---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:257886
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/LaBSE
widget:
- source_sentence: 'Karwa Chauth is a festival celebrated by Hindu women of Northern
and Western India on the fourth day after Purnima in the month of Kartika.
'
sentences:
- 'तस्याः युग्मभ्रातुः वंशानुगत-राजकुमारस्य जाक् इत्यस्य निमेषद्वयात् प्राक् सा
अजायत।
'
- '"तथापि, Internet Explorer नोपयोक्तव्यम् । यतो हि तत् सम्यक् डिस्प्ले न करोति
।"'
- 'कर्वा-चौथ् इति उत्सवः उत्तर-पश्चिम-भारतस्य हिन्दु-महिलाभिः कार्तिकमासे पूर्णिमायाः
अनन्तरं चतुर्थदिने आचर्यते।
'
- source_sentence: '"""And if any man will hurt them, fire proceedeth out of their
mouth, and devoureth their enemies: and if any man will hurt them, he must in
this manner be killed."""'
sentences:
- '"C तथा C++ उभयोः मध्येऽपि, इदं समानं मार्गं इम्प्लिमेण्ट् कर्तुमनुसरति ।"'
- यदि केचित् तौ हिंसितुं चेष्टन्ते तर्हि तयो र्वदनाभ्याम् अग्नि र्निर्गत्य तयोः
शत्रून् भस्मीकरिष्यति। यः कश्चित् तौ हिंसितुं चेष्टते तेनैवमेव विनष्टव्यं।
- यवक्रीत उवाच नायं शक्यस्त्वया बड़े महानोघस्तपोधन। अशक्याद् विनिवर्तस्व शक्यमर्थं
समारभ॥
- source_sentence: 'It tarnishes in air to produce a whitish oxidized layer on the
surface.
'
sentences:
- उपस्थितानां रत्नानां श्रेष्ठानामर्घहारिणाम्। नादृश्यत परः पारो नापरस्तत्र भारत॥
- 'इदं वायौ कलङ्कितं भवति, येन तले श्वेतवर्णीयं आक्सिडैस्ड्-आस्तरणं निर्मीयते।
'
- आचार्येणाभ्यनुज्ञातश्चतुर्णामेकमाश्रमम्। आविमोक्षाच्छरीरस्य सोऽवतिष्ठेद् यथाविधि॥
- source_sentence: 'If you''re planning to fund part or all of your child''s higher
education, it''s best to start saving early on.
'
sentences:
- समयं वाजिमेधस्य विदित्वा पुरुषर्षभः। यथोक्तो धर्मपुत्रेण प्रव्रजन् स्वपुरी प्रति॥
- 'यदि भवान् भवतः सन्ततेः उच्चशिक्षायाः कृते, आंशिकं वा सम्पूर्णं वा शुल्कं दातुम्
इच्छति तर्हि तदर्थं पूर्वमेव धनसञ्चयस्य आरम्भः क्षेमकरः भवेत्।
'
- '"""तदनन्तरं तेषां सप्तकंसधारिणां सप्तदूतानाम् एक आगत्य मां सम्भाष्यावदत्, अत्रागच्छ,
मेदिन्या नरपतयो यया वेश्यया सार्द्धं व्यभिचारकर्म्म कृतवन्तः,"""'
- source_sentence: In spite of these, Dhananjaya made Drona's son carless by cutting
off the out-stretched bow of his foe with three shafts, killing his driver with
a razor like shaft and making away with his banner with three and his four horses
with four other shafts.
sentences:
- तथापि तं प्रस्फुरदात्तकार्मुकं त्रिभिः शरैर्यन्तृशिरः क्षुरेणा हयांश्चतुर्भिश्च
पुनस्त्रिभिर्ध्वज धनंजयो द्रौणिरथादपातयत्॥
- एकवारं पूरितं चेत् एतां प्रक्रियां undo कर्तुं न शक्नुमः ।
- क्रीडां तथा कूर्दनं विना शिक्षा अपूर्णा अस्ति ।
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- src2trg_accuracy
- trg2src_accuracy
- mean_accuracy
model-index:
- name: SentenceTransformer based on sentence-transformers/LaBSE
results:
- task:
type: translation
name: Translation
dataset:
name: eval en sa
type: eval-en-sa
metrics:
- type: src2trg_accuracy
value: 0.944
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.947
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.9455
name: Mean Accuracy
---
# SentenceTransformer based on sentence-transformers/LaBSE
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). 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:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE)
- **Maximum Sequence Length:** 128 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/UKPLab/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': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(3): Normalize()
)
```
## 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 = [
"In spite of these, Dhananjaya made Drona's son carless by cutting off the out-stretched bow of his foe with three shafts, killing his driver with a razor like shaft and making away with his banner with three and his four horses with four other shafts.",
'तथापि तं प्रस्फुरदात्तकार्मुकं त्रिभिः शरैर्यन्तृशिरः क्षुरेणा हयांश्चतुर्भिश्च पुनस्त्रिभिर्ध्वज धनंजयो द्रौणिरथादपातयत्॥',
'क्रीडां तथा कूर्दनं विना शिक्षा अपूर्णा अस्ति ।',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Evaluation
### Metrics
#### Translation
* Dataset: `eval-en-sa`
* Evaluated with [TranslationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.944 |
| trg2src_accuracy | 0.947 |
| **mean_accuracy** | **0.9455** |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 257,886 training samples
* Columns: sentence_0 and sentence_1
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 6 tokens
- mean: 31.6 tokens
- max: 128 tokens
| - min: 7 tokens
- mean: 40.18 tokens
- max: 128 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------|
| It normally connects to port 80 on a computer.
| इदं सामान्यतः एकस्मिन् सङ्गणके पोर्ट् ८० इत्यनेन सम्पर्कं साधयति।
|
| He who gives to a Brahmana a good bed perfumed with fragrant scents, covered with an excellent sheet, and pillows, gets without any effort on his part a beautiful wife, belonging to a respectable family and of agreeable manners. | सुगन्धचित्रास्तरणोपधानं दद्यान्नरो यः शयनं द्विजाय। रूपान्वितां पक्षवती मनोज्ञां भार्यामयत्नोपगतां लभेत् सः। |
| By mid-1665, with the fortress at Purandar besieged and near capture, Shivaji was forced to come to terms with Jai Singh.
| १६६५ तमवर्षस्य मध्यभागे यावत् पुरन्दरस्थस्य दुर्गस्य परिवेष्टनं कृत्वा, ग्रहणस्य समीपे, शिवाजी जयसिङ्घेन सह सन्धानं कर्तुं बाध्यः अभवत्।
|
* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `num_train_epochs`: 15
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `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`: 15
- `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
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `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`: False
- `hub_always_push`: False
- `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
- `dispatch_batches`: None
- `split_batches`: 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
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
Click to expand
| Epoch | Step | Training Loss | eval-en-sa_mean_accuracy |
|:-------:|:------:|:-------------:|:------------------------:|
| 0.0310 | 500 | 0.4289 | - |
| 0.0620 | 1000 | 0.182 | - |
| 0.0931 | 1500 | 0.1405 | - |
| 0.1241 | 2000 | 0.1097 | - |
| 0.1551 | 2500 | 0.0911 | - |
| 0.1861 | 3000 | 0.0791 | - |
| 0.2171 | 3500 | 0.0725 | - |
| 0.2482 | 4000 | 0.067 | - |
| 0.2792 | 4500 | 0.0594 | - |
| 0.3102 | 5000 | 0.0629 | - |
| 0.3412 | 5500 | 0.0535 | - |
| 0.3723 | 6000 | 0.0512 | - |
| 0.4033 | 6500 | 0.0456 | - |
| 0.4343 | 7000 | 0.0462 | - |
| 0.4653 | 7500 | 0.043 | - |
| 0.4963 | 8000 | 0.0425 | - |
| 0.5274 | 8500 | 0.0412 | - |
| 0.5584 | 9000 | 0.0418 | - |
| 0.5894 | 9500 | 0.0415 | - |
| 0.6204 | 10000 | 0.0409 | - |
| 0.6514 | 10500 | 0.04 | - |
| 0.6825 | 11000 | 0.032 | - |
| 0.7135 | 11500 | 0.0323 | - |
| 0.7445 | 12000 | 0.0325 | - |
| 0.7755 | 12500 | 0.0355 | - |
| 0.8066 | 13000 | 0.0285 | - |
| 0.8376 | 13500 | 0.0281 | - |
| 0.8686 | 14000 | 0.0289 | - |
| 0.8996 | 14500 | 0.033 | - |
| 0.9306 | 15000 | 0.0336 | - |
| 0.9617 | 15500 | 0.0335 | - |
| 0.9927 | 16000 | 0.0278 | - |
| 1.0 | 16118 | - | 0.913 |
| 1.0237 | 16500 | 0.0312 | - |
| 1.0547 | 17000 | 0.0294 | - |
| 1.0857 | 17500 | 0.0288 | - |
| 1.1168 | 18000 | 0.0287 | - |
| 1.1478 | 18500 | 0.0245 | - |
| 1.1788 | 19000 | 0.0243 | - |
| 1.2098 | 19500 | 0.022 | - |
| 1.2408 | 20000 | 0.0266 | - |
| 1.2719 | 20500 | 0.0224 | - |
| 1.3029 | 21000 | 0.0283 | - |
| 1.3339 | 21500 | 0.02 | - |
| 1.3649 | 22000 | 0.0212 | - |
| 1.3960 | 22500 | 0.0197 | - |
| 1.4270 | 23000 | 0.0174 | - |
| 1.4580 | 23500 | 0.0179 | - |
| 1.4890 | 24000 | 0.0187 | - |
| 1.5200 | 24500 | 0.0191 | - |
| 1.5511 | 25000 | 0.0151 | - |
| 1.5821 | 25500 | 0.0161 | - |
| 1.6131 | 26000 | 0.0182 | - |
| 1.6441 | 26500 | 0.0155 | - |
| 1.6751 | 27000 | 0.013 | - |
| 1.7062 | 27500 | 0.0119 | - |
| 1.7372 | 28000 | 0.0119 | - |
| 1.7682 | 28500 | 0.0133 | - |
| 1.7992 | 29000 | 0.0113 | - |
| 1.8303 | 29500 | 0.011 | - |
| 1.8613 | 30000 | 0.0133 | - |
| 1.8923 | 30500 | 0.0114 | - |
| 1.9233 | 31000 | 0.0139 | - |
| 1.9543 | 31500 | 0.0131 | - |
| 1.9854 | 32000 | 0.0115 | - |
| 2.0 | 32236 | - | 0.9345 |
| 2.0164 | 32500 | 0.01 | - |
| 2.0474 | 33000 | 0.01 | - |
| 2.0784 | 33500 | 0.0091 | - |
| 2.1094 | 34000 | 0.0131 | - |
| 2.1405 | 34500 | 0.0096 | - |
| 2.1715 | 35000 | 0.0095 | - |
| 2.2025 | 35500 | 0.0103 | - |
| 2.2335 | 36000 | 0.0101 | - |
| 2.2645 | 36500 | 0.0102 | - |
| 2.2956 | 37000 | 0.0102 | - |
| 2.3266 | 37500 | 0.0085 | - |
| 2.3576 | 38000 | 0.0087 | - |
| 2.3886 | 38500 | 0.0103 | - |
| 2.4197 | 39000 | 0.0058 | - |
| 2.4507 | 39500 | 0.0086 | - |
| 2.4817 | 40000 | 0.0088 | - |
| 2.5127 | 40500 | 0.0088 | - |
| 2.5437 | 41000 | 0.007 | - |
| 2.5748 | 41500 | 0.0082 | - |
| 2.6058 | 42000 | 0.0069 | - |
| 2.6368 | 42500 | 0.0071 | - |
| 2.6678 | 43000 | 0.0058 | - |
| 2.6988 | 43500 | 0.0075 | - |
| 2.7299 | 44000 | 0.0064 | - |
| 2.7609 | 44500 | 0.0053 | - |
| 2.7919 | 45000 | 0.0055 | - |
| 2.8229 | 45500 | 0.0061 | - |
| 2.8540 | 46000 | 0.0059 | - |
| 2.8850 | 46500 | 0.0062 | - |
| 2.9160 | 47000 | 0.0046 | - |
| 2.9470 | 47500 | 0.0064 | - |
| 2.9780 | 48000 | 0.0053 | - |
| 3.0 | 48354 | - | 0.941 |
| 3.0091 | 48500 | 0.0048 | - |
| 3.0401 | 49000 | 0.0059 | - |
| 3.0711 | 49500 | 0.005 | - |
| 3.1021 | 50000 | 0.005 | 0.9415 |
| 3.1331 | 50500 | 0.0046 | - |
| 3.1642 | 51000 | 0.005 | - |
| 3.1952 | 51500 | 0.0051 | - |
| 3.2262 | 52000 | 0.0041 | - |
| 3.2572 | 52500 | 0.0052 | - |
| 3.2882 | 53000 | 0.0052 | - |
| 3.3193 | 53500 | 0.0053 | - |
| 3.3503 | 54000 | 0.0041 | - |
| 3.3813 | 54500 | 0.0042 | - |
| 3.4123 | 55000 | 0.0026 | - |
| 3.4434 | 55500 | 0.0045 | - |
| 3.4744 | 56000 | 0.0045 | - |
| 3.5054 | 56500 | 0.0054 | - |
| 3.5364 | 57000 | 0.0055 | - |
| 3.5674 | 57500 | 0.0046 | - |
| 3.5985 | 58000 | 0.0045 | - |
| 3.6295 | 58500 | 0.0041 | - |
| 3.6605 | 59000 | 0.0037 | - |
| 3.6915 | 59500 | 0.003 | - |
| 3.7225 | 60000 | 0.0039 | - |
| 3.7536 | 60500 | 0.0027 | - |
| 3.7846 | 61000 | 0.0041 | - |
| 3.8156 | 61500 | 0.003 | - |
| 3.8466 | 62000 | 0.0027 | - |
| 3.8777 | 62500 | 0.0039 | - |
| 3.9087 | 63000 | 0.0038 | - |
| 3.9397 | 63500 | 0.0029 | - |
| 3.9707 | 64000 | 0.0037 | - |
| 4.0 | 64472 | - | 0.9365 |
| 4.0017 | 64500 | 0.0023 | - |
| 4.0328 | 65000 | 0.0034 | - |
| 4.0638 | 65500 | 0.0033 | - |
| 4.0948 | 66000 | 0.0033 | - |
| 4.1258 | 66500 | 0.004 | - |
| 4.1568 | 67000 | 0.0026 | - |
| 4.1879 | 67500 | 0.0026 | - |
| 4.2189 | 68000 | 0.0025 | - |
| 4.2499 | 68500 | 0.0037 | - |
| 4.2809 | 69000 | 0.0041 | - |
| 4.3119 | 69500 | 0.0031 | - |
| 4.3430 | 70000 | 0.0025 | - |
| 4.3740 | 70500 | 0.0025 | - |
| 4.4050 | 71000 | 0.0022 | - |
| 4.4360 | 71500 | 0.0016 | - |
| 4.4671 | 72000 | 0.003 | - |
| 4.4981 | 72500 | 0.0029 | - |
| 4.5291 | 73000 | 0.003 | - |
| 4.5601 | 73500 | 0.0025 | - |
| 4.5911 | 74000 | 0.0027 | - |
| 4.6222 | 74500 | 0.0028 | - |
| 4.6532 | 75000 | 0.003 | - |
| 4.6842 | 75500 | 0.002 | - |
| 4.7152 | 76000 | 0.0028 | - |
| 4.7462 | 76500 | 0.0016 | - |
| 4.7773 | 77000 | 0.0022 | - |
| 4.8083 | 77500 | 0.0019 | - |
| 4.8393 | 78000 | 0.0019 | - |
| 4.8703 | 78500 | 0.0026 | - |
| 4.9014 | 79000 | 0.0023 | - |
| 4.9324 | 79500 | 0.0016 | - |
| 4.9634 | 80000 | 0.0019 | - |
| 4.9944 | 80500 | 0.0018 | - |
| 5.0 | 80590 | - | 0.937 |
| 5.0254 | 81000 | 0.0028 | - |
| 5.0565 | 81500 | 0.0019 | - |
| 5.0875 | 82000 | 0.0024 | - |
| 5.1185 | 82500 | 0.0016 | - |
| 5.1495 | 83000 | 0.0015 | - |
| 5.1805 | 83500 | 0.0017 | - |
| 5.2116 | 84000 | 0.0016 | - |
| 5.2426 | 84500 | 0.0026 | - |
| 5.2736 | 85000 | 0.0029 | - |
| 5.3046 | 85500 | 0.0027 | - |
| 5.3356 | 86000 | 0.002 | - |
| 5.3667 | 86500 | 0.002 | - |
| 5.3977 | 87000 | 0.0021 | - |
| 5.4287 | 87500 | 0.0011 | - |
| 5.4597 | 88000 | 0.0016 | - |
| 5.4908 | 88500 | 0.0019 | - |
| 5.5218 | 89000 | 0.0027 | - |
| 5.5528 | 89500 | 0.0012 | - |
| 5.5838 | 90000 | 0.0012 | - |
| 5.6148 | 90500 | 0.0016 | - |
| 5.6459 | 91000 | 0.0019 | - |
| 5.6769 | 91500 | 0.0016 | - |
| 5.7079 | 92000 | 0.0027 | - |
| 5.7389 | 92500 | 0.0013 | - |
| 5.7699 | 93000 | 0.0013 | - |
| 5.8010 | 93500 | 0.0015 | - |
| 5.8320 | 94000 | 0.0016 | - |
| 5.8630 | 94500 | 0.002 | - |
| 5.8940 | 95000 | 0.001 | - |
| 5.9251 | 95500 | 0.0014 | - |
| 5.9561 | 96000 | 0.0021 | - |
| 5.9871 | 96500 | 0.0022 | - |
| 6.0 | 96708 | - | 0.933 |
| 6.0181 | 97000 | 0.0016 | - |
| 6.0491 | 97500 | 0.0015 | - |
| 6.0802 | 98000 | 0.0011 | - |
| 6.1112 | 98500 | 0.0016 | - |
| 6.1422 | 99000 | 0.001 | - |
| 6.1732 | 99500 | 0.0013 | - |
| 6.2042 | 100000 | 0.0015 | 0.9365 |
| 6.2353 | 100500 | 0.0017 | - |
| 6.2663 | 101000 | 0.0015 | - |
| 6.2973 | 101500 | 0.0016 | - |
| 6.3283 | 102000 | 0.001 | - |
| 6.3593 | 102500 | 0.0013 | - |
| 6.3904 | 103000 | 0.0013 | - |
| 6.4214 | 103500 | 0.0011 | - |
| 6.4524 | 104000 | 0.0007 | - |
| 6.4834 | 104500 | 0.0013 | - |
| 6.5145 | 105000 | 0.0011 | - |
| 6.5455 | 105500 | 0.0011 | - |
| 6.5765 | 106000 | 0.0015 | - |
| 6.6075 | 106500 | 0.002 | - |
| 6.6385 | 107000 | 0.0011 | - |
| 6.6696 | 107500 | 0.0013 | - |
| 6.7006 | 108000 | 0.0017 | - |
| 6.7316 | 108500 | 0.0008 | - |
| 6.7626 | 109000 | 0.0011 | - |
| 6.7936 | 109500 | 0.0008 | - |
| 6.8247 | 110000 | 0.0009 | - |
| 6.8557 | 110500 | 0.0014 | - |
| 6.8867 | 111000 | 0.0014 | - |
| 6.9177 | 111500 | 0.0014 | - |
| 6.9488 | 112000 | 0.0014 | - |
| 6.9798 | 112500 | 0.0013 | - |
| 7.0 | 112826 | - | 0.9390 |
| 7.0108 | 113000 | 0.0011 | - |
| 7.0418 | 113500 | 0.0013 | - |
| 7.0728 | 114000 | 0.0012 | - |
| 7.1039 | 114500 | 0.001 | - |
| 7.1349 | 115000 | 0.0016 | - |
| 7.1659 | 115500 | 0.0009 | - |
| 7.1969 | 116000 | 0.0009 | - |
| 7.2279 | 116500 | 0.0007 | - |
| 7.2590 | 117000 | 0.0008 | - |
| 7.2900 | 117500 | 0.0014 | - |
| 7.3210 | 118000 | 0.0012 | - |
| 7.3520 | 118500 | 0.0007 | - |
| 7.3831 | 119000 | 0.001 | - |
| 7.4141 | 119500 | 0.001 | - |
| 7.4451 | 120000 | 0.0007 | - |
| 7.4761 | 120500 | 0.0008 | - |
| 7.5071 | 121000 | 0.0009 | - |
| 7.5382 | 121500 | 0.0009 | - |
| 7.5692 | 122000 | 0.001 | - |
| 7.6002 | 122500 | 0.0009 | - |
| 7.6312 | 123000 | 0.0007 | - |
| 7.6622 | 123500 | 0.0009 | - |
| 7.6933 | 124000 | 0.0007 | - |
| 7.7243 | 124500 | 0.0012 | - |
| 7.7553 | 125000 | 0.001 | - |
| 7.7863 | 125500 | 0.0005 | - |
| 7.8173 | 126000 | 0.0005 | - |
| 7.8484 | 126500 | 0.0008 | - |
| 7.8794 | 127000 | 0.0014 | - |
| 7.9104 | 127500 | 0.0014 | - |
| 7.9414 | 128000 | 0.0009 | - |
| 7.9725 | 128500 | 0.0008 | - |
| 8.0 | 128944 | - | 0.94 |
| 8.0035 | 129000 | 0.0013 | - |
| 8.0345 | 129500 | 0.0007 | - |
| 8.0655 | 130000 | 0.0007 | - |
| 8.0965 | 130500 | 0.0008 | - |
| 8.1276 | 131000 | 0.0009 | - |
| 8.1586 | 131500 | 0.0009 | - |
| 8.1896 | 132000 | 0.0007 | - |
| 8.2206 | 132500 | 0.0008 | - |
| 8.2516 | 133000 | 0.0008 | - |
| 8.2827 | 133500 | 0.0006 | - |
| 8.3137 | 134000 | 0.0008 | - |
| 8.3447 | 134500 | 0.001 | - |
| 8.3757 | 135000 | 0.0006 | - |
| 8.4068 | 135500 | 0.0007 | - |
| 8.4378 | 136000 | 0.0007 | - |
| 8.4688 | 136500 | 0.0009 | - |
| 8.4998 | 137000 | 0.0008 | - |
| 8.5308 | 137500 | 0.0006 | - |
| 8.5619 | 138000 | 0.0008 | - |
| 8.5929 | 138500 | 0.0007 | - |
| 8.6239 | 139000 | 0.0008 | - |
| 8.6549 | 139500 | 0.0006 | - |
| 8.6859 | 140000 | 0.0005 | - |
| 8.7170 | 140500 | 0.0006 | - |
| 8.7480 | 141000 | 0.0006 | - |
| 8.7790 | 141500 | 0.0006 | - |
| 8.8100 | 142000 | 0.0005 | - |
| 8.8410 | 142500 | 0.0006 | - |
| 8.8721 | 143000 | 0.0005 | - |
| 8.9031 | 143500 | 0.0006 | - |
| 8.9341 | 144000 | 0.0009 | - |
| 8.9651 | 144500 | 0.0007 | - |
| 8.9962 | 145000 | 0.0007 | - |
| 9.0 | 145062 | - | 0.938 |
| 9.0272 | 145500 | 0.0007 | - |
| 9.0582 | 146000 | 0.0007 | - |
| 9.0892 | 146500 | 0.0007 | - |
| 9.1202 | 147000 | 0.0007 | - |
| 9.1513 | 147500 | 0.0005 | - |
| 9.1823 | 148000 | 0.0005 | - |
| 9.2133 | 148500 | 0.0005 | - |
| 9.2443 | 149000 | 0.0007 | - |
| 9.2753 | 149500 | 0.0006 | - |
| 9.3064 | 150000 | 0.0005 | 0.938 |
| 9.3374 | 150500 | 0.0005 | - |
| 9.3684 | 151000 | 0.0004 | - |
| 9.3994 | 151500 | 0.0007 | - |
| 9.4305 | 152000 | 0.0006 | - |
| 9.4615 | 152500 | 0.0006 | - |
| 9.4925 | 153000 | 0.0012 | - |
| 9.5235 | 153500 | 0.0015 | - |
| 9.5545 | 154000 | 0.0006 | - |
| 9.5856 | 154500 | 0.0004 | - |
| 9.6166 | 155000 | 0.0004 | - |
| 9.6476 | 155500 | 0.0007 | - |
| 9.6786 | 156000 | 0.0005 | - |
| 9.7096 | 156500 | 0.0006 | - |
| 9.7407 | 157000 | 0.0004 | - |
| 9.7717 | 157500 | 0.0004 | - |
| 9.8027 | 158000 | 0.0006 | - |
| 9.8337 | 158500 | 0.0004 | - |
| 9.8647 | 159000 | 0.0005 | - |
| 9.8958 | 159500 | 0.0005 | - |
| 9.9268 | 160000 | 0.0004 | - |
| 9.9578 | 160500 | 0.0007 | - |
| 9.9888 | 161000 | 0.0008 | - |
| 10.0 | 161180 | - | 0.9405 |
| 10.0199 | 161500 | 0.0009 | - |
| 10.0509 | 162000 | 0.0007 | - |
| 10.0819 | 162500 | 0.0007 | - |
| 10.1129 | 163000 | 0.0007 | - |
| 10.1439 | 163500 | 0.0005 | - |
| 10.1750 | 164000 | 0.0005 | - |
| 10.2060 | 164500 | 0.0004 | - |
| 10.2370 | 165000 | 0.0006 | - |
| 10.2680 | 165500 | 0.0006 | - |
| 10.2990 | 166000 | 0.0005 | - |
| 10.3301 | 166500 | 0.0005 | - |
| 10.3611 | 167000 | 0.0006 | - |
| 10.3921 | 167500 | 0.0006 | - |
| 10.4231 | 168000 | 0.0003 | - |
| 10.4542 | 168500 | 0.0005 | - |
| 10.4852 | 169000 | 0.001 | - |
| 10.5162 | 169500 | 0.0007 | - |
| 10.5472 | 170000 | 0.0003 | - |
| 10.5782 | 170500 | 0.0005 | - |
| 10.6093 | 171000 | 0.0003 | - |
| 10.6403 | 171500 | 0.0004 | - |
| 10.6713 | 172000 | 0.0006 | - |
| 10.7023 | 172500 | 0.0006 | - |
| 10.7333 | 173000 | 0.0005 | - |
| 10.7644 | 173500 | 0.0004 | - |
| 10.7954 | 174000 | 0.0003 | - |
| 10.8264 | 174500 | 0.0007 | - |
| 10.8574 | 175000 | 0.0005 | - |
| 10.8884 | 175500 | 0.0003 | - |
| 10.9195 | 176000 | 0.0006 | - |
| 10.9505 | 176500 | 0.001 | - |
| 10.9815 | 177000 | 0.0007 | - |
| 11.0 | 177298 | - | 0.9345 |
| 11.0125 | 177500 | 0.0003 | - |
| 11.0436 | 178000 | 0.0003 | - |
| 11.0746 | 178500 | 0.0005 | - |
| 11.1056 | 179000 | 0.0005 | - |
| 11.1366 | 179500 | 0.0007 | - |
| 11.1676 | 180000 | 0.0008 | - |
| 11.1987 | 180500 | 0.0004 | - |
| 11.2297 | 181000 | 0.0006 | - |
| 11.2607 | 181500 | 0.0006 | - |
| 11.2917 | 182000 | 0.0009 | - |
| 11.3227 | 182500 | 0.0005 | - |
| 11.3538 | 183000 | 0.0004 | - |
| 11.3848 | 183500 | 0.0004 | - |
| 11.4158 | 184000 | 0.0005 | - |
| 11.4468 | 184500 | 0.0003 | - |
| 11.4779 | 185000 | 0.0002 | - |
| 11.5089 | 185500 | 0.0003 | - |
| 11.5399 | 186000 | 0.0007 | - |
| 11.5709 | 186500 | 0.0003 | - |
| 11.6019 | 187000 | 0.0003 | - |
| 11.6330 | 187500 | 0.0004 | - |
| 11.6640 | 188000 | 0.0007 | - |
| 11.6950 | 188500 | 0.0003 | - |
| 11.7260 | 189000 | 0.0003 | - |
| 11.7570 | 189500 | 0.0004 | - |
| 11.7881 | 190000 | 0.0004 | - |
| 11.8191 | 190500 | 0.0003 | - |
| 11.8501 | 191000 | 0.0003 | - |
| 11.8811 | 191500 | 0.0003 | - |
| 11.9121 | 192000 | 0.0002 | - |
| 11.9432 | 192500 | 0.0008 | - |
| 11.9742 | 193000 | 0.0004 | - |
| 12.0 | 193416 | - | 0.944 |
| 12.0052 | 193500 | 0.0005 | - |
| 12.0362 | 194000 | 0.0002 | - |
| 12.0673 | 194500 | 0.0003 | - |
| 12.0983 | 195000 | 0.0004 | - |
| 12.1293 | 195500 | 0.0005 | - |
| 12.1603 | 196000 | 0.0004 | - |
| 12.1913 | 196500 | 0.0002 | - |
| 12.2224 | 197000 | 0.0002 | - |
| 12.2534 | 197500 | 0.0003 | - |
| 12.2844 | 198000 | 0.0003 | - |
| 12.3154 | 198500 | 0.0005 | - |
| 12.3464 | 199000 | 0.0004 | - |
| 12.3775 | 199500 | 0.0004 | - |
| 12.4085 | 200000 | 0.0003 | 0.9435 |
| 12.4395 | 200500 | 0.0003 | - |
| 12.4705 | 201000 | 0.0004 | - |
| 12.5016 | 201500 | 0.0009 | - |
| 12.5326 | 202000 | 0.0005 | - |
| 12.5636 | 202500 | 0.0003 | - |
| 12.5946 | 203000 | 0.0003 | - |
| 12.6256 | 203500 | 0.0002 | - |
| 12.6567 | 204000 | 0.0003 | - |
| 12.6877 | 204500 | 0.0002 | - |
| 12.7187 | 205000 | 0.0005 | - |
| 12.7497 | 205500 | 0.0003 | - |
| 12.7807 | 206000 | 0.0004 | - |
| 12.8118 | 206500 | 0.0003 | - |
| 12.8428 | 207000 | 0.0003 | - |
| 12.8738 | 207500 | 0.0003 | - |
| 12.9048 | 208000 | 0.0003 | - |
| 12.9358 | 208500 | 0.0006 | - |
| 12.9669 | 209000 | 0.0004 | - |
| 12.9979 | 209500 | 0.0004 | - |
| 13.0 | 209534 | - | 0.9455 |