All MiniLM L6 Multilingual V2 En Es Pt Pt Br V2
这是一个多语言句子嵌入模型,支持英语、西班牙语和葡萄牙语,可将文本映射到384维向量空间,适用于语义相似度计算等任务。
下载量 1,957
发布时间 : 1/8/2025
模型简介
该模型基于sentence-transformers框架微调而来,专门用于处理英语、西班牙语和葡萄牙语文本的语义理解任务,如语义搜索、文本分类和聚类分析。
模型特点
多语言支持
专门针对英语、西班牙语和葡萄牙语进行了优化,能有效处理这三种语言的语义理解任务。
高效向量表示
将句子和段落映射到384维稠密向量空间,便于进行高效的相似度计算和语义分析。
高准确率翻译
在英语-葡萄牙语翻译任务中达到98.34%的平均准确率,英语-西班牙语翻译达到90.30%的准确率。
模型能力
语义文本相似度计算
跨语言语义搜索
复述挖掘
文本分类
文本聚类
多语言文本处理
使用案例
信息检索
跨语言文档搜索
使用相同的语义向量空间搜索不同语言的相似文档
高准确率的跨语言匹配能力
内容管理
多语言内容去重
识别不同语言表达的相同内容
有效减少冗余内容
🚀 基于 sentence-transformers/paraphrase-MiniLM-L6-v2 的句子转换器
本模型是基于 sentence-transformers 框架,在 en-pt-br、en-es 和 en-pt 数据集上对 sentence-transformers/paraphrase-MiniLM-L6-v2 进行微调得到的。它能够将句子和段落映射到一个 384 维的密集向量空间,可用于语义文本相似度计算、语义搜索、释义挖掘、文本分类、聚类等任务。
✨ 主要特性
- 多语言支持:支持英语(en)、多语言(multilingual)、西班牙语(es)和葡萄牙语(pt)。
- 高效映射:能够将句子和段落快速准确地映射到 384 维的密集向量空间。
- 广泛应用:可用于多种自然语言处理任务,如语义文本相似度计算、语义搜索、释义挖掘、文本分类、聚类等。
📦 安装指南
首先,安装 Sentence Transformers 库:
pip install -U sentence-transformers
💻 使用示例
基础用法
from sentence_transformers import SentenceTransformer
# 从 🤗 Hub 下载模型
model = SentenceTransformer("jvanhoof/all-MiniLM-L6-multilingual-v2-en-es-pt-pt-br")
# 运行推理
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]
# 获取嵌入向量的相似度分数
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
📚 详细文档
模型详情
模型描述
属性 | 详情 |
---|---|
模型类型 | 句子转换器 |
基础模型 | sentence-transformers/paraphrase-MiniLM-L6-v2 |
最大序列长度 | 128 个词元 |
输出维度 | 384 维 |
相似度函数 | 余弦相似度 |
训练数据集 | en-pt-br、en-es、en-pt |
支持语言 | en、multilingual、es、pt |
模型来源
- 文档:Sentence Transformers 文档
- 仓库:GitHub 上的 Sentence Transformers
- Hugging Face:Hugging Face 上的 Sentence Transformers
完整模型架构
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()
)
评估指标
知识蒸馏
- 数据集:
en-pt-br
、en-es
和en-pt
- 评估方法:使用
MSEEvaluator
进行评估
指标 | en-pt-br | en-es | en-pt |
---|---|---|---|
负均方误差 | -4.0617 | -4.2473 | -4.2555 |
翻译
- 数据集:
en-pt-br
、en-es
和en-pt
- 评估方法:使用
TranslationEvaluator
进行评估
指标 | en-pt-br | en-es | en-pt |
---|---|---|---|
源到目标准确率 | 0.9859 | 0.908 | 0.8951 |
目标到源准确率 | 0.9808 | 0.898 | 0.8824 |
平均准确率 | 0.9834 | 0.903 | 0.8888 |
语义相似度
- 数据集:
sts17-es-en-test
- 评估方法:使用
EmbeddingSimilarityEvaluator
进行评估
指标 | 值 |
---|---|
皮尔逊余弦相似度 | 0.7714 |
斯皮尔曼余弦相似度 | 0.7862 |
训练详情
训练数据集
en-pt-br
- 数据集:en-pt-br(版本 0c70bc6)
- 大小:405,807 个训练样本
- 列信息:
english
、non_english
和label
- 近似统计信息(基于前 1000 个样本):
| | 英语 | 非英语 | 标签 |
|------|------|------|------|
| 类型 | 字符串 | 字符串 | 列表 |
| 详情 |
- 最小:4 个词元
- 平均:23.98 个词元
- 最大:128 个词元
- 最小:6 个词元
- 平均:36.86 个词元
- 最大:128 个词元
- 大小:384 个元素
- 样本示例:
| 英语 | 非英语 | 标签 |
|------|------|------|
|
And then there are certain conceptual things that can also benefit from hand calculating, but I think they're relatively small in number.
|E também existem alguns aspectos conceituais que também podem se beneficiar do cálculo manual, mas eu acho que eles são relativamente poucos.
|[-0.2655501961708069, 0.2715710997581482, 0.13977409899234772, 0.007375418208539486, -0.09395705163478851, ...]
| |One thing I often ask about is ancient Greek and how this relates.
|Uma coisa sobre a qual eu pergunto com frequencia é grego antigo e como ele se relaciona a isto.
|[0.34961527585983276, -0.01806497573852539, 0.06103038787841797, 0.11750973761081696, -0.34720802307128906, ...]
| |See, the thing we're doing right now is we're forcing people to learn mathematics.
|Vejam, o que estamos fazendo agora, é que estamos forçando as pessoas a aprender matemática.
|[0.031645823270082474, -0.1787087768316269, -0.30170342326164246, 0.1304805874824524, -0.29176947474479675, ...]
| - 损失函数:
MSELoss
en-es
- 数据集:en-es
- 大小:6,889,042 个训练样本
- 列信息:
english
、non_english
和label
- 近似统计信息(基于前 1000 个样本):
| | 英语 | 非英语 | 标签 |
|------|------|------|------|
| 类型 | 字符串 | 字符串 | 列表 |
| 详情 |
- 最小:4 个词元
- 平均:24.04 个词元
- 最大:128 个词元
- 最小:5 个词元
- 平均:35.11 个词元
- 最大:128 个词元
- 大小:384 个元素
- 样本示例:
| 英语 | 非英语 | 标签 |
|------|------|------|
|
And then there are certain conceptual things that can also benefit from hand calculating, but I think they're relatively small in number.
|Y luego hay ciertas aspectos conceptuales que pueden beneficiarse del cálculo a mano pero creo que son relativamente pocos.
|[-0.2655501961708069, 0.2715710997581482, 0.13977409899234772, 0.007375418208539486, -0.09395705163478851, ...]
| |One thing I often ask about is ancient Greek and how this relates.
|Algo que pregunto a menudo es sobre el griego antiguo y cómo se relaciona.
|[0.34961527585983276, -0.01806497573852539, 0.06103038787841797, 0.11750973761081696, -0.34720802307128906, ...]
| |See, the thing we're doing right now is we're forcing people to learn mathematics.
|Vean, lo que estamos haciendo ahora es forzar a la gente a aprender matemáticas.
|[0.031645823270082474, -0.1787087768316269, -0.30170342326164246, 0.1304805874824524, -0.29176947474479675, ...]
| - 损失函数:
MSELoss
en-pt
- 数据集:en-pt
- 大小:6,636,095 个训练样本
- 列信息:
english
、non_english
和label
- 近似统计信息(基于前 1000 个样本):
| | 英语 | 非英语 | 标签 |
|------|------|------|------|
| 类型 | 字符串 | 字符串 | 列表 |
| 详情 |
- 最小:4 个词元
- 平均:23.5 个词元
- 最大:128 个词元
- 最小:5 个词元
- 平均:35.23 个词元
- 最大:128 个词元
- 大小:384 个元素
- 样本示例:
| 英语 | 非英语 | 标签 |
|------|------|------|
|
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.
|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.
|[-0.1395619511604309, -0.1703503578901291, 0.21396367251873016, -0.29212212562561035, 0.2718254327774048, ...]
| |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.
|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.
|[-0.002996142255142331, -0.34310653805732727, -0.09672430157661438, 0.23709852993488312, -0.013354267925024033, ...]
| |We can engage so many more students with this, and they can have a better time doing it.
|Podemos cativar tantos mais estudantes com isto, e eles podem divertir-se mais a fazê-lo.
|[0.2670706808567047, 0.09549400955438614, -0.17057836055755615, -0.2152799665927887, -0.2832679748535156, ...]
| - 损失函数:
MSELoss
评估数据集
en-pt-br
- 数据集:en-pt-br(版本 0c70bc6)
- 大小:992 个评估样本
- 列信息:
english
、non_english
和label
- 近似统计信息(基于前 992 个样本):
| | 英语 | 非英语 | 标签 |
|------|------|------|------|
| 类型 | 字符串 | 字符串 | 列表 |
| 详情 |
- 最小:4 个词元
- 平均:24.37 个词元
- 最大:128 个词元
- 最小:5 个词元
- 平均:38.6 个词元
- 最大:128 个词元
- 大小:384 个元素
- 样本示例:
| 英语 | 非英语 | 标签 |
|------|------|------|
|
Thank you so much, Chris.
|Muito obrigado, Chris.
|[-0.1929965764284134, 0.051721055060625076, 0.3780047297477722, -0.20386895537376404, -0.2625442445278168, ...]
| |And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful.
|É realmente uma grande honra ter a oportunidade de estar neste palco pela segunda vez. Estou muito agradecido.
|[0.04667849838733673, 0.16640479862689972, 0.05405835807323456, -0.2507464587688446, -0.5305444002151489, ...]
| |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.
|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.
|[0.04410325363278389, 0.2660813629627228, -0.013608227483928204, 0.08376947790384293, 0.22691071033477783, ...]
| - 损失函数:
MSELoss
en-es
- 数据集:en-es
- 大小:9,990 个评估样本
- 列信息:
english
、non_english
和label
- 近似统计信息(基于前 1000 个样本):
| | 英语 | 非英语 | 标签 |
|------|------|------|------|
| 类型 | 字符串 | 字符串 | 列表 |
| 详情 |
- 最小:4 个词元
- 平均:24.39 个词元
- 最大:128 个词元
- 最小:4 个词元
- 平均:36.38 个词元
- 最大:128 个词元
- 大小:384 个元素
- 样本示例:
| 英语 | 非英语 | 标签 |
|------|------|------|
|
Thank you so much, Chris.
|Muchas gracias Chris.
|[-0.19299663603305817, 0.051721103489398956, 0.37800467014312744, -0.20386885106563568, -0.2625444531440735, ...]
| |And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful.
|Y es en verdad un gran honor tener la oportunidad de venir a este escenario por segunda vez. Estoy extremadamente agradecido.
|[0.04667845368385315, 0.16640479862689972, 0.05405828729271889, -0.25074639916419983, -0.5305443406105042, ...]
| |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.
|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.
|[0.04410335421562195, 0.2660813629627228, -0.01360794436186552, 0.08376938849687576, 0.22691065073013306, ...]
| - 损失函数:
MSELoss
en-pt
- 数据集:en-pt
- 大小:9,992 个评估样本
- 列信息:
english
、non_english
和label
- 近似统计信息(基于前 1000 个样本):
| | 英语 | 非英语 | 标签 |
|------|------|------|------|
| 类型 | 字符串 | 字符串 | 列表 |
| 详情 |
- 最小:4 个词元
- 平均:23.82 个词元
- 最大:128 个词元
- 最小:5 个词元
- 平均:36.7 个词元
- 最大:128 个词元
- 大小:384 个元素
- 样本示例:
| 英语 | 非英语 | 标签 |
|------|------|------|
|
Thank you so much, Chris.
|Muito obrigado, Chris.
|[-0.19299663603305817, 0.051721103489398956, 0.37800467014312744, -0.20386885106563568, -0.2625444531440735, ...]
| |And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful.
|É realmente uma grande honra ter a oportunidade de pisar este palco pela segunda vez. Estou muito agradecido.
|[0.04667849838733673, 0.16640479862689972, 0.05405835807323456, -0.2507464587688446, -0.5305444002151489, ...]
| |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.
|Fiquei muito impressionado com esta conferência e quero agradecer a todos os imensos comentários simpáticos sobre o que eu tinha a dizer naquela noite.
|[0.04410335421562195, 0.2660813629627228, -0.01360794436186552, 0.08376938849687576, 0.22691065073013306, ...]
| - 损失函数:
MSELoss
训练超参数
非默认超参数
eval_strategy
:stepsper_device_train_batch_size
:128per_device_eval_batch_size
:128gradient_accumulation_steps
:8num_train_epochs
:6warmup_ratio
:0.15bf16
:True
所有超参数
点击展开
overwrite_output_dir
:Falsedo_predict
:Falseeval_strategy
:stepsprediction_loss_only
:Trueper_device_train_batch_size
:128per_device_eval_batch_size
:128per_gpu_train_batch_size
:Noneper_gpu_eval_batch_size
:Nonegradient_accumulation_steps
:8eval_accumulation_steps
:Nonetorch_empty_cache_steps
:Nonelearning_rate
:5e-05weight_decay
:0.0adam_beta1
:0.9adam_beta2
:0.999adam_epsilon
:1e-08max_grad_norm
:1.0num_train_epochs
:6max_steps
:-1lr_scheduler_type
:linearlr_scheduler_kwargs
:{}warmup_ratio
:0.15warmup_steps
:0log_level
:passivelog_level_replica
:warninglog_on_each_node
:Truelogging_nan_inf_filter
:Truesave_safetensors
:Truesave_on_each_node
:Falsesave_only_model
:Falserestore_callback_states_from_checkpoint
:Falseno_cuda
:Falseuse_cpu
:Falseuse_mps_device
:Falseseed
:42data_seed
:Nonejit_mode_eval
:Falseuse_ipex
:Falsebf16
:Truefp16
:Falsefp16_opt_level
:O1half_precision_backend
:autobf16_full_eval
:Falsefp16_full_eval
:Falsetf32
:Nonelocal_rank
:0ddp_backend
:Nonetpu_num_cores
:Nonetpu_metrics_debug
:Falsedebug
:[]dataloader_drop_last
:Falsedataloader_num_workers
:0dataloader_prefetch_factor
:Nonepast_index
:-1disable_tqdm
:Falseremove_unused_columns
:Truelabel_names
:Noneload_best_model_at_end
:Falseignore_data_skip
:Falsefsdp
:[]fsdp_min_num_params
:0fsdp_config
:{'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
:Noneaccelerator_config
:{'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
:Nonelabel_smoothing_factor
:0.0optim
:adamw_torchoptim_args
:Noneadafactor
:Falsegroup_by_length
:Falselength_column_name
:lengthddp_find_unused_parameters
:Noneddp_bucket_cap_mb
:Noneddp_broadcast_buffers
:Falsedataloader_pin_memory
:Truedataloader_persistent_workers
:Falseskip_memory_metrics
:Trueuse_legacy_prediction_loop
:Falsepush_to_hub
:Falseresume_from_checkpoint
:Nonehub_model_id
:Nonehub_strategy
:every_savehub_private_repo
:Falsehub_always_push
:Falsegradient_checkpointing
:Falsegradient_checkpointing_kwargs
:Noneinclude_inputs_for_metrics
:Falseinclude_for_metrics
:[]eval_do_concat_batches
:Truefp16_backend
:autopush_to_hub_model_id
:Nonepush_to_hub_organization
:Nonemp_parameters
:auto_find_batch_size
:Falsefull_determinism
:Falsetorchdynamo
:Noneray_scope
:lastddp_timeout
:1800torch_compile
:Falsetorch_compile_backend
:Nonetorch_compile_mode
:Nonedispatch_batches
:Nonesplit_batches
:Noneinclude_tokens_per_second
:Falseinclude_num_input_tokens_seen
:Falseneftune_noise_alpha
:Noneoptim_target_modules
:Nonebatch_eval_metrics
:Falseeval_on_start
:Falseuse_liger_kernel
:Falseeval_use_gather_object
:Falseaverage_tokens_across_devices
:Falseprompts
:Nonebatch_sampler
:batch_samplermulti_dataset_batch_sampler
:proportional
训练日志
点击展开
轮数 | 步数 | 训练损失 | en-pt-br 损失 | en-es 损失 | en-pt 损失 | en-pt-br 负均方误差 | en-pt-br 平均准确率 | en-es 负均方误差 | en-es 平均准确率 | sts17-es-en-test 斯皮尔曼余弦相似度 | en-pt 负均方误差 | en-pt 平均准确率 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | - | - | - | - |
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