 |
| 0.5846 | 116500 | 0.0115 |
| 0.5871 | 117000 | 0.0116 |
| 0.5897 | 117500 | 0.0116 |
| 0.5922 | 118000 | 0.0115 |
| 0.5947 | 118500 | 0.0116 |
| 0.5972 | 119000 | 0.0115 |
| 0.5997 | 119500 | 0.0116 |
| 0.6022 | 120000 | 0.0114 |
| 0.6047 | 120500 | 0.0115 |
| 0.6072 | 121000 | 0.0115 |
| 0.6097 | 121500 | 0.0114 |
| 0.6122 | 122000 | 0.0115 |
| 0.6147 | 122500 | 0.0114 |
| 0.6173 | 123000 | 0.0113 |
| 0.6198 | 123500 | 0.0112 |
| 0.6223 | 124000 | 0.0114 |
| 0.6248 | 124500 | 0.0113 |
| 0.6273 | 125000 | 0.0112 |
| 0.6298 | 125500 | 0.0115 |
| 0.6323 | 126000 | 0.0112 |
| 0.6348 | 126500 | 0.0112 |
| 0.6373 | 127000 | 0.0113 |
| 0.6398 | 127500 | 0.0113 |
| 0.6423 | 128000 | 0.0113 |
| 0.6449 | 128500 | 0.0112 |
| 0.6474 | 129000 | 0.0111 |
| 0.6499 | 129500 | 0.0114 |
| 0.6524 | 130000 | 0.0111 |
| 0.6549 | 130500 | 0.0111 |
| 0.6574 | 131000 | 0.0112 |
| 0.6599 | 131500 | 0.0111 |
| 0.6624 | 132000 | 0.0113 |
| 0.6649 | 132500 | 0.0112 |
| 0.6674 | 133000 | 0.0112 |
| 0.6699 | 133500 | 0.0111 |
| 0.6725 | 134000 | 0.0111 |
| 0.6750 | 134500 | 0.0111 |
| 0.6775 | 135000 | 0.011 |
| 0.6800 | 135500 | 0.0113 |
| 0.6825 | 136000 | 0.011 |
| 0.6850 | 136500 | 0.011 |
| 0.6875 | 137000 | 0.0111 |
| 0.6900 | 137500 | 0.0111 |
| 0.6925 | 138000 | 0.0112 |
| 0.6950 | 138500 | 0.0112 |
| 0.6975 | 139000 | 0.0109 |
| 0.7001 | 139500 | 0.0112 |
| 0.7026 | 140000 | 0.011 |
| 0.7051 | 140500 | 0.011 |
| 0.7076 | 141000 | 0.0108 |
| 0.7101 | 141500 | 0.0109 |
| 0.7126 | 142000 | 0.0108 |
| 0.7151 | 142500 | 0.0109 |
| 0.7176 | 143000 | 0.0109 |
| 0.7201 | 143500 | 0.0109 |
| 0.7226 | 144000 | 0.0112 |
| 0.7251 | 144500 | 0.011 |
| 0.7277 | 145000 | 0.0108 |
| 0.7302 | 145500 | 0.0109 |
| 0.7327 | 146000 | 0.0111 |
| 0.7352 | 146500 | 0.0109 |
| 0.7377 | 147000 | 0.0109 |
| 0.7402 | 147500 | 0.0108 |
| 0.7427 | 148000 | 0.011 |
| 0.7452 | 148500 | 0.0108 |
| 0.7477 | 149000 | 0.0109 |
| 0.7502 | 149500 | 0.0107 |
| 0.7527 | 150000 | 0.0108 |
| 0.7553 | 150500 | 0.011 |
| 0.7578 | 151000 | 0.0107 |
| 0.7603 | 151500 | 0.0108 |
| 0.7628 | 152000 | 0.0107 |
| 0.7653 | 152500 | 0.0108 |
| 0.7678 | 153000 | 0.011 |
| 0.7703 | 153500 | 0.0108 |
| 0.7728 | 154000 | 0.0108 |
| 0.7753 | 154500 | 0.0108 |
| 0.7778 | 155000 | 0.0106 |
| 0.7803 | 155500 | 0.0107 |
| 0.7829 | 156000 | 0.0107 |
| 0.7854 | 156500 | 0.0107 |
| 0.7879 | 157000 | 0.0107 |
| 0.7904 | 157500 | 0.0106 |
| 0.7929 | 158000 | 0.0107 |
| 0.7954 | 158500 | 0.0107 |
| 0.7979 | 159000 | 0.0107 |
| 0.8004 | 159500 | 0.0107 |
| 0.8029 | 160000 | 0.0105 |
| 0.8054 | 160500 | 0.0106 |
| 0.8079 | 161000 | 0.0106 |
| 0.8105 | 161500 | 0.0108 |
| 0.8130 | 162000 | 0.0107 |
| 0.8155 | 162500 | 0.0106 |
| 0.8180 | 163000 | 0.0107 |
| 0.8205 | 163500 | 0.0106 |
| 0.8230 | 164000 | 0.0107 |
| 0.8255 | 164500 | 0.0105 |
| 0.8280 | 165000 | 0.0107 |
| 0.8305 | 165500 | 0.0107 |
| 0.8330 | 166000 | 0.0107 |
| 0.8355 | 166500 | 0.0105 |
| 0.8381 | 167000 | 0.0106 |
| 0.8406 | 167500 | 0.0105 |
| 0.8431 | 168000 | 0.0106 |
| 0.8456 | 168500 | 0.0105 |
| 0.8481 | 169000 | 0.0105 |
| 0.8506 | 169500 | 0.0106 |
| 0.8531 | 170000 | 0.0105 |
| 0.8556 | 170500 | 0.0105 |
| 0.8581 | 171000 | 0.0105 |
| 0.8606 | 171500 | 0.0106 |
| 0.8632 | 172000 | 0.0106 |
| 0.8657 | 172500 | 0.0104 |
| 0.8682 | 173000 | 0.0106 |
| 0.8707 | 173500 | 0.0105 |
| 0.8732 | 174000 | 0.0105 |
| 0.8757 | 174500 | 0.0104 |
| 0.8782 | 175000 | 0.0104 |
| 0.8807 | 175500 | 0.0106 |
| 0.8832 | 176000 | 0.0106 |
| 0.8857 | 176500 | 0.0103 |
| 0.8882 | 177000 | 0.0104 |
| 0.8908 | 177500 | 0.0104 |
| 0.8933 | 178000 | 0.0105 |
| 0.8958 | 178500 | 0.0105 |
| 0.8983 | 179000 | 0.0102 |
| 0.9008 | 179500 | 0.0105 |
| 0.9033 | 180000 | 0.0104 |
| 0.9058 | 180500 | 0.0104 |
| 0.9083 | 181000 | 0.0103 |
| 0.9108 | 181500 | 0.0104 |
| 0.9133 | 182000 | 0.0104 |
| 0.9158 | 182500 | 0.0103 |
| 0.9184 | 183000 | 0.0104 |
| 0.9209 | 183500 | 0.0104 |
| 0.9234 | 184000 | 0.0103 |
| 0.9259 | 184500 | 0.0105 |
| 0.9284 | 185000 | 0.0103 |
| 0.9309 | 185500 | 0.0103 |
| 0.9334 | 186000 | 0.0106 |
| 0.9359 | 186500 | 0.0103 |
| 0.9384 | 187000 | 0.0108 |
| 0.9409 | 187500 | 0.0103 |
| 0.9434 | 188000 | 0.0103 |
| 0.9460 | 188500 | 0.0103 |
| 0.9485 | 189000 | 0.0105 |
| 0.9510 | 189500 | 0.0104 |
| 0.9535 | 190000 | 0.0102 |
| 0.9560 | 190500 | 0.0102 |
| 0.9585 | 191000 | 0.0103 |
| 0.9610 | 191500 | 0.0101 |
| 0.9635 | 192000 | 0.0103 |
| 0.9660 | 192500 | 0.0105 |
| 0.9685 | 193000 | 0.0102 |
| 0.9710 | 193500 | 0.0102 |
| 0.9736 | 194000 | 0.0103 |
| 0.9761 | 194500 | 0.0102 |
| 0.9786 | 195000 | 0.0102 |
| 0.9811 | 195500 | 0.0102 |
| 0.9836 | 196000 | 0.0104 |
| 0.9861 | 196500 | 0.0103 |
| 0.9886 | 197000 | 0.0103 |
| 0.9911 | 197500 | 0.0102 |
| 0.9936 | 198000 | 0.0103 |
| 0.9961 | 198500 | 0.0101 |
| 0.9986 | 199000 | 0.0102 |