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---
language:
- en
- multilingual
- es
- pt
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:13930944
- loss:MSELoss
base_model: sentence-transformers/paraphrase-MiniLM-L6-v2
widget:
- source_sentence: Custer recommends that Congress find a way to end the treaties
with the Lakota as soon as possible.
sentences:
- Custer recomienda al Congreso encontrar un modo de terminar los tratados con los
lakota lo antes posible.
- Pero estos poros de aquí son especiales.
- Esta es la intersección más directa, obvia, de las dos cosas.
- source_sentence: And the USFDA has a jurisdictional problem.
sentences:
- E a FDA dos Estados Unidos tem um problema de jurisdição.
- Eu estimei que, atualmente no mundo, gastamos cerca de 106 vidas em média ensinando
as pessoas a calcular manualmente.
- Posso comprar aquele produto sem comprometer minha ética?
- source_sentence: In Sri Lanka, a decades-long civil war between the Tamil minority
and the Sinhala majority led to a bloody climax in 2009, after perhaps as many
as 100,000 people had been killed since 1983.
sentences:
- Portanto, temos de investir no desenvolvimento de líderes, líderes que tenham
as habilidades, visão e determinação para fazer a paz.
- No Sri Lanka, uma guerra civil de décadas entre a minoria tâmil e a maioria cingalesa
levou a um clímax sangrento em 2009, após cerca de 100 mil pessoas serem assassinadas
desde 1983.
- Nos anos 90, houve uma série de escândalos relativos à produção de bens de marca
nos EUA -- trabalho infantil, trabalho forçado, graves abusos de saúde e segurança
--
- source_sentence: The provisions in the agreement may be complex, but so is the underlying
conflict.
sentences:
- As saladas que você vê no McDonald's vêm desse trabalho -- eles terão uma salada
asiática. Na Pepsi, dois terços do crescimento de rendimento vieram de seus alimentos
saudáveis.
- Não apenas esta, mas conectados com as idéias que estão aqui, para fazê-las mais
coerentes.
- O disposto no acordo pode ser complexo, mas assim é o conflito subjacente.
- source_sentence: We now call this place home.
sentences:
- e outros não contêm. Neste desenho, a célula branca azulada, no canto superior
esquerdo não reage à luz porque não possui o poro ativado por luz.
- Moramos ali. Agora é aqui a nossa casa.
- É mais fácil do que se possa imaginar.
datasets:
- sentence-transformers/parallel-sentences-talks
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- negative_mse
- src2trg_accuracy
- trg2src_accuracy
- mean_accuracy
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L6-v2
results:
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en pt br
type: en-pt-br
metrics:
- type: negative_mse
value: -4.06170654296875
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en pt br
type: en-pt-br
metrics:
- type: src2trg_accuracy
value: 0.9858870967741935
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.9808467741935484
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.983366935483871
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en es
type: en-es
metrics:
- type: negative_mse
value: -4.247319221496582
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en es
type: en-es
metrics:
- type: src2trg_accuracy
value: 0.908008008008008
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.897997997997998
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.903003003003003
name: Mean Accuracy
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts17 es en test
type: sts17-es-en-test
metrics:
- type: pearson_cosine
value: 0.7713723133430411
name: Pearson Cosine
- type: spearman_cosine
value: 0.7861741769541355
name: Spearman Cosine
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en pt
type: en-pt
metrics:
- type: negative_mse
value: -4.255536079406738
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en pt
type: en-pt
metrics:
- type: src2trg_accuracy
value: 0.8951160928742994
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.8824059247397918
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.8887610088070457
name: Mean Accuracy
---
# SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2) on the [en-pt-br](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks), en-es and en-pt datasets. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2) <!-- at revision 9a27583f9c2cc7c03a95c08c5f087318109e2613 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- [en-pt-br](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks)
- en-es
- en-pt
- **Languages:** en, multilingual, es, pt
<!-- - **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': 128, '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})
(2): 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("jvanhoof/all-MiniLM-L6-multilingual-v2-en-es-pt-pt-br")
# Run inference
sentences = [
'We now call this place home.',
'Moramos ali. Agora é aqui a nossa casa.',
'É mais fácil do que se possa imaginar.',
]
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
* Datasets: `en-pt-br`, `en-es` and `en-pt`
* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | en-pt-br | en-es | en-pt |
|:-----------------|:------------|:------------|:------------|
| **negative_mse** | **-4.0617** | **-4.2473** | **-4.2555** |
#### Translation
* Datasets: `en-pt-br`, `en-es` and `en-pt`
* Evaluated with [<code>TranslationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | en-pt-br | en-es | en-pt |
|:------------------|:-----------|:----------|:-----------|
| src2trg_accuracy | 0.9859 | 0.908 | 0.8951 |
| trg2src_accuracy | 0.9808 | 0.898 | 0.8824 |
| **mean_accuracy** | **0.9834** | **0.903** | **0.8888** |
#### Semantic Similarity
* Dataset: `sts17-es-en-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7714 |
| **spearman_cosine** | **0.7862** |
<!--
## 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 Datasets
#### en-pt-br
* Dataset: [en-pt-br](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [0c70bc6](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/0c70bc6714efb1df12f8a16b9056e4653563d128)
* Size: 405,807 training samples
* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | english | non_english | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | <ul><li>min: 4 tokens</li><li>mean: 23.98 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 36.86 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
* Samples:
| english | non_english | label |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|
| <code>And then there are certain conceptual things that can also benefit from hand calculating, but I think they're relatively small in number.</code> | <code>E também existem alguns aspectos conceituais que também podem se beneficiar do cálculo manual, mas eu acho que eles são relativamente poucos.</code> | <code>[-0.2655501961708069, 0.2715710997581482, 0.13977409899234772, 0.007375418208539486, -0.09395705163478851, ...]</code> |
| <code>One thing I often ask about is ancient Greek and how this relates.</code> | <code>Uma coisa sobre a qual eu pergunto com frequencia é grego antigo e como ele se relaciona a isto.</code> | <code>[0.34961527585983276, -0.01806497573852539, 0.06103038787841797, 0.11750973761081696, -0.34720802307128906, ...]</code> |
| <code>See, the thing we're doing right now is we're forcing people to learn mathematics.</code> | <code>Vejam, o que estamos fazendo agora, é que estamos forçando as pessoas a aprender matemática.</code> | <code>[0.031645823270082474, -0.1787087768316269, -0.30170342326164246, 0.1304805874824524, -0.29176947474479675, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-es
* Dataset: en-es
* Size: 6,889,042 training samples
* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | english | non_english | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | <ul><li>min: 4 tokens</li><li>mean: 24.04 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 35.11 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
* Samples:
| english | non_english | label |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|
| <code>And then there are certain conceptual things that can also benefit from hand calculating, but I think they're relatively small in number.</code> | <code>Y luego hay ciertas aspectos conceptuales que pueden beneficiarse del cálculo a mano pero creo que son relativamente pocos.</code> | <code>[-0.2655501961708069, 0.2715710997581482, 0.13977409899234772, 0.007375418208539486, -0.09395705163478851, ...]</code> |
| <code>One thing I often ask about is ancient Greek and how this relates.</code> | <code>Algo que pregunto a menudo es sobre el griego antiguo y cómo se relaciona.</code> | <code>[0.34961527585983276, -0.01806497573852539, 0.06103038787841797, 0.11750973761081696, -0.34720802307128906, ...]</code> |
| <code>See, the thing we're doing right now is we're forcing people to learn mathematics.</code> | <code>Vean, lo que estamos haciendo ahora es forzar a la gente a aprender matemáticas.</code> | <code>[0.031645823270082474, -0.1787087768316269, -0.30170342326164246, 0.1304805874824524, -0.29176947474479675, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-pt
* Dataset: en-pt
* Size: 6,636,095 training samples
* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | english | non_english | label |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | <ul><li>min: 4 tokens</li><li>mean: 23.5 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 35.23 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
* Samples:
| english | non_english | label |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|
| <code>And the country that does this first will, in my view, leapfrog others in achieving a new economy even, an improved economy, an improved outlook.</code> | <code>E o país que fizer isto primeiro vai, na minha opinião, ultrapassar outros em alcançar uma nova economia até uma economia melhorada, uma visão melhorada.</code> | <code>[-0.1395619511604309, -0.1703503578901291, 0.21396367251873016, -0.29212212562561035, 0.2718254327774048, ...]</code> |
| <code>In fact, I even talk about us moving from what we often call now the "knowledge economy" to what we might call a "computational knowledge economy," where high-level math is integral to what everyone does in the way that knowledge currently is.</code> | <code>De facto, eu até falo de mudarmos do que chamamos hoje a economia do conhecimento para o que poderemos chamar a economia do conhecimento computacional, onde a matemática de alto nível está integrada no que toda a gente faz da forma que o conhecimento actualmente está.</code> | <code>[-0.002996142255142331, -0.34310653805732727, -0.09672430157661438, 0.23709852993488312, -0.013354267925024033, ...]</code> |
| <code>We can engage so many more students with this, and they can have a better time doing it.</code> | <code>Podemos cativar tantos mais estudantes com isto, e eles podem divertir-se mais a fazê-lo.</code> | <code>[0.2670706808567047, 0.09549400955438614, -0.17057836055755615, -0.2152799665927887, -0.2832679748535156, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Evaluation Datasets
#### en-pt-br
* Dataset: [en-pt-br](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [0c70bc6](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/0c70bc6714efb1df12f8a16b9056e4653563d128)
* Size: 992 evaluation samples
* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
* Approximate statistics based on the first 992 samples:
| | english | non_english | label |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | <ul><li>min: 4 tokens</li><li>mean: 24.37 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 38.6 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
* Samples:
| english | non_english | label |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------|
| <code>Thank you so much, Chris.</code> | <code>Muito obrigado, Chris.</code> | <code>[-0.1929965764284134, 0.051721055060625076, 0.3780047297477722, -0.20386895537376404, -0.2625442445278168, ...]</code> |
| <code>And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful.</code> | <code>É realmente uma grande honra ter a oportunidade de estar neste palco pela segunda vez. Estou muito agradecido.</code> | <code>[0.04667849838733673, 0.16640479862689972, 0.05405835807323456, -0.2507464587688446, -0.5305444002151489, ...]</code> |
| <code>I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night.</code> | <code>Eu fui muito aplaudido por esta conferência e quero agradecer a todos pelos muitos comentários delicados sobre o que eu tinha a dizer naquela noite.</code> | <code>[0.04410325363278389, 0.2660813629627228, -0.013608227483928204, 0.08376947790384293, 0.22691071033477783, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-es
* Dataset: en-es
* Size: 9,990 evaluation samples
* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | english | non_english | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | <ul><li>min: 4 tokens</li><li>mean: 24.39 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 36.38 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
* Samples:
| english | non_english | label |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|
| <code>Thank you so much, Chris.</code> | <code>Muchas gracias Chris.</code> | <code>[-0.19299663603305817, 0.051721103489398956, 0.37800467014312744, -0.20386885106563568, -0.2625444531440735, ...]</code> |
| <code>And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful.</code> | <code>Y es en verdad un gran honor tener la oportunidad de venir a este escenario por segunda vez. Estoy extremadamente agradecido.</code> | <code>[0.04667845368385315, 0.16640479862689972, 0.05405828729271889, -0.25074639916419983, -0.5305443406105042, ...]</code> |
| <code>I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night.</code> | <code>He quedado conmovido por esta conferencia, y deseo agradecer a todos ustedes sus amables comentarios acerca de lo que tenía que decir la otra noche.</code> | <code>[0.04410335421562195, 0.2660813629627228, -0.01360794436186552, 0.08376938849687576, 0.22691065073013306, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-pt
* Dataset: en-pt
* Size: 9,992 evaluation samples
* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | english | non_english | label |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | <ul><li>min: 4 tokens</li><li>mean: 23.82 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 36.7 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
* Samples:
| english | non_english | label |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|
| <code>Thank you so much, Chris.</code> | <code>Muito obrigado, Chris.</code> | <code>[-0.19299663603305817, 0.051721103489398956, 0.37800467014312744, -0.20386885106563568, -0.2625444531440735, ...]</code> |
| <code>And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful.</code> | <code>É realmente uma grande honra ter a oportunidade de pisar este palco pela segunda vez. Estou muito agradecido.</code> | <code>[0.04667849838733673, 0.16640479862689972, 0.05405835807323456, -0.2507464587688446, -0.5305444002151489, ...]</code> |
| <code>I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night.</code> | <code>Fiquei muito impressionado com esta conferência e quero agradecer a todos os imensos comentários simpáticos sobre o que eu tinha a dizer naquela noite.</code> | <code>[0.04410335421562195, 0.2660813629627228, -0.01360794436186552, 0.08376938849687576, 0.22691065073013306, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `gradient_accumulation_steps`: 8
- `num_train_epochs`: 6
- `warmup_ratio`: 0.15
- `bf16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `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`: 1.0
- `num_train_epochs`: 6
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.15
- `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`: True
- `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`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | en-pt-br loss | en-es loss | en-pt loss | en-pt-br_negative_mse | en-pt-br_mean_accuracy | en-es_negative_mse | en-es_mean_accuracy | sts17-es-en-test_spearman_cosine | en-pt_negative_mse | en-pt_mean_accuracy |
|:------:|:-----:|:-------------:|:-------------:|:----------:|:----------:|:---------------------:|:----------------------:|:------------------:|:-------------------:|:--------------------------------:|:------------------:|:-------------------:|
| 0.0074 | 100 | 0.0512 | - | - | - | - | - | - | - | - | - | - |
| 0.0147 | 200 | 0.0505 | - | - | - | - | - | - | - | - | - | - |
| 0.0221 | 300 | 0.0496 | - | - | - | - | - | - | - | - | - | - |
| 0.0294 | 400 | 0.0489 | - | - | - | - | - | - | - | - | - | - |
| 0.0368 | 500 | 0.0483 | - | - | - | - | - | - | - | - | - | - |
| 0.0441 | 600 | 0.0479 | - | - | - | - | - | - | - | - | - | - |
| 0.0515 | 700 | 0.0476 | - | - | - | - | - | - | - | - | - | - |
| 0.0588 | 800 | 0.0474 | - | - | - | - | - | - | - | - | - | - |
| 0.0662 | 900 | 0.0471 | - | - | - | - | - | - | - | - | - | - |
| 0.0735 | 1000 | 0.0469 | - | - | - | - | - | - | - | - | - | - |
| 0.0809 | 1100 | 0.0467 | - | - | - | - | - | - | - | - | - | - |
| 0.0882 | 1200 | 0.0464 | - | - | - | - | - | - | - | - | - | - |
| 0.0956 | 1300 | 0.0461 | - | - | - | - | - | - | - | - | - | - |
| 0.1029 | 1400 | 0.046 | - | - | - | - | - | - | - | - | - | - |
| 0.1103 | 1500 | 0.0458 | - | - | - | - | - | - | - | - | - | - |
| 0.1176 | 1600 | 0.0456 | - | - | - | - | - | - | - | - | - | - |
| 0.1250 | 1700 | 0.0455 | - | - | - | - | - | - | - | - | - | - |
| 0.1323 | 1800 | 0.0454 | - | - | - | - | - | - | - | - | - | - |
| 0.1397 | 1900 | 0.0452 | - | - | - | - | - | - | - | - | - | - |
| 0.1470 | 2000 | 0.0452 | 0.0441 | 0.0454 | 0.0455 | -4.785339 | 0.5978 | -4.9081144 | 0.5252 | 0.2460 | -4.929552 | 0.4744 |
| 0.1544 | 2100 | 0.0449 | - | - | - | - | - | - | - | - | - | - |
| 0.1617 | 2200 | 0.0449 | - | - | - | - | - | - | - | - | - | - |
| 0.1691 | 2300 | 0.0448 | - | - | - | - | - | - | - | - | - | - |
| 0.1764 | 2400 | 0.0447 | - | - | - | - | - | - | - | - | - | - |
| 0.1838 | 2500 | 0.0446 | - | - | - | - | - | - | - | - | - | - |
| 0.1911 | 2600 | 0.0445 | - | - | - | - | - | - | - | - | - | - |
| 0.1985 | 2700 | 0.0443 | - | - | - | - | - | - | - | - | - | - |
| 0.2058 | 2800 | 0.0443 | - | - | - | - | - | - | - | - | - | - |
| 0.2132 | 2900 | 0.0442 | - | - | - | - | - | - | - | - | - | - |
| 0.2205 | 3000 | 0.0441 | - | - | - | - | - | - | - | - | - | - |
| 0.2279 | 3100 | 0.0441 | - | - | - | - | - | - | - | - | - | - |
| 0.2352 | 3200 | 0.0439 | - | - | - | - | - | - | - | - | - | - |
| 0.2426 | 3300 | 0.0439 | - | - | - | - | - | - | - | - | - | - |
| 0.2499 | 3400 | 0.0439 | - | - | - | - | - | - | - | - | - | - |
| 0.2573 | 3500 | 0.0438 | - | - | - | - | - | - | - | - | - | - |
| 0.2646 | 3600 | 0.0437 | - | - | - | - | - | - | - | - | - | - |
| 0.2720 | 3700 | 0.0436 | - | - | - | - | - | - | - | - | - | - |
| 0.2793 | 3800 | 0.0435 | - | - | - | - | - | - | - | - | - | - |
| 0.2867 | 3900 | 0.0436 | - | - | - | - | - | - | - | - | - | - |
| 0.2940 | 4000 | 0.0435 | 0.0424 | 0.0437 | 0.0438 | -4.5627975 | 0.8054 | -4.6922235 | 0.7096 | 0.3575 | -4.715491 | 0.6680 |
| 0.3014 | 4100 | 0.0434 | - | - | - | - | - | - | - | - | - | - |
| 0.3087 | 4200 | 0.0432 | - | - | - | - | - | - | - | - | - | - |
| 0.3161 | 4300 | 0.0433 | - | - | - | - | - | - | - | - | - | - |
| 0.3234 | 4400 | 0.0432 | - | - | - | - | - | - | - | - | - | - |
| 0.3308 | 4500 | 0.0432 | - | - | - | - | - | - | - | - | - | - |
| 0.3381 | 4600 | 0.0431 | - | - | - | - | - | - | - | - | - | - |
| 0.3455 | 4700 | 0.0431 | - | - | - | - | - | - | - | - | - | - |
| 0.3528 | 4800 | 0.043 | - | - | - | - | - | - | - | - | - | - |
| 0.3602 | 4900 | 0.043 | - | - | - | - | - | - | - | - | - | - |
| 0.3675 | 5000 | 0.043 | - | - | - | - | - | - | - | - | - | - |
| 0.3749 | 5100 | 0.0429 | - | - | - | - | - | - | - | - | - | - |
| 0.3822 | 5200 | 0.0429 | - | - | - | - | - | - | - | - | - | - |
| 0.3896 | 5300 | 0.0427 | - | - | - | - | - | - | - | - | - | - |
| 0.3969 | 5400 | 0.0428 | - | - | - | - | - | - | - | - | - | - |
| 0.4043 | 5500 | 0.0428 | - | - | - | - | - | - | - | - | - | - |
| 0.4116 | 5600 | 0.0427 | - | - | - | - | - | - | - | - | - | - |
| 0.4190 | 5700 | 0.0426 | - | - | - | - | - | - | - | - | - | - |
| 0.4263 | 5800 | 0.0427 | - | - | - | - | - | - | - | - | - | - |
| 0.4337 | 5900 | 0.0427 | - | - | - | - | - | - | - | - | - | - |
| 0.4410 | 6000 | 0.0425 | 0.0414 | 0.0428 | 0.0429 | -4.415331 | 0.8997 | -4.566894 | 0.7969 | 0.4509 | -4.5856175 | 0.7684 |
| 0.4484 | 6100 | 0.0425 | - | - | - | - | - | - | - | - | - | - |
| 0.4557 | 6200 | 0.0425 | - | - | - | - | - | - | - | - | - | - |
| 0.4631 | 6300 | 0.0426 | - | - | - | - | - | - | - | - | - | - |
| 0.4704 | 6400 | 0.0424 | - | - | - | - | - | - | - | - | - | - |
| 0.4778 | 6500 | 0.0424 | - | - | - | - | - | - | - | - | - | - |
| 0.4851 | 6600 | 0.0424 | - | - | - | - | - | - | - | - | - | - |
| 0.4925 | 6700 | 0.0423 | - | - | - | - | - | - | - | - | - | - |
| 0.4998 | 6800 | 0.0425 | - | - | - | - | - | - | - | - | - | - |
| 0.5072 | 6900 | 0.0423 | - | - | - | - | - | - | - | - | - | - |
| 0.5145 | 7000 | 0.0422 | - | - | - | - | - | - | - | - | - | - |
| 0.5219 | 7100 | 0.0423 | - | - | - | - | - | - | - | - | - | - |
| 0.5292 | 7200 | 0.0422 | - | - | - | - | - | - | - | - | - | - |
| 0.5366 | 7300 | 0.0422 | - | - | - | - | - | - | - | - | - | - |
| 0.5439 | 7400 | 0.0422 | - | - | - | - | - | - | - | - | - | - |
| 0.5513 | 7500 | 0.0422 | - | - | - | - | - | - | - | - | - | - |
| 0.5586 | 7600 | 0.0421 | - | - | - | - | - | - | - | - | - | - |
| 0.5660 | 7700 | 0.0421 | - | - | - | - | - | - | - | - | - | - |
| 0.5733 | 7800 | 0.0421 | - | - | - | - | - | - | - | - | - | - |
| 0.5807 | 7900 | 0.0421 | - | - | - | - | - | - | - | - | - | - |
| 0.5880 | 8000 | 0.0421 | 0.0409 | 0.0423 | 0.0424 | -4.325846 | 0.9400 | -4.488157 | 0.8366 | 0.5334 | -4.5032544 | 0.8170 |
| 0.5954 | 8100 | 0.0421 | - | - | - | - | - | - | - | - | - | - |
| 0.6027 | 8200 | 0.042 | - | - | - | - | - | - | - | - | - | - |
| 0.6101 | 8300 | 0.042 | - | - | - | - | - | - | - | - | - | - |
| 0.6174 | 8400 | 0.042 | - | - | - | - | - | - | - | - | - | - |
| 0.6248 | 8500 | 0.0421 | - | - | - | - | - | - | - | - | - | - |
| 0.6321 | 8600 | 0.0419 | - | - | - | - | - | - | - | - | - | - |
| 0.6395 | 8700 | 0.042 | - | - | - | - | - | - | - | - | - | - |
| 0.6468 | 8800 | 0.0419 | - | - | - | - | - | - | - | - | - | - |
| 0.6542 | 8900 | 0.0419 | - | - | - | - | - | - | - | - | - | - |
| 0.6615 | 9000 | 0.0419 | - | - | - | - | - | - | - | - | - | - |
| 0.6689 | 9100 | 0.0418 | - | - | - | - | - | - | - | - | - | - |
| 0.6762 | 9200 | 0.0418 | - | - | - | - | - | - | - | - | - | - |
| 0.6836 | 9300 | 0.0418 | - | - | - | - | - | - | - | - | - | - |
| 0.6909 | 9400 | 0.0418 | - | - | - | - | - | - | - | - | - | - |
| 0.6983 | 9500 | 0.0418 | - | - | - | - | - | - | - | - | - | - |
| 0.7056 | 9600 | 0.0417 | - | - | - | - | - | - | - | - | - | - |
| 0.7130 | 9700 | 0.0417 | - | - | - | - | - | - | - | - | - | - |
| 0.7203 | 9800 | 0.0417 | - | - | - | - | - | - | - | - | - | - |
| 0.7277 | 9900 | 0.0417 | - | - | - | - | - | - | - | - | - | - |
| 0.7350 | 10000 | 0.0417 | 0.0405 | 0.0420 | 0.0420 | -4.2650604 | 0.9526 | -4.434938 | 0.8587 | 0.6200 | -4.4468904 | 0.8390 |
| 0.7424 | 10100 | 0.0416 | - | - | - | - | - | - | - | - | - | - |
| 0.7497 | 10200 | 0.0417 | - | - | - | - | - | - | - | - | - | - |
| 0.7571 | 10300 | 0.0416 | - | - | - | - | - | - | - | - | - | - |
| 0.7644 | 10400 | 0.0416 | - | - | - | - | - | - | - | - | - | - |
| 0.7718 | 10500 | 0.0416 | - | - | - | - | - | - | - | - | - | - |
| 0.7791 | 10600 | 0.0416 | - | - | - | - | - | - | - | - | - | - |
| 0.7865 | 10700 | 0.0416 | - | - | - | - | - | - | - | - | - | - |
| 0.7938 | 10800 | 0.0416 | - | - | - | - | - | - | - | - | - | - |
| 0.8012 | 10900 | 0.0415 | - | - | - | - | - | - | - | - | - | - |
| 0.8085 | 11000 | 0.0416 | - | - | - | - | - | - | - | - | - | - |
| 0.8159 | 11100 | 0.0415 | - | - | - | - | - | - | - | - | - | - |
| 0.8232 | 11200 | 0.0415 | - | - | - | - | - | - | - | - | - | - |
| 0.8306 | 11300 | 0.0415 | - | - | - | - | - | - | - | - | - | - |
| 0.8380 | 11400 | 0.0415 | - | - | - | - | - | - | - | - | - | - |
| 0.8453 | 11500 | 0.0415 | - | - | - | - | - | - | - | - | - | - |
| 0.8527 | 11600 | 0.0415 | - | - | - | - | - | - | - | - | - | - |
| 0.8600 | 11700 | 0.0414 | - | - | - | - | - | - | - | - | - | - |
| 0.8674 | 11800 | 0.0414 | - | - | - | - | - | - | - | - | - | - |
| 0.8747 | 11900 | 0.0414 | - | - | - | - | - | - | - | - | - | - |
| 0.8821 | 12000 | 0.0414 | 0.0402 | 0.0417 | 0.0417 | -4.2234926 | 0.9637 | -4.3952775 | 0.8716 | 0.6684 | -4.404395 | 0.8526 |
| 0.8894 | 12100 | 0.0414 | - | - | - | - | - | - | - | - | - | - |
| 0.8968 | 12200 | 0.0414 | - | - | - | - | - | - | - | - | - | - |
| 0.9041 | 12300 | 0.0414 | - | - | - | - | - | - | - | - | - | - |
| 0.9115 | 12400 | 0.0414 | - | - | - | - | - | - | - | - | - | - |
| 0.9188 | 12500 | 0.0413 | - | - | - | - | - | - | - | - | - | - |
| 0.9262 | 12600 | 0.0413 | - | - | - | - | - | - | - | - | - | - |
| 0.9335 | 12700 | 0.0414 | - | - | - | - | - | - | - | - | - | - |
| 0.9409 | 12800 | 0.0414 | - | - | - | - | - | - | - | - | - | - |
| 0.9482 | 12900 | 0.0413 | - | - | - | - | - | - | - | - | - | - |
| 0.9556 | 13000 | 0.0413 | - | - | - | - | - | - | - | - | - | - |
| 0.9629 | 13100 | 0.0413 | - | - | - | - | - | - | - | - | - | - |
| 0.9703 | 13200 | 0.0413 | - | - | - | - | - | - | - | - | - | - |
| 0.9776 | 13300 | 0.0413 | - | - | - | - | - | - | - | - | - | - |
| 0.9850 | 13400 | 0.0413 | - | - | - | - | - | - | - | - | - | - |
| 0.9923 | 13500 | 0.0412 | - | - | - | - | - | - | - | - | - | - |
| 0.9997 | 13600 | 0.0413 | - | - | - | - | - | - | - | - | - | - |
| 1.0070 | 13700 | 0.0413 | - | - | - | - | - | - | - | - | - | - |
| 1.0144 | 13800 | 0.0413 | - | - | - | - | - | - | - | - | - | - |
| 1.0217 | 13900 | 0.0412 | - | - | - | - | - | - | - | - | - | - |
| 1.0291 | 14000 | 0.0412 | 0.0400 | 0.0415 | 0.0416 | -4.194809 | 0.9682 | -4.36698 | 0.8798 | 0.6892 | -4.37619 | 0.8621 |
| 1.0364 | 14100 | 0.0412 | - | - | - | - | - | - | - | - | - | - |
| 1.0438 | 14200 | 0.0412 | - | - | - | - | - | - | - | - | - | - |
| 1.0511 | 14300 | 0.0412 | - | - | - | - | - | - | - | - | - | - |
| 1.0585 | 14400 | 0.0412 | - | - | - | - | - | - | - | - | - | - |
| 1.0658 | 14500 | 0.0412 | - | - | - | - | - | - | - | - | - | - |
| 1.0732 | 14600 | 0.0412 | - | - | - | - | - | - | - | - | - | - |
| 1.0805 | 14700 | 0.0412 | - | - | - | - | - | - | - | - | - | - |
| 1.0879 | 14800 | 0.0412 | - | - | - | - | - | - | - | - | - | - |
| 1.0952 | 14900 | 0.0411 | - | - | - | - | - | - | - | - | - | - |
| 1.1026 | 15000 | 0.0411 | - | - | - | - | - | - | - | - | - | - |
| 1.1099 | 15100 | 0.0411 | - | - | - | - | - | - | - | - | - | - |
| 1.1173 | 15200 | 0.0411 | - | - | - | - | - | - | - | - | - | - |
| 1.1246 | 15300 | 0.0411 | - | - | - | - | - | - | - | - | - | - |
| 1.1320 | 15400 | 0.0411 | - | - | - | - | - | - | - | - | - | - |
| 1.1393 | 15500 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.1467 | 15600 | 0.0412 | - | - | - | - | - | - | - | - | - | - |
| 1.1540 | 15700 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.1614 | 15800 | 0.0411 | - | - | - | - | - | - | - | - | - | - |
| 1.1687 | 15900 | 0.0412 | - | - | - | - | - | - | - | - | - | - |
| 1.1761 | 16000 | 0.0411 | 0.0399 | 0.0414 | 0.0414 | -4.1725326 | 0.9728 | -4.347921 | 0.8845 | 0.7072 | -4.3567324 | 0.8679 |
| 1.1834 | 16100 | 0.0411 | - | - | - | - | - | - | - | - | - | - |
| 1.1908 | 16200 | 0.0411 | - | - | - | - | - | - | - | - | - | - |
| 1.1981 | 16300 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.2055 | 16400 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.2128 | 16500 | 0.0411 | - | - | - | - | - | - | - | - | - | - |
| 1.2202 | 16600 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.2275 | 16700 | 0.0411 | - | - | - | - | - | - | - | - | - | - |
| 1.2349 | 16800 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.2422 | 16900 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.2496 | 17000 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.2569 | 17100 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.2643 | 17200 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.2716 | 17300 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.2790 | 17400 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.2863 | 17500 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.2937 | 17600 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.3010 | 17700 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.3084 | 17800 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.3157 | 17900 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.3231 | 18000 | 0.041 | 0.0398 | 0.0413 | 0.0413 | -4.156324 | 0.9733 | -4.3334045 | 0.8877 | 0.7126 | -4.3421884 | 0.8721 |
| 1.3304 | 18100 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.3378 | 18200 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.3451 | 18300 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.3525 | 18400 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.3598 | 18500 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.3672 | 18600 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.3745 | 18700 | 0.041 | - | - | - | - | - | - | - | - | - | - |
| 1.3819 | 18800 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.3892 | 18900 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.3966 | 19000 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.4039 | 19100 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.4113 | 19200 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.4186 | 19300 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.4260 | 19400 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.4333 | 19500 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.4407 | 19600 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.4480 | 19700 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.4554 | 19800 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.4627 | 19900 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.4701 | 20000 | 0.0409 | 0.0397 | 0.0413 | 0.0413 | -4.1426473 | 0.9763 | -4.321089 | 0.8898 | 0.7271 | -4.3293304 | 0.8749 |
| 1.4774 | 20100 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.4848 | 20200 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.4921 | 20300 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.4995 | 20400 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.5068 | 20500 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.5142 | 20600 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.5215 | 20700 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.5289 | 20800 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.5362 | 20900 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.5436 | 21000 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.5509 | 21100 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.5583 | 21200 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.5656 | 21300 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.5730 | 21400 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.5803 | 21500 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.5877 | 21600 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.5950 | 21700 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.6024 | 21800 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.6097 | 21900 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.6171 | 22000 | 0.0408 | 0.0397 | 0.0412 | 0.0412 | -4.1324744 | 0.9768 | -4.312636 | 0.8920 | 0.7339 | -4.320667 | 0.8767 |
| 1.6244 | 22100 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.6318 | 22200 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.6391 | 22300 | 0.0409 | - | - | - | - | - | - | - | - | - | - |
| 1.6465 | 22400 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.6538 | 22500 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.6612 | 22600 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.6686 | 22700 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.6759 | 22800 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.6833 | 22900 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.6906 | 23000 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.6980 | 23100 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.7053 | 23200 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.7127 | 23300 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.7200 | 23400 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.7274 | 23500 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.7347 | 23600 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.7421 | 23700 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.7494 | 23800 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.7568 | 23900 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.7641 | 24000 | 0.0408 | 0.0396 | 0.0411 | 0.0412 | -4.1245446 | 0.9748 | -4.304793 | 0.8929 | 0.7383 | -4.312529 | 0.8785 |
| 1.7715 | 24100 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.7788 | 24200 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.7862 | 24300 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.7935 | 24400 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.8009 | 24500 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.8082 | 24600 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.8156 | 24700 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.8229 | 24800 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.8303 | 24900 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.8376 | 25000 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.8450 | 25100 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.8523 | 25200 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.8597 | 25300 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.8670 | 25400 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.8744 | 25500 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.8817 | 25600 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.8891 | 25700 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.8964 | 25800 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.9038 | 25900 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.9111 | 26000 | 0.0407 | 0.0396 | 0.0411 | 0.0411 | -4.115908 | 0.9793 | -4.2973475 | 0.8952 | 0.7492 | -4.3051677 | 0.8805 |
| 1.9185 | 26100 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.9258 | 26200 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.9332 | 26300 | 0.0408 | - | - | - | - | - | - | - | - | - | - |
| 1.9405 | 26400 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.9479 | 26500 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.9552 | 26600 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.9626 | 26700 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.9699 | 26800 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.9773 | 26900 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.9846 | 27000 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.9920 | 27100 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 1.9993 | 27200 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.0067 | 27300 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.0140 | 27400 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.0214 | 27500 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.0287 | 27600 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.0361 | 27700 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.0434 | 27800 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.0508 | 27900 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.0581 | 28000 | 0.0407 | 0.0395 | 0.0411 | 0.0411 | -4.1113133 | 0.9793 | -4.291781 | 0.8963 | 0.7503 | -4.3004823 | 0.8823 |
| 2.0655 | 28100 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.0728 | 28200 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.0802 | 28300 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.0875 | 28400 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.0949 | 28500 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.1022 | 28600 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.1096 | 28700 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.1169 | 28800 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.1243 | 28900 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.1316 | 29000 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.1390 | 29100 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.1463 | 29200 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.1537 | 29300 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.1610 | 29400 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.1684 | 29500 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.1757 | 29600 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.1831 | 29700 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.1904 | 29800 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.1978 | 29900 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.2051 | 30000 | 0.0406 | 0.0395 | 0.0410 | 0.0410 | -4.103692 | 0.9783 | -4.286089 | 0.8967 | 0.7587 | -4.2947936 | 0.8821 |
| 2.2125 | 30100 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.2198 | 30200 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.2272 | 30300 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.2345 | 30400 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.2419 | 30500 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.2492 | 30600 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.2566 | 30700 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.2639 | 30800 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.2713 | 30900 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.2786 | 31000 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.2860 | 31100 | 0.0407 | - | - | - | - | - | - | - | - | - | - |
| 2.2933 | 31200 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.3007 | 31300 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.3080 | 31400 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.3154 | 31500 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.3227 | 31600 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.3301 | 31700 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.3374 | 31800 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.3448 | 31900 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.3521 | 32000 | 0.0406 | 0.0394 | 0.0410 | 0.0410 | -4.0995665 | 0.9788 | -4.282365 | 0.8976 | 0.7618 | -4.291147 | 0.8826 |
| 2.3595 | 32100 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.3668 | 32200 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.3742 | 32300 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.3815 | 32400 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.3889 | 32500 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.3962 | 32600 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.4036 | 32700 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.4109 | 32800 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.4183 | 32900 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.4256 | 33000 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.4330 | 33100 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.4403 | 33200 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.4477 | 33300 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.4550 | 33400 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.4624 | 33500 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.4697 | 33600 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.4771 | 33700 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.4844 | 33800 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.4918 | 33900 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.4992 | 34000 | 0.0406 | 0.0394 | 0.0410 | 0.0410 | -4.0955496 | 0.9798 | -4.278625 | 0.8985 | 0.7684 | -4.2872252 | 0.8832 |
| 2.5065 | 34100 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.5139 | 34200 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.5212 | 34300 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.5286 | 34400 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.5359 | 34500 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.5433 | 34600 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.5506 | 34700 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.5580 | 34800 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.5653 | 34900 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.5727 | 35000 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.5800 | 35100 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.5874 | 35200 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.5947 | 35300 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.6021 | 35400 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.6094 | 35500 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.6168 | 35600 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.6241 | 35700 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.6315 | 35800 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.6388 | 35900 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.6462 | 36000 | 0.0405 | 0.0394 | 0.0410 | 0.0410 | -4.091509 | 0.9808 | -4.2755327 | 0.8986 | 0.7703 | -4.283869 | 0.8847 |
| 2.6535 | 36100 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.6609 | 36200 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.6682 | 36300 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.6756 | 36400 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.6829 | 36500 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.6903 | 36600 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.6976 | 36700 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.7050 | 36800 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.7123 | 36900 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.7197 | 37000 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.7270 | 37100 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.7344 | 37200 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.7417 | 37300 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.7491 | 37400 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.7564 | 37500 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.7638 | 37600 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.7711 | 37700 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.7785 | 37800 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.7858 | 37900 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.7932 | 38000 | 0.0405 | 0.0394 | 0.0409 | 0.0410 | -4.0881968 | 0.9819 | -4.2728844 | 0.8990 | 0.7717 | -4.281445 | 0.8851 |
| 2.8005 | 38100 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.8079 | 38200 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.8152 | 38300 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.8226 | 38400 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.8299 | 38500 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.8373 | 38600 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.8446 | 38700 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.8520 | 38800 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.8593 | 38900 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.8667 | 39000 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 2.8740 | 39100 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.8814 | 39200 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.8887 | 39300 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.8961 | 39400 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.9034 | 39500 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.9108 | 39600 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.9181 | 39700 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.9255 | 39800 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.9328 | 39900 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 2.9402 | 40000 | 0.0405 | 0.0393 | 0.0409 | 0.0409 | -4.0840244 | 0.9824 | -4.2690845 | 0.9001 | 0.7744 | -4.277667 | 0.8853 |
| 2.9475 | 40100 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.9549 | 40200 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.9622 | 40300 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.9696 | 40400 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.9769 | 40500 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.9843 | 40600 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.9916 | 40700 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 2.9990 | 40800 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.0063 | 40900 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.0137 | 41000 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.0210 | 41100 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.0284 | 41200 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.0357 | 41300 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.0431 | 41400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.0504 | 41500 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.0578 | 41600 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.0651 | 41700 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.0725 | 41800 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.0798 | 41900 | 0.0406 | - | - | - | - | - | - | - | - | - | - |
| 3.0872 | 42000 | 0.0405 | 0.0393 | 0.0409 | 0.0409 | -4.0831747 | 0.9819 | -4.267492 | 0.9003 | 0.7798 | -4.2760205 | 0.8860 |
| 3.0945 | 42100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.1019 | 42200 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.1092 | 42300 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.1166 | 42400 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.1239 | 42500 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.1313 | 42600 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.1386 | 42700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.1460 | 42800 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.1533 | 42900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.1607 | 43000 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.1680 | 43100 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.1754 | 43200 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.1827 | 43300 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.1901 | 43400 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.1974 | 43500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.2048 | 43600 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.2121 | 43700 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.2195 | 43800 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.2268 | 43900 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.2342 | 44000 | 0.0405 | 0.0393 | 0.0409 | 0.0409 | -4.0802045 | 0.9824 | -4.2650237 | 0.9005 | 0.7727 | -4.2733293 | 0.8865 |
| 3.2415 | 44100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.2489 | 44200 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.2562 | 44300 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.2636 | 44400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.2709 | 44500 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.2783 | 44600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.2856 | 44700 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.2930 | 44800 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.3003 | 44900 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.3077 | 45000 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.3150 | 45100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.3224 | 45200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.3297 | 45300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.3371 | 45400 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.3445 | 45500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.3518 | 45600 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.3592 | 45700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.3665 | 45800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.3739 | 45900 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.3812 | 46000 | 0.0405 | 0.0393 | 0.0409 | 0.0409 | -4.078617 | 0.9829 | -4.2630715 | 0.9009 | 0.7804 | -4.271867 | 0.8867 |
| 3.3886 | 46100 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 3.3959 | 46200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.4033 | 46300 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.4106 | 46400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.4180 | 46500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.4253 | 46600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.4327 | 46700 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.4400 | 46800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.4474 | 46900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.4547 | 47000 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.4621 | 47100 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.4694 | 47200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.4768 | 47300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.4841 | 47400 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.4915 | 47500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.4988 | 47600 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.5062 | 47700 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.5135 | 47800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.5209 | 47900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.5282 | 48000 | 0.0404 | 0.0393 | 0.0409 | 0.0409 | -4.075687 | 0.9824 | -4.260863 | 0.9012 | 0.7801 | -4.2693996 | 0.8870 |
| 3.5356 | 48100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.5429 | 48200 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.5503 | 48300 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.5576 | 48400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.5650 | 48500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.5723 | 48600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.5797 | 48700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.5870 | 48800 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.5944 | 48900 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.6017 | 49000 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.6091 | 49100 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.6164 | 49200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.6238 | 49300 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.6311 | 49400 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.6385 | 49500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.6458 | 49600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.6532 | 49700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.6605 | 49800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.6679 | 49900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.6752 | 50000 | 0.0404 | 0.0393 | 0.0409 | 0.0409 | -4.074522 | 0.9824 | -4.2595944 | 0.9015 | 0.7800 | -4.2678404 | 0.8869 |
| 3.6826 | 50100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.6899 | 50200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.6973 | 50300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.7046 | 50400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.7120 | 50500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.7193 | 50600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.7267 | 50700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.7340 | 50800 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.7414 | 50900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.7487 | 51000 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.7561 | 51100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.7634 | 51200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.7708 | 51300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.7781 | 51400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.7855 | 51500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.7928 | 51600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.8002 | 51700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.8075 | 51800 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.8149 | 51900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.8222 | 52000 | 0.0405 | 0.0393 | 0.0408 | 0.0409 | -4.0725245 | 0.9829 | -4.258272 | 0.9010 | 0.7804 | -4.2663693 | 0.8873 |
| 3.8296 | 52100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.8369 | 52200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.8443 | 52300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.8516 | 52400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.8590 | 52500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.8663 | 52600 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 3.8737 | 52700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.8810 | 52800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.8884 | 52900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.8957 | 53000 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.9031 | 53100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.9104 | 53200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.9178 | 53300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.9251 | 53400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.9325 | 53500 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.9398 | 53600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.9472 | 53700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.9545 | 53800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.9619 | 53900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.9692 | 54000 | 0.0404 | 0.0393 | 0.0408 | 0.0408 | -4.0710397 | 0.9834 | -4.2558665 | 0.9017 | 0.7822 | -4.264339 | 0.8871 |
| 3.9766 | 54100 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 3.9839 | 54200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.9913 | 54300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 3.9986 | 54400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.0060 | 54500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.0133 | 54600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.0207 | 54700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.0280 | 54800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.0354 | 54900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.0427 | 55000 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.0501 | 55100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.0574 | 55200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.0648 | 55300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.0721 | 55400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.0795 | 55500 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 4.0868 | 55600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.0942 | 55700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.1015 | 55800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.1089 | 55900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.1162 | 56000 | 0.0404 | 0.0393 | 0.0408 | 0.0408 | -4.0708294 | 0.9839 | -4.255319 | 0.9020 | 0.7847 | -4.2634797 | 0.8880 |
| 4.1236 | 56100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.1309 | 56200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.1383 | 56300 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.1456 | 56400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.1530 | 56500 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.1603 | 56600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.1677 | 56700 | 0.0405 | - | - | - | - | - | - | - | - | - | - |
| 4.1751 | 56800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.1824 | 56900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.1898 | 57000 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.1971 | 57100 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.2045 | 57200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.2118 | 57300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.2192 | 57400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.2265 | 57500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.2339 | 57600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.2412 | 57700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.2486 | 57800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.2559 | 57900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.2633 | 58000 | 0.0403 | 0.0392 | 0.0408 | 0.0408 | -4.0691004 | 0.9829 | -4.253834 | 0.9023 | 0.7827 | -4.2621255 | 0.8880 |
| 4.2706 | 58100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.2780 | 58200 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.2853 | 58300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.2927 | 58400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.3000 | 58500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.3074 | 58600 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.3147 | 58700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.3221 | 58800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.3294 | 58900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.3368 | 59000 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.3441 | 59100 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.3515 | 59200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.3588 | 59300 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.3662 | 59400 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.3735 | 59500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.3809 | 59600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.3882 | 59700 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.3956 | 59800 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.4029 | 59900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.4103 | 60000 | 0.0403 | 0.0392 | 0.0408 | 0.0408 | -4.067449 | 0.9824 | -4.252402 | 0.9026 | 0.7815 | -4.260964 | 0.8878 |
| 4.4176 | 60100 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.4250 | 60200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.4323 | 60300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.4397 | 60400 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.4470 | 60500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.4544 | 60600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.4617 | 60700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.4691 | 60800 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.4764 | 60900 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.4838 | 61000 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.4911 | 61100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.4985 | 61200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.5058 | 61300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.5132 | 61400 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.5205 | 61500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.5279 | 61600 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.5352 | 61700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.5426 | 61800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.5499 | 61900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.5573 | 62000 | 0.0403 | 0.0392 | 0.0408 | 0.0408 | -4.06687 | 0.9839 | -4.2519274 | 0.9028 | 0.7839 | -4.260144 | 0.8884 |
| 4.5646 | 62100 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.5720 | 62200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.5793 | 62300 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.5867 | 62400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.5940 | 62500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.6014 | 62600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.6087 | 62700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.6161 | 62800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.6234 | 62900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.6308 | 63000 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.6381 | 63100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.6455 | 63200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.6528 | 63300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.6602 | 63400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.6675 | 63500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.6749 | 63600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.6822 | 63700 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.6896 | 63800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.6969 | 63900 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.7043 | 64000 | 0.0403 | 0.0392 | 0.0408 | 0.0408 | -4.0657706 | 0.9829 | -4.251195 | 0.9026 | 0.7835 | -4.2593575 | 0.8881 |
| 4.7116 | 64100 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.7190 | 64200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.7263 | 64300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.7337 | 64400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.7410 | 64500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.7484 | 64600 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.7557 | 64700 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.7631 | 64800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.7704 | 64900 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.7778 | 65000 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.7851 | 65100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.7925 | 65200 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.7998 | 65300 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.8072 | 65400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.8145 | 65500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.8219 | 65600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.8292 | 65700 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.8366 | 65800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.8439 | 65900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.8513 | 66000 | 0.0404 | 0.0392 | 0.0408 | 0.0408 | -4.0652847 | 0.9834 | -4.2506175 | 0.9028 | 0.7839 | -4.2587624 | 0.8887 |
| 4.8586 | 66100 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.8660 | 66200 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.8733 | 66300 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.8807 | 66400 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.8880 | 66500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.8954 | 66600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.9027 | 66700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.9101 | 66800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.9174 | 66900 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.9248 | 67000 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.9321 | 67100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.9395 | 67200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.9468 | 67300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.9542 | 67400 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.9615 | 67500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.9689 | 67600 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.9762 | 67700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.9836 | 67800 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 4.9909 | 67900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 4.9983 | 68000 | 0.0404 | 0.0392 | 0.0408 | 0.0408 | -4.0641384 | 0.9834 | -4.2498612 | 0.9028 | 0.7849 | -4.258079 | 0.8888 |
| 5.0057 | 68100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.0130 | 68200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.0204 | 68300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.0277 | 68400 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.0351 | 68500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.0424 | 68600 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.0498 | 68700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.0571 | 68800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.0645 | 68900 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.0718 | 69000 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.0792 | 69100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.0865 | 69200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.0939 | 69300 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.1012 | 69400 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.1086 | 69500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.1159 | 69600 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.1233 | 69700 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.1306 | 69800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.1380 | 69900 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.1453 | 70000 | 0.0404 | 0.0392 | 0.0408 | 0.0408 | -4.0635867 | 0.9829 | -4.2490883 | 0.9027 | 0.7853 | -4.2572694 | 0.8888 |
| 5.1527 | 70100 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.1600 | 70200 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.1674 | 70300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.1747 | 70400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.1821 | 70500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.1894 | 70600 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.1968 | 70700 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.2041 | 70800 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.2115 | 70900 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.2188 | 71000 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.2262 | 71100 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.2335 | 71200 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.2409 | 71300 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.2482 | 71400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.2556 | 71500 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.2629 | 71600 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.2703 | 71700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.2776 | 71800 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.2850 | 71900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.2923 | 72000 | 0.0403 | 0.0392 | 0.0408 | 0.0408 | -4.0629997 | 0.9829 | -4.248385 | 0.9029 | 0.7867 | -4.256695 | 0.8886 |
| 5.2997 | 72100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.3070 | 72200 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.3144 | 72300 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.3217 | 72400 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.3291 | 72500 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.3364 | 72600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.3438 | 72700 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.3511 | 72800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.3585 | 72900 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.3658 | 73000 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.3732 | 73100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.3805 | 73200 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.3879 | 73300 | 0.0402 | - | - | - | - | - | - | - | - | - | - |
| 5.3952 | 73400 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.4026 | 73500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.4099 | 73600 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.4173 | 73700 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.4246 | 73800 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.4320 | 73900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.4393 | 74000 | 0.0403 | 0.0392 | 0.0408 | 0.0408 | -4.0628495 | 0.9834 | -4.2482247 | 0.9030 | 0.7844 | -4.256557 | 0.8888 |
| 5.4467 | 74100 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.4540 | 74200 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.4614 | 74300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.4687 | 74400 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.4761 | 74500 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.4834 | 74600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.4908 | 74700 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.4981 | 74800 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.5055 | 74900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.5128 | 75000 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.5202 | 75100 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.5275 | 75200 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.5349 | 75300 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.5422 | 75400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.5496 | 75500 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.5569 | 75600 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.5643 | 75700 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.5716 | 75800 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.5790 | 75900 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.5863 | 76000 | 0.0404 | 0.0392 | 0.0408 | 0.0408 | -4.062086 | 0.9829 | -4.247803 | 0.9032 | 0.7871 | -4.2560315 | 0.8889 |
| 5.5937 | 76100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.6010 | 76200 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.6084 | 76300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.6157 | 76400 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.6231 | 76500 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.6304 | 76600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.6378 | 76700 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.6451 | 76800 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.6525 | 76900 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.6598 | 77000 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.6672 | 77100 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.6745 | 77200 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.6819 | 77300 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.6892 | 77400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.6966 | 77500 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.7039 | 77600 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.7113 | 77700 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.7186 | 77800 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.7260 | 77900 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.7333 | 78000 | 0.0404 | 0.0392 | 0.0408 | 0.0408 | -4.062058 | 0.9834 | -4.247644 | 0.9029 | 0.7858 | -4.2557683 | 0.8888 |
| 5.7407 | 78100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.7480 | 78200 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.7554 | 78300 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.7627 | 78400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.7701 | 78500 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.7774 | 78600 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.7848 | 78700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.7921 | 78800 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.7995 | 78900 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.8068 | 79000 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.8142 | 79100 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.8215 | 79200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.8289 | 79300 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.8363 | 79400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.8436 | 79500 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.8510 | 79600 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.8583 | 79700 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.8657 | 79800 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.8730 | 79900 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.8804 | 80000 | 0.0403 | 0.0392 | 0.0408 | 0.0408 | -4.0617065 | 0.9834 | -4.247319 | 0.9030 | 0.7862 | -4.255536 | 0.8888 |
| 5.8877 | 80100 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.8951 | 80200 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.9024 | 80300 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.9098 | 80400 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.9171 | 80500 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.9245 | 80600 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.9318 | 80700 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.9392 | 80800 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.9465 | 80900 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.9539 | 81000 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.9612 | 81100 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.9686 | 81200 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.9759 | 81300 | 0.0404 | - | - | - | - | - | - | - | - | - | - |
| 5.9833 | 81400 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.9906 | 81500 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
| 5.9980 | 81600 | 0.0403 | - | - | - | - | - | - | - | - | - | - |
</details>
### Framework Versions
- Python: 3.12.7
- Sentence Transformers: 3.3.0
- Transformers: 4.46.2
- PyTorch: 2.5.1+cu124
- Accelerate: 1.1.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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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