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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:878004
- loss:MSELoss
widget:
- source_sentence: Finally all melt into light and dissolve into me
sentences:
- '- - གཡུང་དྲུང་འཇིགས་མེད།'
- མཐར་ནི་འོད་ཞུ་རང་ལ་ཐིམ།།
- >-
དེ་ཤེས་རབ་ཀྱི་ཕ་རོལ་ཏུ་ཕྱིན་པ་ལ་སྤྱོད་པའི་ཚེ།
རྣམ་པ་ཐམས་ཅད་མཁྱེན་པ་ཉིད་དང་ལྡན་པའི་ཡིད་ལ་བྱ་བ་མེད་པར།
གཟུགས་འདུས་བྱས་སྟོང་པ་ཞེས་བྱ་བར་ཡིད་ལ་བྱེད་དེ།
དམིགས་པའི་ཚུལ་གྱིས་འདུས་བྱས་སྟོང་པ་ཉིད་ཀྱང་དམིགས་ལ།
སྟོང་པ་ཉིད་ཀྱིས་ཀྱང་རློམ་སེམས་སུ་བྱེད་དོ། །
- source_sentence: The pain I feel when betrayed is still so much larger than life.
sentences:
- >-
༢༠༡༠ ཟླ་བ་ ༡༠ ཚེས ༠༢ བོད་ཀྱི་བང་ཆེན། Comments Off on
རྟའུ་བློ་བཟང་དཔལ་ལྡན་བཀའ་ཁྲིའི་འོས་མི་ནས་ཕྱིར་འཐེན།
- ༣ ས་པར་ གས་ ས་ ད་པར་ཤ་ཚ་ད ས་པ་ལས་ཧ་ཅང་ག ས་པར་བྱེད་ ་ ང་།
- ཅེས་གསུངས་པ་འདི་ནི། ཕྱི་ལོ་ ༢༠༡༡ ཟླ་ ༥ ཚེས་ ༡༨ ཉིན་ཤེས་རིག་
- source_sentence: I am confident in my own self.
sentences:
- རྗེས་ སུ་ བདག་ བསྒྲུབ་ ཀྱིས༔
- '"ཁྱི་སྐྱག ཡར་ལོངས། "'
- ང་ཡིད་ཆེས་ཀྱི་བརྟས་སོང རང་ས་རང་གིས་སྲུང་བཞིན
- source_sentence: God it isn't easy.
sentences:
- 7:6 ནོ་ཨ་ལོ་ ༦༠༠ ལོན་སྐབས་ས་གཞིར་ཆུ་ལོག་བྱུང་ངོ་།
- ༤ དངུལ་ཆུ་འདུལ་ཚུལ།
- དཀོན་མཆོག࿒ གསུམ࿒ ག་རེ࿒ ག་རེ࿒ རེད།
- source_sentence: He could do it, so he did.
sentences:
- རེས་བྱེད་ཐུབ་པ་དེ་རེད། འོན་ཀྱང་། ཁོ་མོས་
- >-
ཕྱི་སྟོང་པ་ཉིད་ཡོངས་སུ་དག་པ། ཕྱི་སྟོང་པ་ཉིད་ཡོངས་སུ་དག་པས།
ཤེས་པ་པོ་ཡོངས་སུ་དག་པ་སྟེ། དེ་ལྟར་ན་ཤེས་པ་པོ་ཡོངས་སུ་དག་པ་དང་།
ཕྱི་སྟོང་པ་ཉིད་ཡོངས་སུ་དག་པ་འདི་ལ་གཉིས་སུ་མྱེད་དེ་གཉིས་སུ་བྱར་མྱེད་སོ་སོ་མ་ཡིན་ཐ་མྱི་དད་དོ།
།ཤེས་པ་པོ་ཡོངས་སུ་དག་པས།
- འད་ི བསྐྱར་གསོ་བདྱེ ་དགོས་འདུག ཅེས་
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- negative_mse
model-index:
- name: SentenceTransformer
results:
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: stsb dev
type: stsb-dev
metrics:
- type: negative_mse
value: -0.17373771965503693
name: Negative Mse
license: mit
datasets:
- billingsmoore/Aggregated-bo-en
language:
- bo
- en
base_model:
- sentence-transformers/all-MiniLM-L6-v2
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained on the aggregated-bo-en dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. It is intended primarily for usage with the Tibetan language.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- aggregated-bo-en
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## 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("billingsmoore/minilm-bo")
# Run inference
sentences = [
'He could do it, so he did.',
'རེས་བྱེད་ཐུབ་པ་དེ་རེད། འོན་ཀྱང་། ཁོ་མོས་',
'ཕྱི་སྟོང་པ་ཉིད་ཡོངས་སུ་དག་པ། ཕྱི་སྟོང་པ་ཉིད་ཡོངས་སུ་དག་པས། ཤེས་པ་པོ་ཡོངས་སུ་དག་པ་སྟེ། དེ་ལྟར་ན་ཤེས་པ་པོ་ཡོངས་སུ་དག་པ་དང་། ཕྱི་སྟོང་པ་ཉིད་ཡོངས་སུ་དག་པ་འདི་ལ་གཉིས་སུ་མྱེད་དེ་གཉིས་སུ་བྱར་མྱེད་སོ་སོ་མ་ཡིན་ཐ་མྱི་དད་དོ། །ཤེས་པ་པོ་ཡོངས་སུ་དག་པས།',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Knowledge Distillation
* Dataset: `stsb-dev`
* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:------------|
| **negative_mse** | **-0.1737** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### aggregated-bo-en
* Dataset: aggregated-bo-en
* Size: 878,004 training samples
* Columns: <code>tibetan</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | tibetan | label |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | list |
| details | <ul><li>min: 4 tokens</li><li>mean: 29.06 tokens</li><li>max: 373 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
* Samples:
| tibetan | label |
|:------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------|
| <code>ཀི་ལོ་མི་ཊར་ ༤༧.༣༩</code> | <code>[-0.026894396170973778, 0.07161899656057358, -0.06451261788606644, 0.004668479785323143, -0.13893075287342072, ...]</code> |
| <code>ཅ། ཁྱོད་དང་ང་།</code> | <code>[-0.03711550310254097, 0.04723873734474182, 0.027722617611289024, 0.03208618983626366, 0.0021679026540368795, ...]</code> |
| <code>མཚོན་རྨ་གསོ་བ། དེ་བས་མང་། >></code> | <code>[0.016887372359633446, -0.004544022027403116, -0.000849854841362685, -0.046510301530361176, -0.05679721385240555, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Evaluation Dataset
#### aggregated-bo-en
* Dataset: aggregated-bo-en
* Size: 878,004 evaluation samples
* Columns: <code>english</code>, <code>tibetan</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | english | tibetan | label |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | <ul><li>min: 3 tokens</li><li>mean: 22.2 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 32.42 tokens</li><li>max: 487 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
* Samples:
| english | tibetan | label |
|:-----------------------------------------------------------------------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|
| <code>East TN Children's Hospital.</code> | <code>ཤར་གངས་ཕྲུག་གི་གསས་ཁང་།</code> | <code>[-0.05563941225409508, 0.09337888658046722, 0.01915512979030609, 0.02351493015885353, -0.09008331596851349, ...]</code> |
| <code>In this prayer, often called the "high priestly prayer of</code> | <code>སྡེ་ཚན་འདིའི་ནང་དུ་མང་། " མཁན་ཆེན་ཞི་བ་འཚོ། ཇོ་བོ་རྗེ་དཔལ་ལྡན་ཨ་ཏི་ཤ "</code> | <code>[0.033027056604623795, 0.013109864667057991, -0.051157161593437195, -0.07704736292362213, -0.04368748143315315, ...]</code> |
| <code>Spoilers: Oh, I don't know.</code> | <code>ལ་མེད། ཤེས་ཀྱི་མེད། 아니오, 모르겠습니다.</code> | <code>[0.008215248584747314, -0.02530045434832573, -0.029446149244904518, 0.04361790046095848, 0.05075978860259056, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `learning_rate`: 2e-05
- `num_train_epochs`: 25
- `warmup_ratio`: 0.1
- `save_safetensors`: False
- `auto_find_batch_size`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 25
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: False
- `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`: None
- `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`: True
- `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`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | stsb-dev_negative_mse |
|:------:|:-----:|:-------------:|:---------------:|:---------------------:|
| 0 | 0 | - | - | -7.179603 |
| 0.0051 | 500 | 0.0546 | - | - |
| 0.0101 | 1000 | 0.0348 | - | - |
| 0.0152 | 1500 | 0.0169 | - | - |
| 0.0202 | 2000 | 0.0087 | - | - |
| 0.0253 | 2500 | 0.0055 | - | - |
| 0.0304 | 3000 | 0.0041 | - | - |
| 0.0354 | 3500 | 0.0036 | - | - |
| 0.0405 | 4000 | 0.0033 | - | - |
| 0.0456 | 4500 | 0.003 | - | - |
| 0.0506 | 5000 | 0.0029 | - | - |
| 0.0557 | 5500 | 0.0028 | - | - |
| 0.0607 | 6000 | 0.0027 | - | - |
| 0.0658 | 6500 | 0.0027 | - | - |
| 0.0709 | 7000 | 0.0026 | - | - |
| 0.0759 | 7500 | 0.0025 | - | - |
| 0.0810 | 8000 | 0.0025 | - | - |
| 0.0861 | 8500 | 0.0025 | - | - |
| 0.0911 | 9000 | 0.0025 | - | - |
| 0.0962 | 9500 | 0.0025 | - | - |
| 0.1012 | 10000 | 0.0024 | - | - |
| 0.1063 | 10500 | 0.0024 | - | - |
| 0.1114 | 11000 | 0.0024 | - | - |
| 0.1164 | 11500 | 0.0024 | - | - |
| 0.1215 | 12000 | 0.0024 | - | - |
| 0.1265 | 12500 | 0.0024 | - | - |
| 0.1316 | 13000 | 0.0024 | - | - |
| 0.1367 | 13500 | 0.0024 | - | - |
| 0.1417 | 14000 | 0.0024 | - | - |
| 0.1468 | 14500 | 0.0024 | - | - |
| 0.1519 | 15000 | 0.0024 | - | - |
| 0.1569 | 15500 | 0.0024 | - | - |
| 0.1620 | 16000 | 0.0024 | - | - |
| 0.1670 | 16500 | 0.0024 | - | - |
| 0.1721 | 17000 | 0.0024 | - | - |
| 0.1772 | 17500 | 0.0024 | - | - |
| 0.1822 | 18000 | 0.0024 | - | - |
| 0.1873 | 18500 | 0.0024 | - | - |
| 0.1924 | 19000 | 0.0024 | - | - |
| 0.1974 | 19500 | 0.0024 | - | - |
| 0.2025 | 20000 | 0.0024 | - | - |
| 0.2075 | 20500 | 0.0024 | - | - |
| 0.2126 | 21000 | 0.0024 | - | - |
| 0.2177 | 21500 | 0.0024 | - | - |
| 0.2227 | 22000 | 0.0024 | - | - |
| 0.2278 | 22500 | 0.0024 | - | - |
| 0.2329 | 23000 | 0.0024 | - | - |
| 0.2379 | 23500 | 0.0024 | - | - |
| 0.2430 | 24000 | 0.0023 | - | - |
| 0.2480 | 24500 | 0.0024 | - | - |
| 0.2531 | 25000 | 0.0024 | - | - |
| 0.2582 | 25500 | 0.0023 | - | - |
| 0.2632 | 26000 | 0.0024 | - | - |
| 0.2683 | 26500 | 0.0024 | - | - |
| 0.2733 | 27000 | 0.0023 | - | - |
| 0.2784 | 27500 | 0.0023 | - | - |
| 0.2835 | 28000 | 0.0023 | - | - |
| 0.2885 | 28500 | 0.0023 | - | - |
| 0.2936 | 29000 | 0.0023 | - | - |
| 0.2987 | 29500 | 0.0023 | - | - |
| 0.3037 | 30000 | 0.0023 | - | - |
| 0.3088 | 30500 | 0.0023 | - | - |
| 0.3138 | 31000 | 0.0023 | - | - |
| 0.3189 | 31500 | 0.0023 | - | - |
| 0.3240 | 32000 | 0.0023 | - | - |
| 0.3290 | 32500 | 0.0023 | - | - |
| 0.3341 | 33000 | 0.0023 | - | - |
| 0.3392 | 33500 | 0.0023 | - | - |
| 0.3442 | 34000 | 0.0023 | - | - |
| 0.3493 | 34500 | 0.0023 | - | - |
| 0.3543 | 35000 | 0.0023 | - | - |
| 0.3594 | 35500 | 0.0023 | - | - |
| 0.3645 | 36000 | 0.0023 | - | - |
| 0.3695 | 36500 | 0.0023 | - | - |
| 0.3746 | 37000 | 0.0023 | - | - |
| 0.3796 | 37500 | 0.0023 | - | - |
| 0.3847 | 38000 | 0.0023 | - | - |
| 0.3898 | 38500 | 0.0023 | - | - |
| 0.3948 | 39000 | 0.0023 | - | - |
| 0.3999 | 39500 | 0.0023 | - | - |
| 0.4050 | 40000 | 0.0023 | - | - |
| 0.4100 | 40500 | 0.0023 | - | - |
| 0.4151 | 41000 | 0.0023 | - | - |
| 0.4201 | 41500 | 0.0023 | - | - |
| 0.4252 | 42000 | 0.0023 | - | - |
| 0.4303 | 42500 | 0.0023 | - | - |
| 0.4353 | 43000 | 0.0023 | - | - |
| 0.4404 | 43500 | 0.0023 | - | - |
| 0.4455 | 44000 | 0.0022 | - | - |
| 0.4505 | 44500 | 0.0023 | - | - |
| 0.4556 | 45000 | 0.0023 | - | - |
| 0.4606 | 45500 | 0.0022 | - | - |
| 0.4657 | 46000 | 0.0022 | - | - |
| 0.4708 | 46500 | 0.0022 | - | - |
| 0.4758 | 47000 | 0.0022 | - | - |
| 0.4809 | 47500 | 0.0022 | - | - |
| 0.4859 | 48000 | 0.0022 | - | - |
| 0.4910 | 48500 | 0.0022 | - | - |
| 0.4961 | 49000 | 0.0022 | - | - |
| 0.5011 | 49500 | 0.0022 | - | - |
| 0.5062 | 50000 | 0.0022 | - | - |
| 0.5113 | 50500 | 0.0022 | - | - |
| 0.5163 | 51000 | 0.0022 | - | - |
| 0.5214 | 51500 | 0.0022 | - | - |
| 0.5264 | 52000 | 0.0022 | - | - |
| 0.5315 | 52500 | 0.0022 | - | - |
| 0.5366 | 53000 | 0.0022 | - | - |
| 0.5416 | 53500 | 0.0022 | - | - |
| 0.5467 | 54000 | 0.0022 | - | - |
| 0.5518 | 54500 | 0.0022 | - | - |
| 0.5568 | 55000 | 0.0022 | - | - |
| 0.5619 | 55500 | 0.0022 | - | - |
| 0.5669 | 56000 | 0.0022 | - | - |
| 0.5720 | 56500 | 0.0022 | - | - |
| 0.5771 | 57000 | 0.0022 | - | - |
| 0.5821 | 57500 | 0.0022 | - | - |
| 0.5872 | 58000 | 0.0022 | - | - |
| 0.5922 | 58500 | 0.0022 | - | - |
| 0.5973 | 59000 | 0.0022 | - | - |
| 0.6024 | 59500 | 0.0022 | - | - |
| 0.6074 | 60000 | 0.0022 | - | - |
| 0.6125 | 60500 | 0.0022 | - | - |
| 0.6176 | 61000 | 0.0022 | - | - |
| 0.6226 | 61500 | 0.0022 | - | - |
| 0.6277 | 62000 | 0.0022 | - | - |
| 0.6327 | 62500 | 0.0022 | - | - |
| 0.6378 | 63000 | 0.0022 | - | - |
| 0.6429 | 63500 | 0.0022 | - | - |
| 0.6479 | 64000 | 0.0022 | - | - |
| 0.6530 | 64500 | 0.0022 | - | - |
| 0.6581 | 65000 | 0.0022 | - | - |
| 0.6631 | 65500 | 0.0022 | - | - |
| 0.6682 | 66000 | 0.0022 | - | - |
| 0.6732 | 66500 | 0.0021 | - | - |
| 0.6783 | 67000 | 0.0021 | - | - |
| 0.6834 | 67500 | 0.0021 | - | - |
| 0.6884 | 68000 | 0.0021 | - | - |
| 0.6935 | 68500 | 0.0021 | - | - |
| 0.6986 | 69000 | 0.0021 | - | - |
| 0.7036 | 69500 | 0.0021 | - | - |
| 0.7087 | 70000 | 0.0021 | - | - |
| 0.7137 | 70500 | 0.0021 | - | - |
| 0.7188 | 71000 | 0.0021 | - | - |
| 0.7239 | 71500 | 0.0021 | - | - |
| 0.7289 | 72000 | 0.0021 | - | - |
| 0.7340 | 72500 | 0.0021 | - | - |
| 0.7390 | 73000 | 0.0021 | - | - |
| 0.7441 | 73500 | 0.0021 | - | - |
| 0.7492 | 74000 | 0.0021 | - | - |
| 0.7542 | 74500 | 0.0021 | - | - |
| 0.7593 | 75000 | 0.0021 | - | - |
| 0.7644 | 75500 | 0.0021 | - | - |
| 0.7694 | 76000 | 0.0021 | - | - |
| 0.7745 | 76500 | 0.0021 | - | - |
| 0.7795 | 77000 | 0.0021 | - | - |
| 0.7846 | 77500 | 0.0021 | - | - |
| 0.7897 | 78000 | 0.0021 | - | - |
| 0.7947 | 78500 | 0.0021 | - | - |
| 0.7998 | 79000 | 0.0021 | - | - |
| 0.8049 | 79500 | 0.0021 | - | - |
| 0.8099 | 80000 | 0.0021 | - | - |
| 0.8150 | 80500 | 0.0021 | - | - |
| 0.8200 | 81000 | 0.0021 | - | - |
| 0.8251 | 81500 | 0.0021 | - | - |
| 0.8302 | 82000 | 0.0021 | - | - |
| 0.8352 | 82500 | 0.0021 | - | - |
| 0.8403 | 83000 | 0.0021 | - | - |
| 0.8453 | 83500 | 0.0021 | - | - |
| 0.8504 | 84000 | 0.0021 | - | - |
| 0.8555 | 84500 | 0.0021 | - | - |
| 0.8605 | 85000 | 0.0021 | - | - |
| 0.8656 | 85500 | 0.0021 | - | - |
| 0.8707 | 86000 | 0.0021 | - | - |
| 0.8757 | 86500 | 0.0021 | - | - |
| 0.8808 | 87000 | 0.0021 | - | - |
| 0.8858 | 87500 | 0.0021 | - | - |
| 0.8909 | 88000 | 0.0021 | - | - |
| 0.8960 | 88500 | 0.0021 | - | - |
| 0.9010 | 89000 | 0.0021 | - | - |
| 0.9061 | 89500 | 0.0021 | - | - |
| 0.9112 | 90000 | 0.0021 | - | - |
| 0.9162 | 90500 | 0.002 | - | - |
| 0.9213 | 91000 | 0.0021 | - | - |
| 0.9263 | 91500 | 0.0021 | - | - |
| 0.9314 | 92000 | 0.0021 | - | - |
| 0.9365 | 92500 | 0.0021 | - | - |
| 0.9415 | 93000 | 0.002 | - | - |
| 0.9466 | 93500 | 0.002 | - | - |
| 0.9516 | 94000 | 0.0021 | - | - |
| 0.9567 | 94500 | 0.002 | - | - |
| 0.9618 | 95000 | 0.002 | - | - |
| 0.9668 | 95500 | 0.002 | - | - |
| 0.9719 | 96000 | 0.002 | - | - |
| 0.9770 | 96500 | 0.002 | - | - |
| 0.9820 | 97000 | 0.002 | - | - |
| 0.9871 | 97500 | 0.002 | - | - |
| 0.9921 | 98000 | 0.002 | - | - |
| 0.9972 | 98500 | 0.002 | - | - |
| 1.0 | 98776 | - | 0.0022 | -0.1987867 |
| 1.0023 | 99000 | 0.002 | - | - |
| 0.0051 | 500 | 0.002 | - | - |
| 0.0101 | 1000 | 0.002 | - | - |
| 0.0152 | 1500 | 0.002 | - | - |
| 0.0202 | 2000 | 0.002 | - | - |
| 0.0253 | 2500 | 0.002 | - | - |
| 0.0304 | 3000 | 0.002 | - | - |
| 0.0354 | 3500 | 0.002 | - | - |
| 0.0405 | 4000 | 0.002 | - | - |
| 0.0456 | 4500 | 0.002 | - | - |
| 0.0506 | 5000 | 0.002 | - | - |
| 0.0557 | 5500 | 0.002 | - | - |
| 0.0607 | 6000 | 0.002 | - | - |
| 0.0658 | 6500 | 0.002 | - | - |
| 0.0709 | 7000 | 0.002 | - | - |
| 0.0759 | 7500 | 0.002 | - | - |
| 0.0810 | 8000 | 0.002 | - | - |
| 0.0861 | 8500 | 0.002 | - | - |
| 0.0911 | 9000 | 0.002 | - | - |
| 0.0962 | 9500 | 0.002 | - | - |
| 0.1012 | 10000 | 0.002 | - | - |
| 0.1063 | 10500 | 0.002 | - | - |
| 0.1114 | 11000 | 0.002 | - | - |
| 0.1164 | 11500 | 0.002 | - | - |
| 0.1215 | 12000 | 0.002 | - | - |
| 0.1265 | 12500 | 0.002 | - | - |
| 0.1316 | 13000 | 0.002 | - | - |
| 0.1367 | 13500 | 0.002 | - | - |
| 0.1417 | 14000 | 0.002 | - | - |
| 0.1468 | 14500 | 0.002 | - | - |
| 0.1519 | 15000 | 0.002 | - | - |
| 0.1569 | 15500 | 0.002 | - | - |
| 0.1620 | 16000 | 0.002 | - | - |
| 0.1670 | 16500 | 0.002 | - | - |
| 0.1721 | 17000 | 0.002 | - | - |
| 0.1772 | 17500 | 0.002 | - | - |
| 0.1822 | 18000 | 0.002 | - | - |
| 0.1873 | 18500 | 0.002 | - | - |
| 0.1924 | 19000 | 0.002 | - | - |
| 0.1974 | 19500 | 0.002 | - | - |
| 0.2025 | 20000 | 0.002 | - | - |
| 0.2075 | 20500 | 0.002 | - | - |
| 0.2126 | 21000 | 0.002 | - | - |
| 0.2177 | 21500 | 0.002 | - | - |
| 0.2227 | 22000 | 0.002 | - | - |
| 0.2278 | 22500 | 0.002 | - | - |
| 0.2329 | 23000 | 0.002 | - | - |
| 0.2379 | 23500 | 0.002 | - | - |
| 0.2430 | 24000 | 0.002 | - | - |
| 0.2480 | 24500 | 0.002 | - | - |
| 0.2531 | 25000 | 0.002 | - | - |
| 0.2582 | 25500 | 0.002 | - | - |
| 0.2632 | 26000 | 0.002 | - | - |
| 0.2683 | 26500 | 0.002 | - | - |
| 0.2733 | 27000 | 0.002 | - | - |
| 0.2784 | 27500 | 0.002 | - | - |
| 0.2835 | 28000 | 0.002 | - | - |
| 0.2885 | 28500 | 0.002 | - | - |
| 0.2936 | 29000 | 0.002 | - | - |
| 0.2987 | 29500 | 0.002 | - | - |
| 0.3037 | 30000 | 0.002 | - | - |
| 0.3088 | 30500 | 0.002 | - | - |
| 0.3138 | 31000 | 0.002 | - | - |
| 0.3189 | 31500 | 0.002 | - | - |
| 0.3240 | 32000 | 0.002 | - | - |
| 0.3290 | 32500 | 0.002 | - | - |
| 0.3341 | 33000 | 0.002 | - | - |
| 0.3392 | 33500 | 0.002 | - | - |
| 0.3442 | 34000 | 0.002 | - | - |
| 0.3493 | 34500 | 0.002 | - | - |
| 0.3543 | 35000 | 0.002 | - | - |
| 0.3594 | 35500 | 0.002 | - | - |
| 0.3645 | 36000 | 0.002 | - | - |
| 0.3695 | 36500 | 0.002 | - | - |
| 0.3746 | 37000 | 0.002 | - | - |
| 0.3796 | 37500 | 0.002 | - | - |
| 0.3847 | 38000 | 0.002 | - | - |
| 0.3898 | 38500 | 0.002 | - | - |
| 0.3948 | 39000 | 0.002 | - | - |
| 0.3999 | 39500 | 0.002 | - | - |
| 0.4050 | 40000 | 0.002 | - | - |
| 0.4100 | 40500 | 0.002 | - | - |
| 0.4151 | 41000 | 0.002 | - | - |
| 0.4201 | 41500 | 0.002 | - | - |
| 0.4252 | 42000 | 0.002 | - | - |
| 0.4303 | 42500 | 0.002 | - | - |
| 0.4353 | 43000 | 0.002 | - | - |
| 0.4404 | 43500 | 0.002 | - | - |
| 0.4455 | 44000 | 0.002 | - | - |
| 0.4505 | 44500 | 0.002 | - | - |
| 0.4556 | 45000 | 0.002 | - | - |
| 0.4606 | 45500 | 0.002 | - | - |
| 0.4657 | 46000 | 0.002 | - | - |
| 0.4708 | 46500 | 0.002 | - | - |
| 0.4758 | 47000 | 0.002 | - | - |
| 0.4809 | 47500 | 0.002 | - | - |
| 0.4859 | 48000 | 0.002 | - | - |
| 0.4910 | 48500 | 0.002 | - | - |
| 0.4961 | 49000 | 0.002 | - | - |
| 0.5011 | 49500 | 0.002 | - | - |
| 0.5062 | 50000 | 0.002 | - | - |
| 0.5113 | 50500 | 0.002 | - | - |
| 0.5163 | 51000 | 0.002 | - | - |
| 0.5214 | 51500 | 0.002 | - | - |
| 0.5264 | 52000 | 0.002 | - | - |
| 0.5315 | 52500 | 0.002 | - | - |
| 0.5366 | 53000 | 0.002 | - | - |
| 0.5416 | 53500 | 0.002 | - | - |
| 0.5467 | 54000 | 0.002 | - | - |
| 0.5518 | 54500 | 0.002 | - | - |
| 0.5568 | 55000 | 0.002 | - | - |
| 0.5619 | 55500 | 0.002 | - | - |
| 0.5669 | 56000 | 0.002 | - | - |
| 0.5720 | 56500 | 0.002 | - | - |
| 0.5771 | 57000 | 0.002 | - | - |
| 0.5821 | 57500 | 0.002 | - | - |
| 0.5872 | 58000 | 0.002 | - | - |
| 0.5922 | 58500 | 0.002 | - | - |
| 0.5973 | 59000 | 0.002 | - | - |
| 0.6024 | 59500 | 0.002 | - | - |
| 0.6074 | 60000 | 0.002 | - | - |
| 0.6125 | 60500 | 0.0019 | - | - |
| 0.6176 | 61000 | 0.002 | - | - |
| 0.6226 | 61500 | 0.002 | - | - |
| 0.6277 | 62000 | 0.002 | - | - |
| 0.6327 | 62500 | 0.002 | - | - |
| 0.6378 | 63000 | 0.002 | - | - |
| 0.6429 | 63500 | 0.002 | - | - |
| 0.6479 | 64000 | 0.002 | - | - |
| 0.6530 | 64500 | 0.0019 | - | - |
| 0.6581 | 65000 | 0.0019 | - | - |
| 0.6631 | 65500 | 0.002 | - | - |
| 0.6682 | 66000 | 0.002 | - | - |
| 0.6732 | 66500 | 0.0019 | - | - |
| 0.6783 | 67000 | 0.0019 | - | - |
| 0.6834 | 67500 | 0.0019 | - | - |
| 0.6884 | 68000 | 0.0019 | - | - |
| 0.6935 | 68500 | 0.0019 | - | - |
| 0.6986 | 69000 | 0.002 | - | - |
| 0.7036 | 69500 | 0.0019 | - | - |
| 0.7087 | 70000 | 0.0019 | - | - |
| 0.7137 | 70500 | 0.0019 | - | - |
| 0.7188 | 71000 | 0.0019 | - | - |
| 0.7239 | 71500 | 0.0019 | - | - |
| 0.7289 | 72000 | 0.0019 | - | - |
| 0.7340 | 72500 | 0.0019 | - | - |
| 0.7390 | 73000 | 0.0019 | - | - |
| 0.7441 | 73500 | 0.0019 | - | - |
| 0.7492 | 74000 | 0.0019 | - | - |
| 0.7542 | 74500 | 0.0019 | - | - |
| 0.7593 | 75000 | 0.0019 | - | - |
| 0.7644 | 75500 | 0.0019 | - | - |
| 0.7694 | 76000 | 0.0019 | - | - |
| 0.7745 | 76500 | 0.0019 | - | - |
| 0.7795 | 77000 | 0.0019 | - | - |
| 0.7846 | 77500 | 0.0019 | - | - |
| 0.7897 | 78000 | 0.0019 | - | - |
| 0.7947 | 78500 | 0.0019 | - | - |
| 0.7998 | 79000 | 0.0019 | - | - |
| 0.8049 | 79500 | 0.0019 | - | - |
| 0.8099 | 80000 | 0.0019 | - | - |
| 0.8150 | 80500 | 0.0019 | - | - |
| 0.8200 | 81000 | 0.0019 | - | - |
| 0.8251 | 81500 | 0.0019 | - | - |
| 0.8302 | 82000 | 0.0019 | - | - |
| 0.8352 | 82500 | 0.0019 | - | - |
| 0.8403 | 83000 | 0.0019 | - | - |
| 0.8453 | 83500 | 0.0019 | - | - |
| 0.8504 | 84000 | 0.0019 | - | - |
| 0.8555 | 84500 | 0.0019 | - | - |
| 0.8605 | 85000 | 0.0019 | - | - |
| 0.8656 | 85500 | 0.0019 | - | - |
| 0.8707 | 86000 | 0.0019 | - | - |
| 0.8757 | 86500 | 0.0019 | - | - |
| 0.8808 | 87000 | 0.0019 | - | - |
| 0.8858 | 87500 | 0.0019 | - | - |
| 0.8909 | 88000 | 0.0019 | - | - |
| 0.8960 | 88500 | 0.0019 | - | - |
| 0.9010 | 89000 | 0.0019 | - | - |
| 0.9061 | 89500 | 0.0019 | - | - |
| 0.9112 | 90000 | 0.0019 | - | - |
| 0.9162 | 90500 | 0.0019 | - | - |
| 0.9213 | 91000 | 0.0019 | - | - |
| 0.9263 | 91500 | 0.0019 | - | - |
| 0.9314 | 92000 | 0.0019 | - | - |
| 0.9365 | 92500 | 0.0019 | - | - |
| 0.9415 | 93000 | 0.0019 | - | - |
| 0.9466 | 93500 | 0.0019 | - | - |
| 0.9516 | 94000 | 0.0019 | - | - |
| 0.9567 | 94500 | 0.0019 | - | - |
| 0.9618 | 95000 | 0.0019 | - | - |
| 0.9668 | 95500 | 0.0019 | - | - |
| 0.9719 | 96000 | 0.0019 | - | - |
| 0.9770 | 96500 | 0.0019 | - | - |
| 0.9820 | 97000 | 0.0019 | - | - |
| 0.9871 | 97500 | 0.0019 | - | - |
| 0.9921 | 98000 | 0.0019 | - | - |
| 0.9972 | 98500 | 0.0019 | - | - |
| 1.0 | 98776 | - | 0.0021 | -0.18616606 |
| 1.0023 | 99000 | 0.0019 | - | - |
| 0.0051 | 500 | 0.0019 | - | - |
| 0.0101 | 1000 | 0.0019 | - | - |
| 0.0152 | 1500 | 0.0019 | - | - |
| 0.0202 | 2000 | 0.0019 | - | - |
| 0.0253 | 2500 | 0.0019 | - | - |
| 0.0304 | 3000 | 0.0019 | - | - |
| 0.0354 | 3500 | 0.0019 | - | - |
| 0.0405 | 4000 | 0.0019 | - | - |
| 0.0456 | 4500 | 0.0019 | - | - |
| 0.0506 | 5000 | 0.0019 | - | - |
| 0.0557 | 5500 | 0.0019 | - | - |
| 0.0607 | 6000 | 0.0019 | - | - |
| 0.0658 | 6500 | 0.0019 | - | - |
| 0.0709 | 7000 | 0.0019 | - | - |
| 0.0759 | 7500 | 0.0019 | - | - |
| 0.0810 | 8000 | 0.0019 | - | - |
| 0.0861 | 8500 | 0.0019 | - | - |
| 0.0911 | 9000 | 0.0019 | - | - |
| 0.0962 | 9500 | 0.0019 | - | - |
| 0.1012 | 10000 | 0.0019 | - | - |
| 0.1063 | 10500 | 0.0019 | - | - |
| 0.1114 | 11000 | 0.0019 | - | - |
| 0.1164 | 11500 | 0.0019 | - | - |
| 0.1215 | 12000 | 0.0019 | - | - |
| 0.1265 | 12500 | 0.0019 | - | - |
| 0.1316 | 13000 | 0.0019 | - | - |
| 0.1367 | 13500 | 0.0019 | - | - |
| 0.1417 | 14000 | 0.0019 | - | - |
| 0.1468 | 14500 | 0.0019 | - | - |
| 0.1519 | 15000 | 0.0019 | - | - |
| 0.1569 | 15500 | 0.0019 | - | - |
| 0.1620 | 16000 | 0.0019 | - | - |
| 0.1670 | 16500 | 0.0019 | - | - |
| 0.1721 | 17000 | 0.0019 | - | - |
| 0.1772 | 17500 | 0.0019 | - | - |
| 0.1822 | 18000 | 0.0019 | - | - |
| 0.1873 | 18500 | 0.0019 | - | - |
| 0.1924 | 19000 | 0.0019 | - | - |
| 0.1974 | 19500 | 0.0019 | - | - |
| 0.2025 | 20000 | 0.0019 | - | - |
| 0.2075 | 20500 | 0.0019 | - | - |
| 0.2126 | 21000 | 0.0019 | - | - |
| 0.2177 | 21500 | 0.0019 | - | - |
| 0.2227 | 22000 | 0.0019 | - | - |
| 0.2278 | 22500 | 0.0019 | - | - |
| 0.2329 | 23000 | 0.0019 | - | - |
| 0.2379 | 23500 | 0.0019 | - | - |
| 0.2430 | 24000 | 0.0019 | - | - |
| 0.2480 | 24500 | 0.0019 | - | - |
| 0.2531 | 25000 | 0.0019 | - | - |
| 0.2582 | 25500 | 0.0019 | - | - |
| 0.2632 | 26000 | 0.0019 | - | - |
| 0.2683 | 26500 | 0.0019 | - | - |
| 0.2733 | 27000 | 0.0019 | - | - |
| 0.2784 | 27500 | 0.0019 | - | - |
| 0.2835 | 28000 | 0.0019 | - | - |
| 0.2885 | 28500 | 0.0019 | - | - |
| 0.2936 | 29000 | 0.0019 | - | - |
| 0.2987 | 29500 | 0.0019 | - | - |
| 0.3037 | 30000 | 0.0019 | - | - |
| 0.3088 | 30500 | 0.0019 | - | - |
| 0.3138 | 31000 | 0.0019 | - | - |
| 0.3189 | 31500 | 0.0019 | - | - |
| 0.3240 | 32000 | 0.0019 | - | - |
| 0.3290 | 32500 | 0.0019 | - | - |
| 0.3341 | 33000 | 0.0019 | - | - |
| 0.3392 | 33500 | 0.0019 | - | - |
| 0.3442 | 34000 | 0.0019 | - | - |
| 0.3493 | 34500 | 0.0019 | - | - |
| 0.3543 | 35000 | 0.0019 | - | - |
| 0.3594 | 35500 | 0.0019 | - | - |
| 0.3645 | 36000 | 0.0019 | - | - |
| 0.3695 | 36500 | 0.0019 | - | - |
| 0.3746 | 37000 | 0.0019 | - | - |
| 0.3796 | 37500 | 0.0019 | - | - |
| 0.3847 | 38000 | 0.0019 | - | - |
| 0.3898 | 38500 | 0.0019 | - | - |
| 0.3948 | 39000 | 0.0019 | - | - |
| 0.3999 | 39500 | 0.0019 | - | - |
| 0.4050 | 40000 | 0.0019 | - | - |
| 0.4100 | 40500 | 0.0019 | - | - |
| 0.4151 | 41000 | 0.0019 | - | - |
| 0.4201 | 41500 | 0.0019 | - | - |
| 0.4252 | 42000 | 0.0019 | - | - |
| 0.4303 | 42500 | 0.0019 | - | - |
| 0.4353 | 43000 | 0.0019 | - | - |
| 0.4404 | 43500 | 0.0019 | - | - |
| 0.4455 | 44000 | 0.0019 | - | - |
| 0.4505 | 44500 | 0.0019 | - | - |
| 0.4556 | 45000 | 0.0019 | - | - |
| 0.4606 | 45500 | 0.0019 | - | - |
| 0.4657 | 46000 | 0.0019 | - | - |
| 0.4708 | 46500 | 0.0019 | - | - |
| 0.4758 | 47000 | 0.0019 | - | - |
| 0.4809 | 47500 | 0.0019 | - | - |
| 0.4859 | 48000 | 0.0019 | - | - |
| 0.4910 | 48500 | 0.0019 | - | - |
| 0.4961 | 49000 | 0.0019 | - | - |
| 0.5011 | 49500 | 0.0019 | - | - |
| 0.5062 | 50000 | 0.0019 | - | - |
| 0.5113 | 50500 | 0.0019 | - | - |
| 0.5163 | 51000 | 0.0019 | - | - |
| 0.5214 | 51500 | 0.0018 | - | - |
| 0.5264 | 52000 | 0.0019 | - | - |
| 0.5315 | 52500 | 0.0019 | - | - |
| 0.5366 | 53000 | 0.0019 | - | - |
| 0.5416 | 53500 | 0.0019 | - | - |
| 0.5467 | 54000 | 0.0019 | - | - |
| 0.5518 | 54500 | 0.0019 | - | - |
| 0.5568 | 55000 | 0.0019 | - | - |
| 0.5619 | 55500 | 0.0018 | - | - |
| 0.5669 | 56000 | 0.0019 | - | - |
| 0.5720 | 56500 | 0.0019 | - | - |
| 0.5771 | 57000 | 0.0018 | - | - |
| 0.5821 | 57500 | 0.0018 | - | - |
| 0.5872 | 58000 | 0.0019 | - | - |
| 0.5922 | 58500 | 0.0019 | - | - |
| 0.5973 | 59000 | 0.0019 | - | - |
| 0.6024 | 59500 | 0.0019 | - | - |
| 0.6074 | 60000 | 0.0018 | - | - |
| 0.6125 | 60500 | 0.0018 | - | - |
| 0.6176 | 61000 | 0.0019 | - | - |
| 0.6226 | 61500 | 0.0018 | - | - |
| 0.6277 | 62000 | 0.0019 | - | - |
| 0.6327 | 62500 | 0.0019 | - | - |
| 0.6378 | 63000 | 0.0019 | - | - |
| 0.6429 | 63500 | 0.0019 | - | - |
| 0.6479 | 64000 | 0.0018 | - | - |
| 0.6530 | 64500 | 0.0018 | - | - |
| 0.6581 | 65000 | 0.0018 | - | - |
| 0.6631 | 65500 | 0.0019 | - | - |
| 0.6682 | 66000 | 0.0019 | - | - |
| 0.6732 | 66500 | 0.0018 | - | - |
| 0.6783 | 67000 | 0.0018 | - | - |
| 0.6834 | 67500 | 0.0018 | - | - |
| 0.6884 | 68000 | 0.0019 | - | - |
| 0.6935 | 68500 | 0.0018 | - | - |
| 0.6986 | 69000 | 0.0019 | - | - |
| 0.7036 | 69500 | 0.0018 | - | - |
| 0.7087 | 70000 | 0.0018 | - | - |
| 0.7137 | 70500 | 0.0018 | - | - |
| 0.7188 | 71000 | 0.0018 | - | - |
| 0.7239 | 71500 | 0.0018 | - | - |
| 0.7289 | 72000 | 0.0018 | - | - |
| 0.7340 | 72500 | 0.0018 | - | - |
| 0.7390 | 73000 | 0.0018 | - | - |
| 0.7441 | 73500 | 0.0018 | - | - |
| 0.7492 | 74000 | 0.0018 | - | - |
| 0.7542 | 74500 | 0.0018 | - | - |
| 0.7593 | 75000 | 0.0018 | - | - |
| 0.7644 | 75500 | 0.0018 | - | - |
| 0.7694 | 76000 | 0.0018 | - | - |
| 0.7745 | 76500 | 0.0018 | - | - |
| 0.7795 | 77000 | 0.0018 | - | - |
| 0.7846 | 77500 | 0.0018 | - | - |
| 0.7897 | 78000 | 0.0018 | - | - |
| 0.7947 | 78500 | 0.0018 | - | - |
| 0.7998 | 79000 | 0.0018 | - | - |
| 0.8049 | 79500 | 0.0018 | - | - |
| 0.8099 | 80000 | 0.0018 | - | - |
| 0.8150 | 80500 | 0.0018 | - | - |
| 0.8200 | 81000 | 0.0018 | - | - |
| 0.8251 | 81500 | 0.0018 | - | - |
| 0.8302 | 82000 | 0.0018 | - | - |
| 0.8352 | 82500 | 0.0019 | - | - |
| 0.8403 | 83000 | 0.0018 | - | - |
| 0.8453 | 83500 | 0.0018 | - | - |
| 0.8504 | 84000 | 0.0018 | - | - |
| 0.8555 | 84500 | 0.0018 | - | - |
| 0.8605 | 85000 | 0.0018 | - | - |
| 0.8656 | 85500 | 0.0018 | - | - |
| 0.8707 | 86000 | 0.0018 | - | - |
| 0.8757 | 86500 | 0.0018 | - | - |
| 0.8808 | 87000 | 0.0018 | - | - |
| 0.8858 | 87500 | 0.0018 | - | - |
| 0.8909 | 88000 | 0.0018 | - | - |
| 0.8960 | 88500 | 0.0018 | - | - |
| 0.9010 | 89000 | 0.0018 | - | - |
| 0.9061 | 89500 | 0.0018 | - | - |
| 0.9112 | 90000 | 0.0018 | - | - |
| 0.9162 | 90500 | 0.0018 | - | - |
| 0.9213 | 91000 | 0.0018 | - | - |
| 0.9263 | 91500 | 0.0018 | - | - |
| 0.9314 | 92000 | 0.0018 | - | - |
| 0.9365 | 92500 | 0.0018 | - | - |
| 0.9415 | 93000 | 0.0018 | - | - |
| 0.9466 | 93500 | 0.0018 | - | - |
| 0.9516 | 94000 | 0.0018 | - | - |
| 0.9567 | 94500 | 0.0018 | - | - |
| 0.9618 | 95000 | 0.0018 | - | - |
| 0.9668 | 95500 | 0.0018 | - | - |
| 0.9719 | 96000 | 0.0018 | - | - |
| 0.9770 | 96500 | 0.0018 | - | - |
| 0.9820 | 97000 | 0.0018 | - | - |
| 0.9871 | 97500 | 0.0018 | - | - |
| 0.9921 | 98000 | 0.0018 | - | - |
| 0.9972 | 98500 | 0.0018 | - | - |
| 1.0 | 98776 | - | 0.0021 | -0.17975432 |
| 0.0051 | 500 | 0.0018 | - | - |
| 0.0101 | 1000 | 0.0018 | - | - |
| 0.0152 | 1500 | 0.0018 | - | - |
| 0.0202 | 2000 | 0.0018 | - | - |
| 0.0253 | 2500 | 0.0018 | - | - |
| 0.0304 | 3000 | 0.0018 | - | - |
| 0.0354 | 3500 | 0.0018 | - | - |
| 0.0405 | 4000 | 0.0018 | - | - |
| 0.0456 | 4500 | 0.0018 | - | - |
| 0.0506 | 5000 | 0.0018 | - | - |
| 0.0557 | 5500 | 0.0018 | - | - |
| 0.0607 | 6000 | 0.0018 | - | - |
| 0.0658 | 6500 | 0.0018 | - | - |
| 0.0709 | 7000 | 0.0018 | - | - |
| 0.0759 | 7500 | 0.0018 | - | - |
| 0.0810 | 8000 | 0.0018 | - | - |
| 0.0861 | 8500 | 0.0018 | - | - |
| 0.0911 | 9000 | 0.0018 | - | - |
| 0.0962 | 9500 | 0.0018 | - | - |
| 0.1012 | 10000 | 0.0018 | - | - |
| 0.1063 | 10500 | 0.0018 | - | - |
| 0.1114 | 11000 | 0.0018 | - | - |
| 0.1164 | 11500 | 0.0018 | - | - |
| 0.1215 | 12000 | 0.0018 | - | - |
| 0.1265 | 12500 | 0.0018 | - | - |
| 0.1316 | 13000 | 0.0018 | - | - |
| 0.1367 | 13500 | 0.0018 | - | - |
| 0.1417 | 14000 | 0.0018 | - | - |
| 0.1468 | 14500 | 0.0018 | - | - |
| 0.1519 | 15000 | 0.0018 | - | - |
| 0.1569 | 15500 | 0.0018 | - | - |
| 0.1620 | 16000 | 0.0018 | - | - |
| 0.1670 | 16500 | 0.0018 | - | - |
| 0.1721 | 17000 | 0.0018 | - | - |
| 0.1772 | 17500 | 0.0018 | - | - |
| 0.1822 | 18000 | 0.0018 | - | - |
| 0.1873 | 18500 | 0.0018 | - | - |
| 0.1924 | 19000 | 0.0018 | - | - |
| 0.1974 | 19500 | 0.0018 | - | - |
| 0.2025 | 20000 | 0.0018 | - | - |
| 0.2075 | 20500 | 0.0018 | - | - |
| 0.2126 | 21000 | 0.0018 | - | - |
| 0.2177 | 21500 | 0.0018 | - | - |
| 0.2227 | 22000 | 0.0018 | - | - |
| 0.2278 | 22500 | 0.0018 | - | - |
| 0.2329 | 23000 | 0.0018 | - | - |
| 0.2379 | 23500 | 0.0018 | - | - |
| 0.2430 | 24000 | 0.0018 | - | - |
| 0.2480 | 24500 | 0.0018 | - | - |
| 0.2531 | 25000 | 0.0018 | - | - |
| 0.2582 | 25500 | 0.0018 | - | - |
| 0.2632 | 26000 | 0.0018 | - | - |
| 0.2683 | 26500 | 0.0018 | - | - |
| 0.2733 | 27000 | 0.0018 | - | - |
| 0.2784 | 27500 | 0.0018 | - | - |
| 0.2835 | 28000 | 0.0018 | - | - |
| 0.2885 | 28500 | 0.0018 | - | - |
| 0.2936 | 29000 | 0.0018 | - | - |
| 0.2987 | 29500 | 0.0018 | - | - |
| 0.3037 | 30000 | 0.0018 | - | - |
| 0.3088 | 30500 | 0.0018 | - | - |
| 0.3138 | 31000 | 0.0018 | - | - |
| 0.3189 | 31500 | 0.0018 | - | - |
| 0.3240 | 32000 | 0.0018 | - | - |
| 0.3290 | 32500 | 0.0018 | - | - |
| 0.3341 | 33000 | 0.0018 | - | - |
| 0.3392 | 33500 | 0.0018 | - | - |
| 0.3442 | 34000 | 0.0018 | - | - |
| 0.3493 | 34500 | 0.0018 | - | - |
| 0.3543 | 35000 | 0.0018 | - | - |
| 0.3594 | 35500 | 0.0018 | - | - |
| 0.3645 | 36000 | 0.0018 | - | - |
| 0.3695 | 36500 | 0.0018 | - | - |
| 0.3746 | 37000 | 0.0018 | - | - |
| 0.3796 | 37500 | 0.0018 | - | - |
| 0.3847 | 38000 | 0.0018 | - | - |
| 0.3898 | 38500 | 0.0018 | - | - |
| 0.3948 | 39000 | 0.0018 | - | - |
| 0.3999 | 39500 | 0.0018 | - | - |
| 0.4050 | 40000 | 0.0018 | - | - |
| 0.4100 | 40500 | 0.0018 | - | - |
| 0.4151 | 41000 | 0.0018 | - | - |
| 0.4201 | 41500 | 0.0018 | - | - |
| 0.4252 | 42000 | 0.0018 | - | - |
| 0.4303 | 42500 | 0.0018 | - | - |
| 0.4353 | 43000 | 0.0018 | - | - |
| 0.4404 | 43500 | 0.0018 | - | - |
| 0.4455 | 44000 | 0.0018 | - | - |
| 0.4505 | 44500 | 0.0018 | - | - |
| 0.4556 | 45000 | 0.0018 | - | - |
| 0.4606 | 45500 | 0.0018 | - | - |
| 0.4657 | 46000 | 0.0018 | - | - |
| 0.4708 | 46500 | 0.0018 | - | - |
| 0.4758 | 47000 | 0.0018 | - | - |
| 0.4809 | 47500 | 0.0018 | - | - |
| 0.4859 | 48000 | 0.0018 | - | - |
| 0.4910 | 48500 | 0.0018 | - | - |
| 0.4961 | 49000 | 0.0018 | - | - |
| 0.5011 | 49500 | 0.0018 | - | - |
| 0.5062 | 50000 | 0.0018 | - | - |
| 0.5113 | 50500 | 0.0018 | - | - |
| 0.5163 | 51000 | 0.0018 | - | - |
| 0.5214 | 51500 | 0.0018 | - | - |
| 0.5264 | 52000 | 0.0018 | - | - |
| 0.5315 | 52500 | 0.0018 | - | - |
| 0.5366 | 53000 | 0.0018 | - | - |
| 0.5416 | 53500 | 0.0018 | - | - |
| 0.5467 | 54000 | 0.0018 | - | - |
| 0.5518 | 54500 | 0.0018 | - | - |
| 0.5568 | 55000 | 0.0018 | - | - |
| 0.5619 | 55500 | 0.0018 | - | - |
| 0.5669 | 56000 | 0.0018 | - | - |
| 0.5720 | 56500 | 0.0018 | - | - |
| 0.5771 | 57000 | 0.0018 | - | - |
| 0.5821 | 57500 | 0.0018 | - | - |
| 0.5872 | 58000 | 0.0018 | - | - |
| 0.5922 | 58500 | 0.0018 | - | - |
| 0.5973 | 59000 | 0.0018 | - | - |
| 0.6024 | 59500 | 0.0018 | - | - |
| 0.6074 | 60000 | 0.0018 | - | - |
| 0.6125 | 60500 | 0.0018 | - | - |
| 0.6176 | 61000 | 0.0018 | - | - |
| 0.6226 | 61500 | 0.0018 | - | - |
| 0.6277 | 62000 | 0.0018 | - | - |
| 0.6327 | 62500 | 0.0018 | - | - |
| 0.6378 | 63000 | 0.0018 | - | - |
| 0.6429 | 63500 | 0.0018 | - | - |
| 0.6479 | 64000 | 0.0018 | - | - |
| 0.6530 | 64500 | 0.0018 | - | - |
| 0.6581 | 65000 | 0.0018 | - | - |
| 0.6631 | 65500 | 0.0018 | - | - |
| 0.6682 | 66000 | 0.0018 | - | - |
| 0.6732 | 66500 | 0.0018 | - | - |
| 0.6783 | 67000 | 0.0018 | - | - |
| 0.6834 | 67500 | 0.0018 | - | - |
| 0.6884 | 68000 | 0.0018 | - | - |
| 0.6935 | 68500 | 0.0018 | - | - |
| 0.6986 | 69000 | 0.0018 | - | - |
| 0.7036 | 69500 | 0.0018 | - | - |
| 0.7087 | 70000 | 0.0018 | - | - |
| 0.7137 | 70500 | 0.0018 | - | - |
| 0.7188 | 71000 | 0.0018 | - | - |
| 0.7239 | 71500 | 0.0018 | - | - |
| 0.7289 | 72000 | 0.0018 | - | - |
| 0.7340 | 72500 | 0.0018 | - | - |
| 0.7390 | 73000 | 0.0018 | - | - |
| 0.7441 | 73500 | 0.0018 | - | - |
| 0.7492 | 74000 | 0.0018 | - | - |
| 0.7542 | 74500 | 0.0018 | - | - |
| 0.7593 | 75000 | 0.0018 | - | - |
| 0.7644 | 75500 | 0.0018 | - | - |
| 0.7694 | 76000 | 0.0018 | - | - |
| 0.7745 | 76500 | 0.0018 | - | - |
| 0.7795 | 77000 | 0.0018 | - | - |
| 0.7846 | 77500 | 0.0018 | - | - |
| 0.7897 | 78000 | 0.0018 | - | - |
| 0.7947 | 78500 | 0.0018 | - | - |
| 0.7998 | 79000 | 0.0018 | - | - |
| 0.8049 | 79500 | 0.0018 | - | - |
| 0.8099 | 80000 | 0.0018 | - | - |
| 0.8150 | 80500 | 0.0018 | - | - |
| 0.8200 | 81000 | 0.0018 | - | - |
| 0.8251 | 81500 | 0.0018 | - | - |
| 0.8302 | 82000 | 0.0018 | - | - |
| 0.8352 | 82500 | 0.0018 | - | - |
| 0.8403 | 83000 | 0.0018 | - | - |
| 0.8453 | 83500 | 0.0018 | - | - |
| 0.8504 | 84000 | 0.0018 | - | - |
| 0.8555 | 84500 | 0.0018 | - | - |
| 0.8605 | 85000 | 0.0018 | - | - |
| 0.8656 | 85500 | 0.0018 | - | - |
| 0.8707 | 86000 | 0.0018 | - | - |
| 0.8757 | 86500 | 0.0018 | - | - |
| 0.8808 | 87000 | 0.0018 | - | - |
| 0.8858 | 87500 | 0.0018 | - | - |
| 0.8909 | 88000 | 0.0018 | - | - |
| 0.8960 | 88500 | 0.0018 | - | - |
| 0.9010 | 89000 | 0.0018 | - | - |
| 0.9061 | 89500 | 0.0018 | - | - |
| 0.9112 | 90000 | 0.0018 | - | - |
| 0.9162 | 90500 | 0.0018 | - | - |
| 0.9213 | 91000 | 0.0018 | - | - |
| 0.9263 | 91500 | 0.0018 | - | - |
| 0.9314 | 92000 | 0.0018 | - | - |
| 0.9365 | 92500 | 0.0018 | - | - |
| 0.9415 | 93000 | 0.0018 | - | - |
| 0.9466 | 93500 | 0.0018 | - | - |
| 0.9516 | 94000 | 0.0018 | - | - |
| 0.9567 | 94500 | 0.0018 | - | - |
| 0.9618 | 95000 | 0.0017 | - | - |
| 0.9668 | 95500 | 0.0018 | - | - |
| 0.9719 | 96000 | 0.0018 | - | - |
| 0.9770 | 96500 | 0.0018 | - | - |
| 0.9820 | 97000 | 0.0018 | - | - |
| 0.9871 | 97500 | 0.0018 | - | - |
| 0.9921 | 98000 | 0.0018 | - | - |
| 0.9972 | 98500 | 0.0018 | - | - |
| 1.0 | 98776 | - | 0.0021 | -0.17605598 |
| 0.0051 | 500 | 0.0018 | - | - |
| 0.0101 | 1000 | 0.0018 | - | - |
| 0.0152 | 1500 | 0.0018 | - | - |
| 0.0202 | 2000 | 0.0018 | - | - |
| 0.0253 | 2500 | 0.0018 | - | - |
| 0.0304 | 3000 | 0.0018 | - | - |
| 0.0354 | 3500 | 0.0018 | - | - |
| 0.0405 | 4000 | 0.0018 | - | - |
| 0.0456 | 4500 | 0.0018 | - | - |
| 0.0506 | 5000 | 0.0018 | - | - |
| 0.0557 | 5500 | 0.0018 | - | - |
| 0.0607 | 6000 | 0.0018 | - | - |
| 0.0658 | 6500 | 0.0018 | - | - |
| 0.0709 | 7000 | 0.0018 | - | - |
| 0.0759 | 7500 | 0.0018 | - | - |
| 0.0810 | 8000 | 0.0018 | - | - |
| 0.0861 | 8500 | 0.0018 | - | - |
| 0.0911 | 9000 | 0.0018 | - | - |
| 0.0962 | 9500 | 0.0018 | - | - |
| 0.1012 | 10000 | 0.0018 | - | - |
| 0.1063 | 10500 | 0.0018 | - | - |
| 0.1114 | 11000 | 0.0018 | - | - |
| 0.1164 | 11500 | 0.0018 | - | - |
| 0.1215 | 12000 | 0.0018 | - | - |
| 0.1265 | 12500 | 0.0018 | - | - |
| 0.1316 | 13000 | 0.0018 | - | - |
| 0.1367 | 13500 | 0.0018 | - | - |
| 0.1417 | 14000 | 0.0018 | - | - |
| 0.1468 | 14500 | 0.0018 | - | - |
| 0.1519 | 15000 | 0.0018 | - | - |
| 0.1569 | 15500 | 0.0018 | - | - |
| 0.1620 | 16000 | 0.0018 | - | - |
| 0.1670 | 16500 | 0.0018 | - | - |
| 0.1721 | 17000 | 0.0018 | - | - |
| 0.1772 | 17500 | 0.0018 | - | - |
| 0.1822 | 18000 | 0.0018 | - | - |
| 0.1873 | 18500 | 0.0018 | - | - |
| 0.1924 | 19000 | 0.0018 | - | - |
| 0.1974 | 19500 | 0.0018 | - | - |
| 0.2025 | 20000 | 0.0018 | - | - |
| 0.2075 | 20500 | 0.0018 | - | - |
| 0.2126 | 21000 | 0.0018 | - | - |
| 0.2177 | 21500 | 0.0018 | - | - |
| 0.2227 | 22000 | 0.0018 | - | - |
| 0.2278 | 22500 | 0.0017 | - | - |
| 0.2329 | 23000 | 0.0018 | - | - |
| 0.2379 | 23500 | 0.0018 | - | - |
| 0.2430 | 24000 | 0.0018 | - | - |
| 0.2480 | 24500 | 0.0018 | - | - |
| 0.2531 | 25000 | 0.0018 | - | - |
| 0.2582 | 25500 | 0.0018 | - | - |
| 0.2632 | 26000 | 0.0018 | - | - |
| 0.2683 | 26500 | 0.0018 | - | - |
| 0.2733 | 27000 | 0.0018 | - | - |
| 0.2784 | 27500 | 0.0018 | - | - |
| 0.2835 | 28000 | 0.0018 | - | - |
| 0.2885 | 28500 | 0.0018 | - | - |
| 0.2936 | 29000 | 0.0018 | - | - |
| 0.2987 | 29500 | 0.0018 | - | - |
| 0.3037 | 30000 | 0.0018 | - | - |
| 0.3088 | 30500 | 0.0018 | - | - |
| 0.3138 | 31000 | 0.0018 | - | - |
| 0.3189 | 31500 | 0.0018 | - | - |
| 0.3240 | 32000 | 0.0018 | - | - |
| 0.3290 | 32500 | 0.0018 | - | - |
| 0.3341 | 33000 | 0.0018 | - | - |
| 0.3392 | 33500 | 0.0018 | - | - |
| 0.3442 | 34000 | 0.0018 | - | - |
| 0.3493 | 34500 | 0.0018 | - | - |
| 0.3543 | 35000 | 0.0018 | - | - |
| 0.3594 | 35500 | 0.0018 | - | - |
| 0.3645 | 36000 | 0.0018 | - | - |
| 0.3695 | 36500 | 0.0018 | - | - |
| 0.3746 | 37000 | 0.0018 | - | - |
| 0.3796 | 37500 | 0.0018 | - | - |
| 0.3847 | 38000 | 0.0018 | - | - |
| 0.3898 | 38500 | 0.0018 | - | - |
| 0.3948 | 39000 | 0.0018 | - | - |
| 0.3999 | 39500 | 0.0018 | - | - |
| 0.4050 | 40000 | 0.0018 | - | - |
| 0.4100 | 40500 | 0.0018 | - | - |
| 0.4151 | 41000 | 0.0018 | - | - |
| 0.4201 | 41500 | 0.0018 | - | - |
| 0.4252 | 42000 | 0.0018 | - | - |
| 0.4303 | 42500 | 0.0018 | - | - |
| 0.4353 | 43000 | 0.0018 | - | - |
| 0.4404 | 43500 | 0.0018 | - | - |
| 0.4455 | 44000 | 0.0018 | - | - |
| 0.4505 | 44500 | 0.0018 | - | - |
| 0.4556 | 45000 | 0.0018 | - | - |
| 0.4606 | 45500 | 0.0018 | - | - |
| 0.4657 | 46000 | 0.0018 | - | - |
| 0.4708 | 46500 | 0.0018 | - | - |
| 0.4758 | 47000 | 0.0018 | - | - |
| 0.4809 | 47500 | 0.0018 | - | - |
| 0.4859 | 48000 | 0.0018 | - | - |
| 0.4910 | 48500 | 0.0018 | - | - |
| 0.4961 | 49000 | 0.0018 | - | - |
| 0.5011 | 49500 | 0.0018 | - | - |
| 0.5062 | 50000 | 0.0018 | - | - |
| 0.5113 | 50500 | 0.0018 | - | - |
| 0.5163 | 51000 | 0.0018 | - | - |
| 0.5214 | 51500 | 0.0017 | - | - |
| 0.5264 | 52000 | 0.0018 | - | - |
| 0.5315 | 52500 | 0.0018 | - | - |
| 0.5366 | 53000 | 0.0018 | - | - |
| 0.5416 | 53500 | 0.0018 | - | - |
| 0.5467 | 54000 | 0.0018 | - | - |
| 0.5518 | 54500 | 0.0018 | - | - |
| 0.5568 | 55000 | 0.0017 | - | - |
| 0.5619 | 55500 | 0.0017 | - | - |
| 0.5669 | 56000 | 0.0018 | - | - |
| 0.5720 | 56500 | 0.0017 | - | - |
| 0.5771 | 57000 | 0.0017 | - | - |
| 0.5821 | 57500 | 0.0017 | - | - |
| 0.5872 | 58000 | 0.0018 | - | - |
| 0.5922 | 58500 | 0.0017 | - | - |
| 0.5973 | 59000 | 0.0018 | - | - |
| 0.6024 | 59500 | 0.0018 | - | - |
| 0.6074 | 60000 | 0.0017 | - | - |
| 0.6125 | 60500 | 0.0017 | - | - |
| 0.6176 | 61000 | 0.0018 | - | - |
| 0.6226 | 61500 | 0.0017 | - | - |
| 0.6277 | 62000 | 0.0018 | - | - |
| 0.6327 | 62500 | 0.0018 | - | - |
| 0.6378 | 63000 | 0.0018 | - | - |
| 0.6429 | 63500 | 0.0018 | - | - |
| 0.6479 | 64000 | 0.0017 | - | - |
| 0.6530 | 64500 | 0.0017 | - | - |
| 0.6581 | 65000 | 0.0017 | - | - |
| 0.6631 | 65500 | 0.0017 | - | - |
| 0.6682 | 66000 | 0.0018 | - | - |
| 0.6732 | 66500 | 0.0017 | - | - |
| 0.6783 | 67000 | 0.0017 | - | - |
| 0.6834 | 67500 | 0.0017 | - | - |
| 0.6884 | 68000 | 0.0018 | - | - |
| 0.6935 | 68500 | 0.0017 | - | - |
| 0.6986 | 69000 | 0.0018 | - | - |
| 0.7036 | 69500 | 0.0017 | - | - |
| 0.7087 | 70000 | 0.0017 | - | - |
| 0.7137 | 70500 | 0.0017 | - | - |
| 0.7188 | 71000 | 0.0017 | - | - |
| 0.7239 | 71500 | 0.0017 | - | - |
| 0.7289 | 72000 | 0.0017 | - | - |
| 0.7340 | 72500 | 0.0017 | - | - |
| 0.7390 | 73000 | 0.0017 | - | - |
| 0.7441 | 73500 | 0.0017 | - | - |
| 0.7492 | 74000 | 0.0018 | - | - |
| 0.7542 | 74500 | 0.0017 | - | - |
| 0.7593 | 75000 | 0.0017 | - | - |
| 0.7644 | 75500 | 0.0017 | - | - |
| 0.7694 | 76000 | 0.0017 | - | - |
| 0.7745 | 76500 | 0.0017 | - | - |
| 0.7795 | 77000 | 0.0017 | - | - |
| 0.7846 | 77500 | 0.0017 | - | - |
| 0.7897 | 78000 | 0.0017 | - | - |
| 0.7947 | 78500 | 0.0017 | - | - |
| 0.7998 | 79000 | 0.0017 | - | - |
| 0.8049 | 79500 | 0.0017 | - | - |
| 0.8099 | 80000 | 0.0017 | - | - |
| 0.8150 | 80500 | 0.0017 | - | - |
| 0.8200 | 81000 | 0.0017 | - | - |
| 0.8251 | 81500 | 0.0017 | - | - |
| 0.8302 | 82000 | 0.0017 | - | - |
| 0.8352 | 82500 | 0.0018 | - | - |
| 0.8403 | 83000 | 0.0017 | - | - |
| 0.8453 | 83500 | 0.0017 | - | - |
| 0.8504 | 84000 | 0.0017 | - | - |
| 0.8555 | 84500 | 0.0017 | - | - |
| 0.8605 | 85000 | 0.0017 | - | - |
| 0.8656 | 85500 | 0.0017 | - | - |
| 0.8707 | 86000 | 0.0017 | - | - |
| 0.8757 | 86500 | 0.0017 | - | - |
| 0.8808 | 87000 | 0.0017 | - | - |
| 0.8858 | 87500 | 0.0017 | - | - |
| 0.8909 | 88000 | 0.0017 | - | - |
| 0.8960 | 88500 | 0.0017 | - | - |
| 0.9010 | 89000 | 0.0017 | - | - |
| 0.9061 | 89500 | 0.0017 | - | - |
| 0.9112 | 90000 | 0.0017 | - | - |
| 0.9162 | 90500 | 0.0017 | - | - |
| 0.9213 | 91000 | 0.0017 | - | - |
| 0.9263 | 91500 | 0.0017 | - | - |
| 0.9314 | 92000 | 0.0017 | - | - |
| 0.9365 | 92500 | 0.0017 | - | - |
| 0.9415 | 93000 | 0.0017 | - | - |
| 0.9466 | 93500 | 0.0017 | - | - |
| 0.9516 | 94000 | 0.0017 | - | - |
| 0.9567 | 94500 | 0.0017 | - | - |
| 0.9618 | 95000 | 0.0017 | - | - |
| 0.9668 | 95500 | 0.0017 | - | - |
| 0.9719 | 96000 | 0.0017 | - | - |
| 0.9770 | 96500 | 0.0017 | - | - |
| 0.9820 | 97000 | 0.0017 | - | - |
| 0.9871 | 97500 | 0.0017 | - | - |
| 0.9921 | 98000 | 0.0017 | - | - |
| 0.9972 | 98500 | 0.0017 | - | - |
| 1.0 | 98776 | - | 0.0021 | -0.17373772 |
</details>
### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 3.3.1
- Transformers: 4.48.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.0
- Datasets: 3.1.0
- Tokenizers: 0.21.0
## 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",
}
```
#### MSELoss
```bibtex
@inproceedings{reimers-2020-multilingual-sentence-bert,
title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2004.09813",
}
```
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