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+---
+base_model: FacebookAI/xlm-roberta-large
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:525972
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: ثلاثة كلاب، واحد منهم لديه كرة زرقاء.
+ sentences:
+ - هناك ثلاثة حيوانات.
+ - كل ثلاثة كلاب لديهم ألعاب حمراء
+ - أنثى تلعب كرة القدم
+- source_sentence: نسبة الكوليسترول في غمد الميالين
+ sentences:
+ - العديد من الخلايا الدبقية التي تصطف على طول محور عصبي مطلوبة لتكوين الميالين بالكامل
+ وعزل خلية عصبية طويلة. يتكون المايلين من حوالي 30٪ بروتين و 27٪ كوليسترول و 43٪
+ فوسفوليبيد ، ويعتمد إنتاج المايلين بشكل كامل على تخليق الكوليسترول في الخلايا
+ الدبقية. مادة دهنية تحيط بأجزاء طويلة من الألياف العصبية. الميالين يعزل الخلايا
+ العصبية ويعزز مرور الإشارات الكهربائية في جميع أنحاء دائرة جهازك العصبي.
+ - جزء من لفافة عنق الرحم يحيط بالشريان السباتي والوريد الوداجي الداخلي والعصب المبهم
+ أو العصب الودي المبهم. الوتر الرسغي. أغماد لأوتار العضلات التي تتحرك فوق الرسغ.
+ غمد مغزلي. غمد عظم الفخذ. الاستثمار الخارجي للعصب البصري. غمد الفخذ. الغمد اللفافي
+ لأوعية الفخذ. غمد هنلي. endoneurium ، وخاصة الاستمرارية الدقيقة حول الفروع الطرفية
+ للألياف العصبية. غمد رقائقي. العجان.
+ - تتضمن البيانات، عند الاقتضاء، مقاييس الأداء.
+- source_sentence: قد تتأثر تقديرات مخاطر وفيات الأوزون على المدى القصير أيضًا بالمسألة
+ الإحصائية التي اكتشفها معهد الآثار الصحية (Greenbaum, 2002a).
+ sentences:
+ - لم يجد معهد الآثار الصحية أي مشاكل في تقييم مخاطر وفيات الأوزون على المدى القصير
+ - قد ينتج عن الصداع النصفي والصداع العنقودي ألمًا شديدًا من جانب واحد ، ولكن على
+ عكس ألم العصب الثلاثي التوائم ، لا تحدث هذه الحالات عن طريق الحركة أو ملامسة الوجه
+ ولا تستجيب على الفور لكاربامازيبين. انظر الجدول 1 أدناه.
+ - اكتشف معهد الآثار الصحية مشكلة إحصائية مع تقييم مخاطر وفيات الأوزون قصيرة الأجل.
+- source_sentence: الآثار الجانبية فينيليفرين
+ sentences:
+ - يسبب آثارا جانبية عند بعض المرضى. التأثير الجانبي الأكثر شيوعًا هو السعال المستمر.
+ في حين أن معظم الآثار الجانبية لليزينوبريل غير ضارة ، يجب أن تكون على دراية بالآثار
+ الجانبية الخطيرة ، والتي يمكن أن تشير إلى رد فعل تحسسي ، إذا كنت تعاني من أي آثار
+ جانبية ، يجب عليك التحدث إلى طبيبك. ترتبط أحيانًا بانخفاض ضغط الدم (انخفاض ضغط
+ الدم) ، خاصة في بداية العلاج.
+ - تشمل بعض الآثار الجانبية المحتملة لفينيليفرين الدوخة والأرق والصداع. في معظم الحالات
+ ، تميل الآثار الجانبية إلى أن تكون طفيفة ويسهل علاجها بشكل عام.
+ - الرجل يلعب كرة السلة
+- source_sentence: سوق للمنتجات داخل مبنى كبير ذو جدران بيضاء.
+ sentences:
+ - راكب الدراجة مغطى بالطين
+ - سوق المنتجات داخل مبنى صغير أسود الجدران.
+ - السوق يبيع الخضروات.
+model-index:
+- name: SentenceTransformer based on FacebookAI/xlm-roberta-large
+ results:
+ - task:
+ type: semantic-similarity
+ name: Semantic Similarity
+ dataset:
+ name: sts dev
+ type: sts-dev
+ metrics:
+ - type: pearson_cosine
+ value: 0.8256350418804052
+ name: Pearson Cosine
+ - type: spearman_cosine
+ value: 0.827478494281667
+ name: Spearman Cosine
+ - type: pearson_manhattan
+ value: 0.8228224900306127
+ name: Pearson Manhattan
+ - type: spearman_manhattan
+ value: 0.8284011632112219
+ name: Spearman Manhattan
+ - type: pearson_euclidean
+ value: 0.8231973876582674
+ name: Pearson Euclidean
+ - type: spearman_euclidean
+ value: 0.8288613978074281
+ name: Spearman Euclidean
+ - type: pearson_dot
+ value: 0.8016573454999604
+ name: Pearson Dot
+ - type: spearman_dot
+ value: 0.8004396683364462
+ name: Spearman Dot
+ - type: pearson_max
+ value: 0.8256350418804052
+ name: Pearson Max
+ - type: spearman_max
+ value: 0.8288613978074281
+ name: Spearman Max
+---
+
+# SentenceTransformer based on FacebookAI/xlm-roberta-large
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large). It maps sentences & paragraphs to a 1024-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:** [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large)
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 tokens
+- **Similarity Function:** Cosine Similarity
+
+
+
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
+ (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+ 'سوق للمنتجات داخل مبنى كبير ذو جدران بيضاء.',
+ 'السوق يبيع الخضروات.',
+ 'سوق المنتجات داخل مبنى صغير أسود الجدران.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+
+
+
+
+
+
+## Evaluation
+
+### Metrics
+
+#### Semantic Similarity
+* Dataset: `sts-dev`
+* Evaluated with [EmbeddingSimilarityEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric | Value |
+|:--------------------|:-----------|
+| pearson_cosine | 0.8256 |
+| **spearman_cosine** | **0.8275** |
+| pearson_manhattan | 0.8228 |
+| spearman_manhattan | 0.8284 |
+| pearson_euclidean | 0.8232 |
+| spearman_euclidean | 0.8289 |
+| pearson_dot | 0.8017 |
+| spearman_dot | 0.8004 |
+| pearson_max | 0.8256 |
+| spearman_max | 0.8289 |
+
+
+
+
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 525,972 training samples
+* Columns: anchor
, positive
, and negative
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive | negative |
+ |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+ | type | string | string | string |
+ | details |
- min: 4 tokens
- mean: 17.13 tokens
- max: 84 tokens
| - min: 4 tokens
- mean: 54.94 tokens
- max: 262 tokens
| - min: 4 tokens
- mean: 52.05 tokens
- max: 236 tokens
|
+* Samples:
+ | anchor | positive | negative |
+ |:------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+ | كم عدد دعامات القلب التي يمكن أن يمتلكها الشخص
| الدعامة عبارة عن أنبوب مصنوع من شبكة معدنية يتم إدخاله في الشريان للمساعدة في إبقائه مفتوحًا. يتم وضع دعامات القلب أثناء عملية الرأب الوعائي ، ثم تُترك في مكانها ، وهناك نوعان من الدعامات. الدعامات المعدنية العارية هي النوع التقليدي ، وهي مصنوعة فقط من المعدن. مع هذه ، هناك احتمال أن ينسد الشريان بأنسجة ندبة أثناء عملية الشفاء. مع مزيج من عدة شرايين طويلة ، والتي قد يتم تركيبها على طول أطوالها بالكامل ، وإمكانية وضع الدعامات داخل الدعامات ، وكمية الشخص يمكن أن يكون عمليا لا حدود له. ومع ذلك ، فإن وجود الكثير من الدعامات ليس بالأمر الجيد أبدًا.
| "حتى لو لم تكن رياضيًا ، فإن المعرفة حول معدل ضربات قلبك يمكن أن تساعدك على مراقبة مستوى لياقتك ¢ Ã' ، وقد تساعدك حتى على اكتشاف التطور. مشاكل صحية. معدل ضربات القلب ، أو النبض ، هو عدد ضربات قلبك في الدقيقة ، ويختلف معدل ضربات القلب الطبيعي من شخص لآخر ، ويمكن أن تكون معرفتك مقياسًا مهمًا لصحة القلب ، حتى لو كنت ¢ ""لست رياضيًا ، يمكن أن تساعدك المعرفة حول معدل ضربات قلبك على مراقبة مستوى لياقتك"" وقد تساعدك حتى على اكتشاف المشاكل الصحية المتطورة. معدل ضربات القلب ، أو النبض ، هو عدد ضربات قلبك في الدقيقة."
|
+ | ماذا يعني عندما تكون الوظيفة فردية
| يمكن تحديد دالة فردية عن طريق استبدال كل من قيم x و y بقيمتي x و -y. إذا كانت القيم في المعادلة معكوسة (الإيجابيات هي السلبيات والسلبيات هي الإيجابيات) ، فإن الوظيفة تكون فردية. خذ ، على سبيل المثال ، هذه المعادلة y = x ^ 2.
| البحث حساس لحالة الأحرف. وظيفة FIND هي وظيفة مضمنة في Excel تم تصنيفها على أنها دالة سلسلة / نص. يمكن استخدامه كدالة في ورقة العمل (WS) في Excel. كدالة في ورقة العمل ، يمكن إدخال الدالة FIND كجزء من صيغة في خلية بورقة عمل. صيغة الدالة FIND في Microsoft Excel هي:
|
+ | رجل أسود مسن يستخدم آلة خياطة على قميص
| رجل يستخدم آلة خياطة
| رجل أبيض مسن يستخدم آلة خياطة على السراويل
|
+* Loss: [MatryoshkaLoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+ ```json
+ {
+ "loss": "MultipleNegativesRankingLoss",
+ "matryoshka_dims": [
+ 1024,
+ 768,
+ 512,
+ 256,
+ 128,
+ 64
+ ],
+ "matryoshka_weights": [
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1
+ ],
+ "n_dims_per_step": -1
+ }
+ ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 5,313 evaluation samples
+* Columns: anchor
, positive
, and negative
+* Approximate statistics based on the first 1000 samples:
+ | | anchor | positive | negative |
+ |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+ | type | string | string | string |
+ | details | - min: 4 tokens
- mean: 17.5 tokens
- max: 174 tokens
| - min: 4 tokens
- mean: 53.34 tokens
- max: 294 tokens
| - min: 4 tokens
- mean: 51.92 tokens
- max: 273 tokens
|
+* Samples:
+ | anchor | positive | negative |
+ |:----------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+ | ما لون شجرة الصنوبر الأحمر
| الصنوبر الأحمر (النرويج الصنوبر) الصنوبر resinosa. لحاء هذه الشجرة بني محمر اللون. في الجذوع الأقدم ، ينكسر اللحاء إلى حواف عريضة مسطحة تفصل بينها شقوق ضحلة. غالبًا ما يتم الخلط بين الصنوبر الأحمر والصنوبر النمساوي المقدم ، ولحاء هذه الشجرة بني محمر اللون. في الجذوع الأقدم ، ينكسر اللحاء إلى حواف عريضة مسطحة تفصل بينها شقوق ضحلة.
| النسر - رمز القوة ، نسر يظهر في شجرة صنوبر هو هدية مناسبة لرجل أكبر سنًا ، متمنياً له قوة نسر وطول عمر شجرة صنوبر. نسر على صخرة في البحر يرمز إلى البطل الذي يخوض معركة منفردة. الحصان هو رمز القوة والسرعة. ثمانية خيول في لوحة تمثل خيول الملك مو الشهيرة من القرن العاشر قبل الميلاد.
|
+ | امرأة تمشي في شارع المدينة
| أنثى في بيئة حضرية
| امرأة تمشي في حديقة ذات عشب
|
+ | أكبر مستهلك للمياه المعبأة
| كانت بولندا أكبر مستهلك للمياه المعبأة في العام الماضي ، حيث شكلت 23٪ من الحجم الإجمالي. احتلت روسيا المرتبة الثانية بنسبة 21٪ ورومانيا في المرتبة الثالثة بنسبة 10٪. من حيث معدلات النمو ، تصدرت بلغاريا الطريق بزيادة قدرها 22٪ ، تليها المجر وإستونيا وروسيا.
| يجب أن يكون لجميع الشركات التي تنتج المواد الاستهلاكية ، بموجب القانون الفيدرالي ، تاريخ انتهاء صلاحية عليها. نظرًا لأن المياه المعبأة مستهلكات ، يجب على الشركة وضع تاريخ انتهاء صلاحية عليها. كما أن بعض الماء يصبح غير نقي بسبب ذوبان البلاستيك أو المواد التي يطلقها البلاستيك في الماء.
|
+* Loss: [MatryoshkaLoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+ ```json
+ {
+ "loss": "MultipleNegativesRankingLoss",
+ "matryoshka_dims": [
+ 1024,
+ 768,
+ 512,
+ 256,
+ 128,
+ 64
+ ],
+ "matryoshka_weights": [
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1
+ ],
+ "n_dims_per_step": -1
+ }
+ ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `gradient_accumulation_steps`: 2
+- `learning_rate`: 6e-06
+- `num_train_epochs`: 1
+- `lr_scheduler_type`: constant_with_warmup
+- `warmup_ratio`: 0.05
+- `fp16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+Click to expand
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 2
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 6e-06
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: constant_with_warmup
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.05
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `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
+- `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
+- `eval_use_gather_object`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+
+
+### Training Logs
+Click to expand
+
+| Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine |
+|:------:|:-----:|:-------------:|:---------------:|:-----------------------:|
+| 0 | 0 | - | - | 0.5020 |
+| 0.0006 | 10 | 21.4435 | - | - |
+| 0.0012 | 20 | 21.3717 | - | - |
+| 0.0018 | 30 | 20.8768 | - | - |
+| 0.0024 | 40 | 21.5983 | - | - |
+| 0.0030 | 50 | 21.4375 | - | - |
+| 0.0037 | 60 | 20.8731 | - | - |
+| 0.0043 | 70 | 21.1706 | - | - |
+| 0.0049 | 80 | 19.9868 | - | - |
+| 0.0055 | 90 | 19.81 | - | - |
+| 0.0061 | 100 | 19.7024 | - | - |
+| 0.0067 | 110 | 18.9338 | - | - |
+| 0.0073 | 120 | 18.8047 | - | - |
+| 0.0079 | 130 | 18.0191 | - | - |
+| 0.0085 | 140 | 17.3543 | - | - |
+| 0.0091 | 150 | 16.2901 | - | - |
+| 0.0097 | 160 | 16.0705 | - | - |
+| 0.0103 | 170 | 15.3631 | - | - |
+| 0.0110 | 180 | 15.3457 | - | - |
+| 0.0116 | 190 | 15.2714 | - | - |
+| 0.0122 | 200 | 15.0009 | - | - |
+| 0.0128 | 210 | 14.2687 | - | - |
+| 0.0134 | 220 | 14.9628 | - | - |
+| 0.0140 | 230 | 14.6214 | - | - |
+| 0.0146 | 240 | 14.0547 | - | - |
+| 0.0152 | 250 | 13.9721 | - | - |
+| 0.0158 | 260 | 13.8674 | - | - |
+| 0.0164 | 270 | 14.2228 | - | - |
+| 0.0170 | 280 | 13.4609 | - | - |
+| 0.0176 | 290 | 13.5085 | - | - |
+| 0.0183 | 300 | 13.0996 | - | - |
+| 0.0189 | 310 | 12.6665 | - | - |
+| 0.0195 | 320 | 10.8726 | - | - |
+| 0.0201 | 330 | 9.5858 | - | - |
+| 0.0207 | 340 | 10.0155 | - | - |
+| 0.0213 | 350 | 9.3637 | - | - |
+| 0.0219 | 360 | 8.2787 | - | - |
+| 0.0225 | 370 | 8.1796 | - | - |
+| 0.0231 | 380 | 7.1682 | - | - |
+| 0.0237 | 390 | 7.2735 | - | - |
+| 0.0243 | 400 | 7.4527 | - | - |
+| 0.0249 | 410 | 6.6717 | - | - |
+| 0.0256 | 420 | 7.3839 | - | - |
+| 0.0262 | 430 | 5.5281 | - | - |
+| 0.0268 | 440 | 5.7704 | - | - |
+| 0.0274 | 450 | 6.4584 | - | - |
+| 0.0280 | 460 | 6.2236 | - | - |
+| 0.0286 | 470 | 5.2214 | - | - |
+| 0.0292 | 480 | 6.7058 | - | - |
+| 0.0298 | 490 | 6.4218 | - | - |
+| 0.0304 | 500 | 5.6464 | 4.2905 | 0.7654 |
+| 0.0310 | 510 | 6.3232 | - | - |
+| 0.0316 | 520 | 5.5365 | - | - |
+| 0.0322 | 530 | 4.9866 | - | - |
+| 0.0329 | 540 | 4.7878 | - | - |
+| 0.0335 | 550 | 4.9709 | - | - |
+| 0.0341 | 560 | 4.7273 | - | - |
+| 0.0347 | 570 | 4.9668 | - | - |
+| 0.0353 | 580 | 4.5264 | - | - |
+| 0.0359 | 590 | 4.9037 | - | - |
+| 0.0365 | 600 | 4.4175 | - | - |
+| 0.0371 | 610 | 5.0075 | - | - |
+| 0.0377 | 620 | 4.9083 | - | - |
+| 0.0383 | 630 | 3.701 | - | - |
+| 0.0389 | 640 | 4.0337 | - | - |
+| 0.0395 | 650 | 3.8381 | - | - |
+| 0.0402 | 660 | 4.1402 | - | - |
+| 0.0408 | 670 | 4.027 | - | - |
+| 0.0414 | 680 | 4.2162 | - | - |
+| 0.0420 | 690 | 4.4585 | - | - |
+| 0.0426 | 700 | 3.6895 | - | - |
+| 0.0432 | 710 | 4.0551 | - | - |
+| 0.0438 | 720 | 3.9698 | - | - |
+| 0.0444 | 730 | 4.603 | - | - |
+| 0.0450 | 740 | 4.298 | - | - |
+| 0.0456 | 750 | 3.4477 | - | - |
+| 0.0462 | 760 | 3.4911 | - | - |
+| 0.0468 | 770 | 4.3236 | - | - |
+| 0.0475 | 780 | 3.8387 | - | - |
+| 0.0481 | 790 | 3.9192 | - | - |
+| 0.0487 | 800 | 4.5429 | - | - |
+| 0.0493 | 810 | 3.7079 | - | - |
+| 0.0499 | 820 | 3.3982 | - | - |
+| 0.0505 | 830 | 4.0582 | - | - |
+| 0.0511 | 840 | 3.6453 | - | - |
+| 0.0517 | 850 | 3.1096 | - | - |
+| 0.0523 | 860 | 3.0238 | - | - |
+| 0.0529 | 870 | 3.2529 | - | - |
+| 0.0535 | 880 | 3.9375 | - | - |
+| 0.0541 | 890 | 4.2027 | - | - |
+| 0.0548 | 900 | 3.1972 | - | - |
+| 0.0554 | 910 | 4.1808 | - | - |
+| 0.0560 | 920 | 3.4926 | - | - |
+| 0.0566 | 930 | 3.6871 | - | - |
+| 0.0572 | 940 | 2.6525 | - | - |
+| 0.0578 | 950 | 3.8531 | - | - |
+| 0.0584 | 960 | 2.977 | - | - |
+| 0.0590 | 970 | 3.1851 | - | - |
+| 0.0596 | 980 | 2.6765 | - | - |
+| 0.0602 | 990 | 3.2409 | - | - |
+| 0.0608 | 1000 | 3.0853 | 2.5746 | 0.7993 |
+| 0.0614 | 1010 | 3.0047 | - | - |
+| 0.0621 | 1020 | 2.5571 | - | - |
+| 0.0627 | 1030 | 3.5924 | - | - |
+| 0.0633 | 1040 | 3.0118 | - | - |
+| 0.0639 | 1050 | 3.3328 | - | - |
+| 0.0645 | 1060 | 3.3521 | - | - |
+| 0.0651 | 1070 | 3.6967 | - | - |
+| 0.0657 | 1080 | 2.5625 | - | - |
+| 0.0663 | 1090 | 3.1467 | - | - |
+| 0.0669 | 1100 | 3.086 | - | - |
+| 0.0675 | 1110 | 2.9848 | - | - |
+| 0.0681 | 1120 | 3.4545 | - | - |
+| 0.0687 | 1130 | 2.8503 | - | - |
+| 0.0694 | 1140 | 3.01 | - | - |
+| 0.0700 | 1150 | 2.5596 | - | - |
+| 0.0706 | 1160 | 2.7204 | - | - |
+| 0.0712 | 1170 | 3.0992 | - | - |
+| 0.0718 | 1180 | 3.4662 | - | - |
+| 0.0724 | 1190 | 3.3522 | - | - |
+| 0.0730 | 1200 | 2.9208 | - | - |
+| 0.0736 | 1210 | 2.9255 | - | - |
+| 0.0742 | 1220 | 2.7254 | - | - |
+| 0.0748 | 1230 | 2.8535 | - | - |
+| 0.0754 | 1240 | 3.0474 | - | - |
+| 0.0760 | 1250 | 3.1126 | - | - |
+| 0.0767 | 1260 | 2.3903 | - | - |
+| 0.0773 | 1270 | 3.0233 | - | - |
+| 0.0779 | 1280 | 2.8023 | - | - |
+| 0.0785 | 1290 | 2.9833 | - | - |
+| 0.0791 | 1300 | 2.8474 | - | - |
+| 0.0797 | 1310 | 2.7475 | - | - |
+| 0.0803 | 1320 | 2.7909 | - | - |
+| 0.0809 | 1330 | 2.9 | - | - |
+| 0.0815 | 1340 | 2.6851 | - | - |
+| 0.0821 | 1350 | 2.3341 | - | - |
+| 0.0827 | 1360 | 2.7356 | - | - |
+| 0.0833 | 1370 | 2.9598 | - | - |
+| 0.0840 | 1380 | 3.0407 | - | - |
+| 0.0846 | 1390 | 2.5379 | - | - |
+| 0.0852 | 1400 | 3.0827 | - | - |
+| 0.0858 | 1410 | 2.6063 | - | - |
+| 0.0864 | 1420 | 2.3416 | - | - |
+| 0.0870 | 1430 | 2.389 | - | - |
+| 0.0876 | 1440 | 2.2908 | - | - |
+| 0.0882 | 1450 | 2.2592 | - | - |
+| 0.0888 | 1460 | 2.557 | - | - |
+| 0.0894 | 1470 | 3.0709 | - | - |
+| 0.0900 | 1480 | 2.6669 | - | - |
+| 0.0906 | 1490 | 2.3669 | - | - |
+| 0.0913 | 1500 | 2.0875 | 2.1857 | 0.8022 |
+| 0.0919 | 1510 | 2.4048 | - | - |
+| 0.0925 | 1520 | 2.438 | - | - |
+| 0.0931 | 1530 | 2.6925 | - | - |
+| 0.0937 | 1540 | 2.6539 | - | - |
+| 0.0943 | 1550 | 2.533 | - | - |
+| 0.0949 | 1560 | 3.1083 | - | - |
+| 0.0955 | 1570 | 2.2875 | - | - |
+| 0.0961 | 1580 | 3.3862 | - | - |
+| 0.0967 | 1590 | 2.5905 | - | - |
+| 0.0973 | 1600 | 3.2255 | - | - |
+| 0.0979 | 1610 | 2.4644 | - | - |
+| 0.0986 | 1620 | 2.3459 | - | - |
+| 0.0992 | 1630 | 2.8529 | - | - |
+| 0.0998 | 1640 | 2.1764 | - | - |
+| 0.1004 | 1650 | 1.9525 | - | - |
+| 0.1010 | 1660 | 2.4797 | - | - |
+| 0.1016 | 1670 | 2.738 | - | - |
+| 0.1022 | 1680 | 2.6411 | - | - |
+| 0.1028 | 1690 | 2.8727 | - | - |
+| 0.1034 | 1700 | 2.6647 | - | - |
+| 0.1040 | 1710 | 2.2901 | - | - |
+| 0.1046 | 1720 | 1.8548 | - | - |
+| 0.1053 | 1730 | 2.7483 | - | - |
+| 0.1059 | 1740 | 2.9149 | - | - |
+| 0.1065 | 1750 | 2.4161 | - | - |
+| 0.1071 | 1760 | 2.892 | - | - |
+| 0.1077 | 1770 | 2.5077 | - | - |
+| 0.1083 | 1780 | 2.4095 | - | - |
+| 0.1089 | 1790 | 2.2579 | - | - |
+| 0.1095 | 1800 | 2.7354 | - | - |
+| 0.1101 | 1810 | 2.2449 | - | - |
+| 0.1107 | 1820 | 2.5732 | - | - |
+| 0.1113 | 1830 | 2.2574 | - | - |
+| 0.1119 | 1840 | 2.3138 | - | - |
+| 0.1126 | 1850 | 2.3812 | - | - |
+| 0.1132 | 1860 | 3.0886 | - | - |
+| 0.1138 | 1870 | 2.0547 | - | - |
+| 0.1144 | 1880 | 2.5267 | - | - |
+| 0.1150 | 1890 | 2.3027 | - | - |
+| 0.1156 | 1900 | 2.0564 | - | - |
+| 0.1162 | 1910 | 2.2067 | - | - |
+| 0.1168 | 1920 | 2.7163 | - | - |
+| 0.1174 | 1930 | 2.2444 | - | - |
+| 0.1180 | 1940 | 2.3602 | - | - |
+| 0.1186 | 1950 | 2.3116 | - | - |
+| 0.1192 | 1960 | 2.6275 | - | - |
+| 0.1199 | 1970 | 2.4513 | - | - |
+| 0.1205 | 1980 | 2.248 | - | - |
+| 0.1211 | 1990 | 2.5932 | - | - |
+| 0.1217 | 2000 | 2.4649 | 2.0423 | 0.8091 |
+| 0.1223 | 2010 | 2.2928 | - | - |
+| 0.1229 | 2020 | 2.2641 | - | - |
+| 0.1235 | 2030 | 2.2922 | - | - |
+| 0.1241 | 2040 | 2.7012 | - | - |
+| 0.1247 | 2050 | 2.5443 | - | - |
+| 0.1253 | 2060 | 2.2732 | - | - |
+| 0.1259 | 2070 | 2.2286 | - | - |
+| 0.1265 | 2080 | 2.4436 | - | - |
+| 0.1272 | 2090 | 2.6274 | - | - |
+| 0.1278 | 2100 | 2.4676 | - | - |
+| 0.1284 | 2110 | 2.4846 | - | - |
+| 0.1290 | 2120 | 2.4191 | - | - |
+| 0.1296 | 2130 | 2.2225 | - | - |
+| 0.1302 | 2140 | 2.1632 | - | - |
+| 0.1308 | 2150 | 2.8109 | - | - |
+| 0.1314 | 2160 | 2.2506 | - | - |
+| 0.1320 | 2170 | 2.2097 | - | - |
+| 0.1326 | 2180 | 2.1465 | - | - |
+| 0.1332 | 2190 | 2.4718 | - | - |
+| 0.1338 | 2200 | 2.0065 | - | - |
+| 0.1345 | 2210 | 2.0881 | - | - |
+| 0.1351 | 2220 | 2.6028 | - | - |
+| 0.1357 | 2230 | 2.4396 | - | - |
+| 0.1363 | 2240 | 2.3964 | - | - |
+| 0.1369 | 2250 | 2.5122 | - | - |
+| 0.1375 | 2260 | 2.2119 | - | - |
+| 0.1381 | 2270 | 2.6083 | - | - |
+| 0.1387 | 2280 | 2.9089 | - | - |
+| 0.1393 | 2290 | 2.4405 | - | - |
+| 0.1399 | 2300 | 2.5661 | - | - |
+| 0.1405 | 2310 | 1.7193 | - | - |
+| 0.1411 | 2320 | 2.2237 | - | - |
+| 0.1418 | 2330 | 2.3725 | - | - |
+| 0.1424 | 2340 | 1.9095 | - | - |
+| 0.1430 | 2350 | 2.3458 | - | - |
+| 0.1436 | 2360 | 2.2409 | - | - |
+| 0.1442 | 2370 | 2.5058 | - | - |
+| 0.1448 | 2380 | 2.7686 | - | - |
+| 0.1454 | 2390 | 2.5467 | - | - |
+| 0.1460 | 2400 | 2.2733 | - | - |
+| 0.1466 | 2410 | 2.4094 | - | - |
+| 0.1472 | 2420 | 2.0335 | - | - |
+| 0.1478 | 2430 | 2.0628 | - | - |
+| 0.1484 | 2440 | 2.0153 | - | - |
+| 0.1491 | 2450 | 2.5779 | - | - |
+| 0.1497 | 2460 | 2.4797 | - | - |
+| 0.1503 | 2470 | 2.6106 | - | - |
+| 0.1509 | 2480 | 2.509 | - | - |
+| 0.1515 | 2490 | 2.576 | - | - |
+| 0.1521 | 2500 | 2.4158 | 1.8880 | 0.8100 |
+| 0.1527 | 2510 | 2.4631 | - | - |
+| 0.1533 | 2520 | 2.4689 | - | - |
+| 0.1539 | 2530 | 1.8991 | - | - |
+| 0.1545 | 2540 | 2.0037 | - | - |
+| 0.1551 | 2550 | 2.5575 | - | - |
+| 0.1557 | 2560 | 2.3801 | - | - |
+| 0.1564 | 2570 | 3.0848 | - | - |
+| 0.1570 | 2580 | 2.1983 | - | - |
+| 0.1576 | 2590 | 2.3668 | - | - |
+| 0.1582 | 2600 | 2.6198 | - | - |
+| 0.1588 | 2610 | 1.8254 | - | - |
+| 0.1594 | 2620 | 2.7682 | - | - |
+| 0.1600 | 2630 | 2.3169 | - | - |
+| 0.1606 | 2640 | 2.3229 | - | - |
+| 0.1612 | 2650 | 2.2648 | - | - |
+| 0.1618 | 2660 | 2.5666 | - | - |
+| 0.1624 | 2670 | 2.1311 | - | - |
+| 0.1630 | 2680 | 2.714 | - | - |
+| 0.1637 | 2690 | 2.2482 | - | - |
+| 0.1643 | 2700 | 1.5924 | - | - |
+| 0.1649 | 2710 | 1.981 | - | - |
+| 0.1655 | 2720 | 2.3084 | - | - |
+| 0.1661 | 2730 | 1.8018 | - | - |
+| 0.1667 | 2740 | 2.8646 | - | - |
+| 0.1673 | 2750 | 2.3481 | - | - |
+| 0.1679 | 2760 | 1.6595 | - | - |
+| 0.1685 | 2770 | 1.9359 | - | - |
+| 0.1691 | 2780 | 2.2035 | - | - |
+| 0.1697 | 2790 | 1.768 | - | - |
+| 0.1703 | 2800 | 2.2909 | - | - |
+| 0.1710 | 2810 | 2.4359 | - | - |
+| 0.1716 | 2820 | 2.1752 | - | - |
+| 0.1722 | 2830 | 2.2363 | - | - |
+| 0.1728 | 2840 | 2.2288 | - | - |
+| 0.1734 | 2850 | 1.7949 | - | - |
+| 0.1740 | 2860 | 1.9309 | - | - |
+| 0.1746 | 2870 | 2.2123 | - | - |
+| 0.1752 | 2880 | 1.9533 | - | - |
+| 0.1758 | 2890 | 2.1364 | - | - |
+| 0.1764 | 2900 | 2.5226 | - | - |
+| 0.1770 | 2910 | 2.0234 | - | - |
+| 0.1776 | 2920 | 1.9281 | - | - |
+| 0.1783 | 2930 | 2.2906 | - | - |
+| 0.1789 | 2940 | 2.4426 | - | - |
+| 0.1795 | 2950 | 1.9415 | - | - |
+| 0.1801 | 2960 | 2.0118 | - | - |
+| 0.1807 | 2970 | 1.8743 | - | - |
+| 0.1813 | 2980 | 2.1937 | - | - |
+| 0.1819 | 2990 | 2.3486 | - | - |
+| 0.1825 | 3000 | 2.0213 | 1.7885 | 0.8194 |
+| 0.1831 | 3010 | 2.8386 | - | - |
+| 0.1837 | 3020 | 2.1086 | - | - |
+| 0.1843 | 3030 | 1.9674 | - | - |
+| 0.1849 | 3040 | 2.2939 | - | - |
+| 0.1856 | 3050 | 2.3851 | - | - |
+| 0.1862 | 3060 | 1.8537 | - | - |
+| 0.1868 | 3070 | 2.5518 | - | - |
+| 0.1874 | 3080 | 2.3096 | - | - |
+| 0.1880 | 3090 | 2.5557 | - | - |
+| 0.1886 | 3100 | 2.8594 | - | - |
+| 0.1892 | 3110 | 2.0555 | - | - |
+| 0.1898 | 3120 | 2.0453 | - | - |
+| 0.1904 | 3130 | 2.0322 | - | - |
+| 0.1910 | 3140 | 2.3151 | - | - |
+| 0.1916 | 3150 | 2.0746 | - | - |
+| 0.1922 | 3160 | 1.7228 | - | - |
+| 0.1929 | 3170 | 2.1176 | - | - |
+| 0.1935 | 3180 | 2.0774 | - | - |
+| 0.1941 | 3190 | 2.3653 | - | - |
+| 0.1947 | 3200 | 2.1124 | - | - |
+| 0.1953 | 3210 | 2.4932 | - | - |
+| 0.1959 | 3220 | 2.6039 | - | - |
+| 0.1965 | 3230 | 1.6741 | - | - |
+| 0.1971 | 3240 | 1.9226 | - | - |
+| 0.1977 | 3250 | 2.0653 | - | - |
+| 0.1983 | 3260 | 2.0098 | - | - |
+| 0.1989 | 3270 | 2.1304 | - | - |
+| 0.1995 | 3280 | 1.9026 | - | - |
+| 0.2002 | 3290 | 1.7509 | - | - |
+| 0.2008 | 3300 | 2.1752 | - | - |
+| 0.2014 | 3310 | 2.0579 | - | - |
+| 0.2020 | 3320 | 2.1505 | - | - |
+| 0.2026 | 3330 | 2.244 | - | - |
+| 0.2032 | 3340 | 2.0012 | - | - |
+| 0.2038 | 3350 | 2.1361 | - | - |
+| 0.2044 | 3360 | 2.031 | - | - |
+| 0.2050 | 3370 | 2.0056 | - | - |
+| 0.2056 | 3380 | 1.9624 | - | - |
+| 0.2062 | 3390 | 2.2317 | - | - |
+| 0.2069 | 3400 | 2.3869 | - | - |
+| 0.2075 | 3410 | 2.0784 | - | - |
+| 0.2081 | 3420 | 1.8601 | - | - |
+| 0.2087 | 3430 | 1.7063 | - | - |
+| 0.2093 | 3440 | 2.1913 | - | - |
+| 0.2099 | 3450 | 1.611 | - | - |
+| 0.2105 | 3460 | 2.1682 | - | - |
+| 0.2111 | 3470 | 2.052 | - | - |
+| 0.2117 | 3480 | 1.8947 | - | - |
+| 0.2123 | 3490 | 2.1593 | - | - |
+| 0.2129 | 3500 | 2.0164 | 1.6326 | 0.8125 |
+| 0.2135 | 3510 | 2.1637 | - | - |
+| 0.2142 | 3520 | 2.3442 | - | - |
+| 0.2148 | 3530 | 1.9714 | - | - |
+| 0.2154 | 3540 | 1.9191 | - | - |
+| 0.2160 | 3550 | 1.9054 | - | - |
+| 0.2166 | 3560 | 1.7648 | - | - |
+| 0.2172 | 3570 | 1.5984 | - | - |
+| 0.2178 | 3580 | 1.8352 | - | - |
+| 0.2184 | 3590 | 1.7359 | - | - |
+| 0.2190 | 3600 | 1.6215 | - | - |
+| 0.2196 | 3610 | 2.4038 | - | - |
+| 0.2202 | 3620 | 1.9934 | - | - |
+| 0.2208 | 3630 | 1.8032 | - | - |
+| 0.2215 | 3640 | 2.1424 | - | - |
+| 0.2221 | 3650 | 1.8685 | - | - |
+| 0.2227 | 3660 | 1.8718 | - | - |
+| 0.2233 | 3670 | 2.2936 | - | - |
+| 0.2239 | 3680 | 2.3066 | - | - |
+| 0.2245 | 3690 | 2.1467 | - | - |
+| 0.2251 | 3700 | 1.9157 | - | - |
+| 0.2257 | 3710 | 2.1634 | - | - |
+| 0.2263 | 3720 | 2.0877 | - | - |
+| 0.2269 | 3730 | 1.9922 | - | - |
+| 0.2275 | 3740 | 1.7445 | - | - |
+| 0.2281 | 3750 | 1.7505 | - | - |
+| 0.2288 | 3760 | 1.7483 | - | - |
+| 0.2294 | 3770 | 2.0549 | - | - |
+| 0.2300 | 3780 | 1.7194 | - | - |
+| 0.2306 | 3790 | 1.7902 | - | - |
+| 0.2312 | 3800 | 2.0417 | - | - |
+| 0.2318 | 3810 | 2.0775 | - | - |
+| 0.2324 | 3820 | 1.8369 | - | - |
+| 0.2330 | 3830 | 2.03 | - | - |
+| 0.2336 | 3840 | 1.9612 | - | - |
+| 0.2342 | 3850 | 1.7391 | - | - |
+| 0.2348 | 3860 | 2.3491 | - | - |
+| 0.2354 | 3870 | 2.0881 | - | - |
+| 0.2361 | 3880 | 2.0937 | - | - |
+| 0.2367 | 3890 | 2.2639 | - | - |
+| 0.2373 | 3900 | 1.7997 | - | - |
+| 0.2379 | 3910 | 1.6543 | - | - |
+| 0.2385 | 3920 | 2.4777 | - | - |
+| 0.2391 | 3930 | 1.9603 | - | - |
+| 0.2397 | 3940 | 2.5438 | - | - |
+| 0.2403 | 3950 | 1.6183 | - | - |
+| 0.2409 | 3960 | 1.6891 | - | - |
+| 0.2415 | 3970 | 1.9894 | - | - |
+| 0.2421 | 3980 | 1.4788 | - | - |
+| 0.2427 | 3990 | 1.544 | - | - |
+| 0.2434 | 4000 | 2.5451 | 1.6071 | 0.8085 |
+| 0.2440 | 4010 | 2.1108 | - | - |
+| 0.2446 | 4020 | 1.7795 | - | - |
+| 0.2452 | 4030 | 1.9481 | - | - |
+| 0.2458 | 4040 | 2.0071 | - | - |
+| 0.2464 | 4050 | 2.4824 | - | - |
+| 0.2470 | 4060 | 1.7675 | - | - |
+| 0.2476 | 4070 | 2.0736 | - | - |
+| 0.2482 | 4080 | 1.8881 | - | - |
+| 0.2488 | 4090 | 1.4922 | - | - |
+| 0.2494 | 4100 | 2.2162 | - | - |
+| 0.2500 | 4110 | 1.9379 | - | - |
+| 0.2507 | 4120 | 1.492 | - | - |
+| 0.2513 | 4130 | 2.321 | - | - |
+| 0.2519 | 4140 | 1.8728 | - | - |
+| 0.2525 | 4150 | 2.0446 | - | - |
+| 0.2531 | 4160 | 1.8886 | - | - |
+| 0.2537 | 4170 | 2.3516 | - | - |
+| 0.2543 | 4180 | 1.4527 | - | - |
+| 0.2549 | 4190 | 1.8565 | - | - |
+| 0.2555 | 4200 | 1.2772 | - | - |
+| 0.2561 | 4210 | 2.0268 | - | - |
+| 0.2567 | 4220 | 1.8977 | - | - |
+| 0.2573 | 4230 | 2.1598 | - | - |
+| 0.2580 | 4240 | 2.0181 | - | - |
+| 0.2586 | 4250 | 1.9695 | - | - |
+| 0.2592 | 4260 | 1.7055 | - | - |
+| 0.2598 | 4270 | 1.452 | - | - |
+| 0.2604 | 4280 | 1.7157 | - | - |
+| 0.2610 | 4290 | 2.159 | - | - |
+| 0.2616 | 4300 | 1.9468 | - | - |
+| 0.2622 | 4310 | 2.3077 | - | - |
+| 0.2628 | 4320 | 2.249 | - | - |
+| 0.2634 | 4330 | 1.8195 | - | - |
+| 0.2640 | 4340 | 1.7286 | - | - |
+| 0.2646 | 4350 | 1.9193 | - | - |
+| 0.2653 | 4360 | 1.7587 | - | - |
+| 0.2659 | 4370 | 2.0261 | - | - |
+| 0.2665 | 4380 | 1.643 | - | - |
+| 0.2671 | 4390 | 2.3491 | - | - |
+| 0.2677 | 4400 | 1.9908 | - | - |
+| 0.2683 | 4410 | 1.5614 | - | - |
+| 0.2689 | 4420 | 1.5435 | - | - |
+| 0.2695 | 4430 | 1.9115 | - | - |
+| 0.2701 | 4440 | 2.1565 | - | - |
+| 0.2707 | 4450 | 1.6645 | - | - |
+| 0.2713 | 4460 | 1.6229 | - | - |
+| 0.2719 | 4470 | 1.6025 | - | - |
+| 0.2726 | 4480 | 1.6732 | - | - |
+| 0.2732 | 4490 | 1.8929 | - | - |
+| 0.2738 | 4500 | 1.9043 | 1.5524 | 0.8170 |
+| 0.2744 | 4510 | 2.0704 | - | - |
+| 0.2750 | 4520 | 1.7518 | - | - |
+| 0.2756 | 4530 | 1.7307 | - | - |
+| 0.2762 | 4540 | 2.0582 | - | - |
+| 0.2768 | 4550 | 2.0518 | - | - |
+| 0.2774 | 4560 | 2.1475 | - | - |
+| 0.2780 | 4570 | 1.7513 | - | - |
+| 0.2786 | 4580 | 1.7217 | - | - |
+| 0.2792 | 4590 | 1.8506 | - | - |
+| 0.2799 | 4600 | 1.7839 | - | - |
+| 0.2805 | 4610 | 1.7636 | - | - |
+| 0.2811 | 4620 | 2.4504 | - | - |
+| 0.2817 | 4630 | 1.8202 | - | - |
+| 0.2823 | 4640 | 1.9163 | - | - |
+| 0.2829 | 4650 | 2.2026 | - | - |
+| 0.2835 | 4660 | 1.7565 | - | - |
+| 0.2841 | 4670 | 1.7611 | - | - |
+| 0.2847 | 4680 | 1.982 | - | - |
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+| 0.2859 | 4700 | 2.022 | - | - |
+| 0.2865 | 4710 | 1.5905 | - | - |
+| 0.2872 | 4720 | 1.9998 | - | - |
+| 0.2878 | 4730 | 1.9294 | - | - |
+| 0.2884 | 4740 | 2.2862 | - | - |
+| 0.2890 | 4750 | 2.1944 | - | - |
+| 0.2896 | 4760 | 1.815 | - | - |
+| 0.2902 | 4770 | 1.5759 | - | - |
+| 0.2908 | 4780 | 1.6481 | - | - |
+| 0.2914 | 4790 | 1.6934 | - | - |
+| 0.2920 | 4800 | 2.2347 | - | - |
+| 0.2926 | 4810 | 1.7961 | - | - |
+| 0.2932 | 4820 | 2.2624 | - | - |
+| 0.2938 | 4830 | 1.6544 | - | - |
+| 0.2945 | 4840 | 2.0198 | - | - |
+| 0.2951 | 4850 | 1.6184 | - | - |
+| 0.2957 | 4860 | 1.6182 | - | - |
+| 0.2963 | 4870 | 2.1709 | - | - |
+| 0.2969 | 4880 | 1.8362 | - | - |
+| 0.2975 | 4890 | 1.8456 | - | - |
+| 0.2981 | 4900 | 1.694 | - | - |
+| 0.2987 | 4910 | 1.6234 | - | - |
+| 0.2993 | 4920 | 1.5079 | - | - |
+| 0.2999 | 4930 | 2.3818 | - | - |
+| 0.3005 | 4940 | 1.4689 | - | - |
+| 0.3011 | 4950 | 1.6119 | - | - |
+| 0.3018 | 4960 | 1.729 | - | - |
+| 0.3024 | 4970 | 1.3665 | - | - |
+| 0.3030 | 4980 | 1.8715 | - | - |
+| 0.3036 | 4990 | 2.1445 | - | - |
+| 0.3042 | 5000 | 1.7364 | 1.5370 | 0.8231 |
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+| 0.3054 | 5020 | 1.6435 | - | - |
+| 0.3060 | 5030 | 1.5962 | - | - |
+| 0.3066 | 5040 | 1.6887 | - | - |
+| 0.3072 | 5050 | 1.978 | - | - |
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+| 0.3085 | 5070 | 1.3992 | - | - |
+| 0.3091 | 5080 | 2.5111 | - | - |
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+| 0.3103 | 5100 | 1.4076 | - | - |
+| 0.3109 | 5110 | 2.1234 | - | - |
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+| 0.3121 | 5130 | 1.9899 | - | - |
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+| 0.3139 | 5160 | 1.8397 | - | - |
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+| 0.3151 | 5180 | 2.1321 | - | - |
+| 0.3158 | 5190 | 2.0014 | - | - |
+| 0.3164 | 5200 | 1.828 | - | - |
+| 0.3170 | 5210 | 2.3236 | - | - |
+| 0.3176 | 5220 | 2.353 | - | - |
+| 0.3182 | 5230 | 1.918 | - | - |
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+| 0.3206 | 5270 | 1.5277 | - | - |
+| 0.3212 | 5280 | 1.9375 | - | - |
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+| 0.3224 | 5300 | 2.0324 | - | - |
+| 0.3231 | 5310 | 1.6346 | - | - |
+| 0.3237 | 5320 | 2.0467 | - | - |
+| 0.3243 | 5330 | 1.6091 | - | - |
+| 0.3249 | 5340 | 1.4123 | - | - |
+| 0.3255 | 5350 | 1.9284 | - | - |
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+| 0.3291 | 5410 | 2.3529 | - | - |
+| 0.3297 | 5420 | 2.0192 | - | - |
+| 0.3304 | 5430 | 2.0734 | - | - |
+| 0.3310 | 5440 | 1.9783 | - | - |
+| 0.3316 | 5450 | 1.276 | - | - |
+| 0.3322 | 5460 | 1.3195 | - | - |
+| 0.3328 | 5470 | 1.6383 | - | - |
+| 0.3334 | 5480 | 1.1813 | - | - |
+| 0.3340 | 5490 | 2.0388 | - | - |
+| 0.3346 | 5500 | 1.8688 | 1.5780 | 0.8175 |
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+| 0.3358 | 5520 | 2.1903 | - | - |
+| 0.3364 | 5530 | 1.9834 | - | - |
+| 0.3370 | 5540 | 1.6896 | - | - |
+| 0.3377 | 5550 | 1.3363 | - | - |
+| 0.3383 | 5560 | 1.6546 | - | - |
+| 0.3389 | 5570 | 1.9395 | - | - |
+| 0.3395 | 5580 | 2.0097 | - | - |
+| 0.3401 | 5590 | 1.7401 | - | - |
+| 0.3407 | 5600 | 2.0762 | - | - |
+| 0.3413 | 5610 | 1.8717 | - | - |
+| 0.3419 | 5620 | 1.6267 | - | - |
+| 0.3425 | 5630 | 2.2863 | - | - |
+| 0.3431 | 5640 | 1.8856 | - | - |
+| 0.3437 | 5650 | 1.6284 | - | - |
+| 0.3443 | 5660 | 1.9623 | - | - |
+| 0.3450 | 5670 | 1.921 | - | - |
+| 0.3456 | 5680 | 1.9259 | - | - |
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+| 0.3480 | 5720 | 2.1402 | - | - |
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+| 0.3498 | 5750 | 1.6076 | - | - |
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+| 0.3547 | 5830 | 1.8636 | - | - |
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+| 0.3577 | 5880 | 1.5148 | - | - |
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+| 0.3608 | 5930 | 1.5526 | - | - |
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+| 0.3638 | 5980 | 2.2481 | - | - |
+| 0.3644 | 5990 | 2.0231 | - | - |
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+| 0.3669 | 6030 | 1.6828 | - | - |
+| 0.3675 | 6040 | 1.6239 | - | - |
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+| 0.3687 | 6060 | 2.3066 | - | - |
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+| 0.3894 | 6400 | 2.0161 | - | - |
+| 0.3900 | 6410 | 1.6998 | - | - |
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+| 0.3942 | 6480 | 1.6878 | - | - |
+| 0.3948 | 6490 | 1.2815 | - | - |
+| 0.3954 | 6500 | 1.5526 | 1.4838 | 0.8213 |
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+| 0.3967 | 6520 | 1.7506 | - | - |
+| 0.3973 | 6530 | 1.802 | - | - |
+| 0.3979 | 6540 | 2.0784 | - | - |
+| 0.3985 | 6550 | 1.5526 | - | - |
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+| 0.4034 | 6630 | 1.9451 | - | - |
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+| 0.4058 | 6670 | 1.8987 | - | - |
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+| 0.4253 | 6990 | 2.0803 | - | - |
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+| 0.4277 | 7030 | 1.6805 | - | - |
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+| 0.4313 | 7090 | 1.7176 | - | - |
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+| 0.4441 | 7300 | 1.8626 | - | - |
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+| 0.4466 | 7340 | 1.7913 | - | - |
+| 0.4472 | 7350 | 1.9289 | - | - |
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+| 0.4502 | 7400 | 1.9099 | - | - |
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+| 0.4514 | 7420 | 1.6221 | - | - |
+| 0.4520 | 7430 | 1.3471 | - | - |
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+| 0.4532 | 7450 | 1.7363 | - | - |
+| 0.4539 | 7460 | 1.6542 | - | - |
+| 0.4545 | 7470 | 1.4896 | - | - |
+| 0.4551 | 7480 | 1.7227 | - | - |
+| 0.4557 | 7490 | 1.9539 | - | - |
+| 0.4563 | 7500 | 1.6945 | 1.3835 | 0.8243 |
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+| 0.4575 | 7520 | 1.6797 | - | - |
+| 0.4581 | 7530 | 1.4601 | - | - |
+| 0.4587 | 7540 | 1.7558 | - | - |
+| 0.4593 | 7550 | 1.3169 | - | - |
+| 0.4599 | 7560 | 1.3422 | - | - |
+| 0.4605 | 7570 | 1.9374 | - | - |
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+| 0.4618 | 7590 | 1.7853 | - | - |
+| 0.4624 | 7600 | 1.518 | - | - |
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+| 0.4654 | 7650 | 1.4328 | - | - |
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+| 0.4721 | 7760 | 1.8455 | - | - |
+| 0.4727 | 7770 | 1.3723 | - | - |
+| 0.4733 | 7780 | 2.1408 | - | - |
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+| 0.4745 | 7800 | 1.4386 | - | - |
+| 0.4751 | 7810 | 1.7706 | - | - |
+| 0.4758 | 7820 | 1.7328 | - | - |
+| 0.4764 | 7830 | 1.6368 | - | - |
+| 0.4770 | 7840 | 1.8325 | - | - |
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+| 0.4782 | 7860 | 1.6887 | - | - |
+| 0.4788 | 7870 | 1.1931 | - | - |
+| 0.4794 | 7880 | 1.4435 | - | - |
+| 0.4800 | 7890 | 1.5165 | - | - |
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+| 0.4818 | 7920 | 1.9893 | - | - |
+| 0.4824 | 7930 | 1.9035 | - | - |
+| 0.4831 | 7940 | 2.0425 | - | - |
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+| 0.4843 | 7960 | 1.3849 | - | - |
+| 0.4849 | 7970 | 1.9614 | - | - |
+| 0.4855 | 7980 | 1.6126 | - | - |
+| 0.4861 | 7990 | 1.6933 | - | - |
+| 0.4867 | 8000 | 1.7181 | 1.3778 | 0.8175 |
+| 0.4873 | 8010 | 2.1528 | - | - |
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+| 0.4885 | 8030 | 1.7853 | - | - |
+| 0.4891 | 8040 | 1.79 | - | - |
+| 0.4897 | 8050 | 1.5977 | - | - |
+| 0.4904 | 8060 | 1.5511 | - | - |
+| 0.4910 | 8070 | 1.7976 | - | - |
+| 0.4916 | 8080 | 1.81 | - | - |
+| 0.4922 | 8090 | 1.5593 | - | - |
+| 0.4928 | 8100 | 1.9106 | - | - |
+| 0.4934 | 8110 | 1.3097 | - | - |
+| 0.4940 | 8120 | 1.4777 | - | - |
+| 0.4946 | 8130 | 1.3517 | - | - |
+| 0.4952 | 8140 | 1.5497 | - | - |
+| 0.4958 | 8150 | 1.7368 | - | - |
+| 0.4964 | 8160 | 1.6545 | - | - |
+| 0.4970 | 8170 | 1.6929 | - | - |
+| 0.4977 | 8180 | 1.4323 | - | - |
+| 0.4983 | 8190 | 1.5734 | - | - |
+| 0.4989 | 8200 | 1.5643 | - | - |
+| 0.4995 | 8210 | 1.3835 | - | - |
+| 0.5001 | 8220 | 1.5981 | - | - |
+| 0.5007 | 8230 | 1.4588 | - | - |
+| 0.5013 | 8240 | 1.3868 | - | - |
+| 0.5019 | 8250 | 1.2571 | - | - |
+| 0.5025 | 8260 | 1.277 | - | - |
+| 0.5031 | 8270 | 1.5071 | - | - |
+| 0.5037 | 8280 | 1.336 | - | - |
+| 0.5043 | 8290 | 1.5977 | - | - |
+| 0.5050 | 8300 | 1.2451 | - | - |
+| 0.5056 | 8310 | 1.5968 | - | - |
+| 0.5062 | 8320 | 1.6694 | - | - |
+| 0.5068 | 8330 | 1.1109 | - | - |
+| 0.5074 | 8340 | 2.2767 | - | - |
+| 0.5080 | 8350 | 1.3383 | - | - |
+| 0.5086 | 8360 | 1.5102 | - | - |
+| 0.5092 | 8370 | 1.939 | - | - |
+| 0.5098 | 8380 | 1.7686 | - | - |
+| 0.5104 | 8390 | 1.8728 | - | - |
+| 0.5110 | 8400 | 1.5706 | - | - |
+| 0.5117 | 8410 | 1.5601 | - | - |
+| 0.5123 | 8420 | 1.5278 | - | - |
+| 0.5129 | 8430 | 1.7908 | - | - |
+| 0.5135 | 8440 | 1.7933 | - | - |
+| 0.5141 | 8450 | 1.2068 | - | - |
+| 0.5147 | 8460 | 1.6638 | - | - |
+| 0.5153 | 8470 | 1.5271 | - | - |
+| 0.5159 | 8480 | 1.2856 | - | - |
+| 0.5165 | 8490 | 1.3721 | - | - |
+| 0.5171 | 8500 | 1.5358 | 1.3578 | 0.8231 |
+| 0.5177 | 8510 | 1.527 | - | - |
+| 0.5183 | 8520 | 1.3677 | - | - |
+| 0.5190 | 8530 | 1.5354 | - | - |
+| 0.5196 | 8540 | 1.3059 | - | - |
+| 0.5202 | 8550 | 1.7313 | - | - |
+| 0.5208 | 8560 | 1.4281 | - | - |
+| 0.5214 | 8570 | 1.6653 | - | - |
+| 0.5220 | 8580 | 1.0879 | - | - |
+| 0.5226 | 8590 | 1.6085 | - | - |
+| 0.5232 | 8600 | 1.568 | - | - |
+| 0.5238 | 8610 | 1.9912 | - | - |
+| 0.5244 | 8620 | 1.913 | - | - |
+| 0.5250 | 8630 | 1.4899 | - | - |
+| 0.5256 | 8640 | 1.6354 | - | - |
+| 0.5263 | 8650 | 1.7563 | - | - |
+| 0.5269 | 8660 | 1.9818 | - | - |
+| 0.5275 | 8670 | 1.4272 | - | - |
+| 0.5281 | 8680 | 1.4427 | - | - |
+| 0.5287 | 8690 | 1.6859 | - | - |
+| 0.5293 | 8700 | 1.6195 | - | - |
+| 0.5299 | 8710 | 1.6415 | - | - |
+| 0.5305 | 8720 | 1.4718 | - | - |
+| 0.5311 | 8730 | 1.2839 | - | - |
+| 0.5317 | 8740 | 1.7617 | - | - |
+| 0.5323 | 8750 | 1.7704 | - | - |
+| 0.5329 | 8760 | 1.4339 | - | - |
+| 0.5336 | 8770 | 1.2745 | - | - |
+| 0.5342 | 8780 | 1.474 | - | - |
+| 0.5348 | 8790 | 1.6072 | - | - |
+| 0.5354 | 8800 | 1.6181 | - | - |
+| 0.5360 | 8810 | 1.7749 | - | - |
+| 0.5366 | 8820 | 1.5674 | - | - |
+| 0.5372 | 8830 | 1.7084 | - | - |
+| 0.5378 | 8840 | 1.5086 | - | - |
+| 0.5384 | 8850 | 1.3243 | - | - |
+| 0.5390 | 8860 | 1.5248 | - | - |
+| 0.5396 | 8870 | 1.6092 | - | - |
+| 0.5402 | 8880 | 1.8286 | - | - |
+| 0.5409 | 8890 | 1.4337 | - | - |
+| 0.5415 | 8900 | 1.9393 | - | - |
+| 0.5421 | 8910 | 1.6412 | - | - |
+| 0.5427 | 8920 | 1.2774 | - | - |
+| 0.5433 | 8930 | 1.1121 | - | - |
+| 0.5439 | 8940 | 1.5913 | - | - |
+| 0.5445 | 8950 | 2.1098 | - | - |
+| 0.5451 | 8960 | 1.3627 | - | - |
+| 0.5457 | 8970 | 1.8817 | - | - |
+| 0.5463 | 8980 | 1.1466 | - | - |
+| 0.5469 | 8990 | 1.427 | - | - |
+| 0.5475 | 9000 | 1.4717 | 1.4102 | 0.8178 |
+| 0.5482 | 9010 | 1.509 | - | - |
+| 0.5488 | 9020 | 1.5914 | - | - |
+| 0.5494 | 9030 | 1.7844 | - | - |
+| 0.5500 | 9040 | 1.6509 | - | - |
+| 0.5506 | 9050 | 1.7327 | - | - |
+| 0.5512 | 9060 | 1.4727 | - | - |
+| 0.5518 | 9070 | 1.4369 | - | - |
+| 0.5524 | 9080 | 1.8911 | - | - |
+| 0.5530 | 9090 | 1.5244 | - | - |
+| 0.5536 | 9100 | 1.5994 | - | - |
+| 0.5542 | 9110 | 1.9137 | - | - |
+| 0.5548 | 9120 | 1.4161 | - | - |
+| 0.5555 | 9130 | 1.5631 | - | - |
+| 0.5561 | 9140 | 1.7852 | - | - |
+| 0.5567 | 9150 | 1.4497 | - | - |
+| 0.5573 | 9160 | 1.2413 | - | - |
+| 0.5579 | 9170 | 1.1672 | - | - |
+| 0.5585 | 9180 | 1.6636 | - | - |
+| 0.5591 | 9190 | 1.4866 | - | - |
+| 0.5597 | 9200 | 1.6563 | - | - |
+| 0.5603 | 9210 | 2.0463 | - | - |
+| 0.5609 | 9220 | 1.2139 | - | - |
+| 0.5615 | 9230 | 1.3252 | - | - |
+| 0.5621 | 9240 | 1.6008 | - | - |
+| 0.5628 | 9250 | 1.8193 | - | - |
+| 0.5634 | 9260 | 1.6998 | - | - |
+| 0.5640 | 9270 | 1.551 | - | - |
+| 0.5646 | 9280 | 1.3007 | - | - |
+| 0.5652 | 9290 | 1.6006 | - | - |
+| 0.5658 | 9300 | 1.7028 | - | - |
+| 0.5664 | 9310 | 1.7579 | - | - |
+| 0.5670 | 9320 | 1.7845 | - | - |
+| 0.5676 | 9330 | 1.6506 | - | - |
+| 0.5682 | 9340 | 1.5992 | - | - |
+| 0.5688 | 9350 | 1.9422 | - | - |
+| 0.5694 | 9360 | 1.9625 | - | - |
+| 0.5701 | 9370 | 1.6415 | - | - |
+| 0.5707 | 9380 | 1.5999 | - | - |
+| 0.5713 | 9390 | 1.129 | - | - |
+| 0.5719 | 9400 | 1.7184 | - | - |
+| 0.5725 | 9410 | 1.618 | - | - |
+| 0.5731 | 9420 | 1.7857 | - | - |
+| 0.5737 | 9430 | 1.7363 | - | - |
+| 0.5743 | 9440 | 1.3789 | - | - |
+| 0.5749 | 9450 | 1.6749 | - | - |
+| 0.5755 | 9460 | 1.4316 | - | - |
+| 0.5761 | 9470 | 1.2857 | - | - |
+| 0.5767 | 9480 | 1.2245 | - | - |
+| 0.5774 | 9490 | 1.7602 | - | - |
+| 0.5780 | 9500 | 1.4389 | 1.3897 | 0.8258 |
+| 0.5786 | 9510 | 1.6818 | - | - |
+| 0.5792 | 9520 | 1.38 | - | - |
+| 0.5798 | 9530 | 1.1306 | - | - |
+| 0.5804 | 9540 | 1.6627 | - | - |
+| 0.5810 | 9550 | 1.4518 | - | - |
+| 0.5816 | 9560 | 1.8925 | - | - |
+| 0.5822 | 9570 | 1.9371 | - | - |
+| 0.5828 | 9580 | 1.7479 | - | - |
+| 0.5834 | 9590 | 1.5293 | - | - |
+| 0.5840 | 9600 | 1.5156 | - | - |
+| 0.5847 | 9610 | 1.6572 | - | - |
+| 0.5853 | 9620 | 1.7011 | - | - |
+| 0.5859 | 9630 | 1.2592 | - | - |
+| 0.5865 | 9640 | 1.9584 | - | - |
+| 0.5871 | 9650 | 1.4101 | - | - |
+| 0.5877 | 9660 | 1.7749 | - | - |
+| 0.5883 | 9670 | 1.4748 | - | - |
+| 0.5889 | 9680 | 1.6937 | - | - |
+| 0.5895 | 9690 | 1.371 | - | - |
+| 0.5901 | 9700 | 1.5079 | - | - |
+| 0.5907 | 9710 | 1.4735 | - | - |
+| 0.5913 | 9720 | 1.1619 | - | - |
+| 0.5920 | 9730 | 1.6992 | - | - |
+| 0.5926 | 9740 | 1.3543 | - | - |
+| 0.5932 | 9750 | 1.8505 | - | - |
+| 0.5938 | 9760 | 1.2082 | - | - |
+| 0.5944 | 9770 | 1.4975 | - | - |
+| 0.5950 | 9780 | 1.5958 | - | - |
+| 0.5956 | 9790 | 1.7489 | - | - |
+| 0.5962 | 9800 | 1.6759 | - | - |
+| 0.5968 | 9810 | 1.5673 | - | - |
+| 0.5974 | 9820 | 1.3411 | - | - |
+| 0.5980 | 9830 | 1.532 | - | - |
+| 0.5986 | 9840 | 1.5557 | - | - |
+| 0.5993 | 9850 | 1.3259 | - | - |
+| 0.5999 | 9860 | 1.5657 | - | - |
+| 0.6005 | 9870 | 1.2814 | - | - |
+| 0.6011 | 9880 | 1.1037 | - | - |
+| 0.6017 | 9890 | 1.1898 | - | - |
+| 0.6023 | 9900 | 1.5659 | - | - |
+| 0.6029 | 9910 | 1.1897 | - | - |
+| 0.6035 | 9920 | 1.6582 | - | - |
+| 0.6041 | 9930 | 1.2092 | - | - |
+| 0.6047 | 9940 | 1.4406 | - | - |
+| 0.6053 | 9950 | 1.3649 | - | - |
+| 0.6059 | 9960 | 1.5076 | - | - |
+| 0.6066 | 9970 | 1.1618 | - | - |
+| 0.6072 | 9980 | 1.0621 | - | - |
+| 0.6078 | 9990 | 1.0977 | - | - |
+| 0.6084 | 10000 | 1.2553 | 1.3001 | 0.8154 |
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+| 0.6096 | 10020 | 1.8555 | - | - |
+| 0.6102 | 10030 | 1.727 | - | - |
+| 0.6108 | 10040 | 1.6654 | - | - |
+| 0.6114 | 10050 | 1.4262 | - | - |
+| 0.6120 | 10060 | 1.3796 | - | - |
+| 0.6126 | 10070 | 1.5312 | - | - |
+| 0.6133 | 10080 | 1.8316 | - | - |
+| 0.6139 | 10090 | 1.3924 | - | - |
+| 0.6145 | 10100 | 1.7344 | - | - |
+| 0.6151 | 10110 | 1.0926 | - | - |
+| 0.6157 | 10120 | 1.1192 | - | - |
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+| 0.6169 | 10140 | 1.6186 | - | - |
+| 0.6175 | 10150 | 1.2103 | - | - |
+| 0.6181 | 10160 | 1.4246 | - | - |
+| 0.6187 | 10170 | 1.592 | - | - |
+| 0.6193 | 10180 | 1.168 | - | - |
+| 0.6199 | 10190 | 1.2142 | - | - |
+| 0.6206 | 10200 | 1.6069 | - | - |
+| 0.6212 | 10210 | 1.7001 | - | - |
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+| 0.6224 | 10230 | 1.6861 | - | - |
+| 0.6230 | 10240 | 1.887 | - | - |
+| 0.6236 | 10250 | 1.2798 | - | - |
+| 0.6242 | 10260 | 1.4773 | - | - |
+| 0.6248 | 10270 | 1.3837 | - | - |
+| 0.6254 | 10280 | 1.8983 | - | - |
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+| 0.6266 | 10300 | 1.4255 | - | - |
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+| 0.6285 | 10330 | 1.2714 | - | - |
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+| 0.6297 | 10350 | 1.6387 | - | - |
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+| 0.6333 | 10410 | 1.6833 | - | - |
+| 0.6339 | 10420 | 1.1938 | - | - |
+| 0.6345 | 10430 | 1.412 | - | - |
+| 0.6352 | 10440 | 1.3658 | - | - |
+| 0.6358 | 10450 | 1.5629 | - | - |
+| 0.6364 | 10460 | 1.2093 | - | - |
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+| 0.6376 | 10480 | 1.4018 | - | - |
+| 0.6382 | 10490 | 1.4735 | - | - |
+| 0.6388 | 10500 | 1.3286 | 1.2855 | 0.8233 |
+| 0.6394 | 10510 | 1.46 | - | - |
+| 0.6400 | 10520 | 1.5266 | - | - |
+| 0.6406 | 10530 | 1.6415 | - | - |
+| 0.6412 | 10540 | 1.5083 | - | - |
+| 0.6418 | 10550 | 1.2792 | - | - |
+| 0.6425 | 10560 | 2.0078 | - | - |
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+| 0.6443 | 10590 | 1.0533 | - | - |
+| 0.6449 | 10600 | 1.9115 | - | - |
+| 0.6455 | 10610 | 2.281 | - | - |
+| 0.6461 | 10620 | 1.4983 | - | - |
+| 0.6467 | 10630 | 1.8566 | - | - |
+| 0.6473 | 10640 | 1.3474 | - | - |
+| 0.6479 | 10650 | 1.477 | - | - |
+| 0.6485 | 10660 | 1.5494 | - | - |
+| 0.6491 | 10670 | 1.8323 | - | - |
+| 0.6498 | 10680 | 1.4012 | - | - |
+| 0.6504 | 10690 | 1.5852 | - | - |
+| 0.6510 | 10700 | 1.3499 | - | - |
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+| 0.6522 | 10720 | 1.7463 | - | - |
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+| 0.6534 | 10740 | 1.1842 | - | - |
+| 0.6540 | 10750 | 1.1089 | - | - |
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+| 0.6564 | 10790 | 1.7483 | - | - |
+| 0.6571 | 10800 | 1.8314 | - | - |
+| 0.6577 | 10810 | 1.4631 | - | - |
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+| 0.6595 | 10840 | 1.5896 | - | - |
+| 0.6601 | 10850 | 1.4226 | - | - |
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+| 0.6613 | 10870 | 1.6902 | - | - |
+| 0.6619 | 10880 | 1.6942 | - | - |
+| 0.6625 | 10890 | 1.2605 | - | - |
+| 0.6631 | 10900 | 1.1422 | - | - |
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+| 0.6644 | 10920 | 1.5722 | - | - |
+| 0.6650 | 10930 | 1.5957 | - | - |
+| 0.6656 | 10940 | 1.1748 | - | - |
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+| 0.6668 | 10960 | 1.5614 | - | - |
+| 0.6674 | 10970 | 1.0166 | - | - |
+| 0.6680 | 10980 | 1.5466 | - | - |
+| 0.6686 | 10990 | 1.324 | - | - |
+| 0.6692 | 11000 | 1.3835 | 1.2825 | 0.8215 |
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+| 0.6704 | 11020 | 1.1919 | - | - |
+| 0.6710 | 11030 | 1.5146 | - | - |
+| 0.6717 | 11040 | 1.6479 | - | - |
+| 0.6723 | 11050 | 1.273 | - | - |
+| 0.6729 | 11060 | 1.7737 | - | - |
+| 0.6735 | 11070 | 1.3816 | - | - |
+| 0.6741 | 11080 | 1.6265 | - | - |
+| 0.6747 | 11090 | 1.7054 | - | - |
+| 0.6753 | 11100 | 1.0439 | - | - |
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+| 0.6771 | 11130 | 2.1534 | - | - |
+| 0.6777 | 11140 | 1.5005 | - | - |
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+| 0.6790 | 11160 | 2.0103 | - | - |
+| 0.6796 | 11170 | 1.1585 | - | - |
+| 0.6802 | 11180 | 1.8996 | - | - |
+| 0.6808 | 11190 | 1.6425 | - | - |
+| 0.6814 | 11200 | 1.0645 | - | - |
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+| 0.6826 | 11220 | 1.1813 | - | - |
+| 0.6832 | 11230 | 1.6146 | - | - |
+| 0.6838 | 11240 | 1.2571 | - | - |
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+| 0.6850 | 11260 | 1.8446 | - | - |
+| 0.6856 | 11270 | 1.4882 | - | - |
+| 0.6863 | 11280 | 1.3993 | - | - |
+| 0.6869 | 11290 | 1.0834 | - | - |
+| 0.6875 | 11300 | 1.4249 | - | - |
+| 0.6881 | 11310 | 1.5861 | - | - |
+| 0.6887 | 11320 | 1.3802 | - | - |
+| 0.6893 | 11330 | 1.3079 | - | - |
+| 0.6899 | 11340 | 2.0982 | - | - |
+| 0.6905 | 11350 | 1.9461 | - | - |
+| 0.6911 | 11360 | 1.2621 | - | - |
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+| 0.6923 | 11380 | 1.4284 | - | - |
+| 0.6929 | 11390 | 1.4797 | - | - |
+| 0.6936 | 11400 | 1.2065 | - | - |
+| 0.6942 | 11410 | 1.4993 | - | - |
+| 0.6948 | 11420 | 1.4127 | - | - |
+| 0.6954 | 11430 | 1.1966 | - | - |
+| 0.6960 | 11440 | 1.7712 | - | - |
+| 0.6966 | 11450 | 1.3437 | - | - |
+| 0.6972 | 11460 | 1.2769 | - | - |
+| 0.6978 | 11470 | 1.1741 | - | - |
+| 0.6984 | 11480 | 1.7074 | - | - |
+| 0.6990 | 11490 | 1.6412 | - | - |
+| 0.6996 | 11500 | 1.4801 | 1.2417 | 0.8235 |
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+| 0.7009 | 11520 | 1.6028 | - | - |
+| 0.7015 | 11530 | 1.5173 | - | - |
+| 0.7021 | 11540 | 1.2637 | - | - |
+| 0.7027 | 11550 | 1.5151 | - | - |
+| 0.7033 | 11560 | 0.9938 | - | - |
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+| 0.7057 | 11600 | 1.5303 | - | - |
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+| 0.7094 | 11660 | 1.8622 | - | - |
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+| 0.7106 | 11680 | 1.4854 | - | - |
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+| 0.7118 | 11700 | 1.1033 | - | - |
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+| 0.7130 | 11720 | 1.5136 | - | - |
+| 0.7136 | 11730 | 1.3675 | - | - |
+| 0.7142 | 11740 | 1.1926 | - | - |
+| 0.7149 | 11750 | 1.5906 | - | - |
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+| 0.7161 | 11770 | 1.3581 | - | - |
+| 0.7167 | 11780 | 1.0382 | - | - |
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+| 0.7179 | 11800 | 1.1324 | - | - |
+| 0.7185 | 11810 | 1.3408 | - | - |
+| 0.7191 | 11820 | 1.5514 | - | - |
+| 0.7197 | 11830 | 1.4818 | - | - |
+| 0.7203 | 11840 | 1.6802 | - | - |
+| 0.7209 | 11850 | 1.3433 | - | - |
+| 0.7215 | 11860 | 1.4655 | - | - |
+| 0.7222 | 11870 | 1.3235 | - | - |
+| 0.7228 | 11880 | 1.1081 | - | - |
+| 0.7234 | 11890 | 1.7482 | - | - |
+| 0.7240 | 11900 | 1.7194 | - | - |
+| 0.7246 | 11910 | 1.38 | - | - |
+| 0.7252 | 11920 | 1.8826 | - | - |
+| 0.7258 | 11930 | 1.5682 | - | - |
+| 0.7264 | 11940 | 1.578 | - | - |
+| 0.7270 | 11950 | 1.4856 | - | - |
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+| 0.7282 | 11970 | 1.5247 | - | - |
+| 0.7288 | 11980 | 1.4876 | - | - |
+| 0.7295 | 11990 | 2.2117 | - | - |
+| 0.7301 | 12000 | 1.5645 | 1.1920 | 0.8275 |
+| 0.7307 | 12010 | 1.4485 | - | - |
+| 0.7313 | 12020 | 1.5233 | - | - |
+| 0.7319 | 12030 | 1.3488 | - | - |
+| 0.7325 | 12040 | 1.4454 | - | - |
+| 0.7331 | 12050 | 1.2349 | - | - |
+| 0.7337 | 12060 | 1.8142 | - | - |
+| 0.7343 | 12070 | 1.4516 | - | - |
+| 0.7349 | 12080 | 1.675 | - | - |
+| 0.7355 | 12090 | 1.6949 | - | - |
+| 0.7361 | 12100 | 1.6257 | - | - |
+| 0.7368 | 12110 | 1.2147 | - | - |
+| 0.7374 | 12120 | 1.0857 | - | - |
+| 0.7380 | 12130 | 1.5563 | - | - |
+| 0.7386 | 12140 | 1.3987 | - | - |
+| 0.7392 | 12150 | 1.7647 | - | - |
+| 0.7398 | 12160 | 1.6893 | - | - |
+| 0.7404 | 12170 | 0.9929 | - | - |
+| 0.7410 | 12180 | 1.2464 | - | - |
+| 0.7416 | 12190 | 1.4941 | - | - |
+| 0.7422 | 12200 | 1.5126 | - | - |
+| 0.7428 | 12210 | 1.2593 | - | - |
+| 0.7434 | 12220 | 1.8845 | - | - |
+| 0.7441 | 12230 | 1.4532 | - | - |
+| 0.7447 | 12240 | 1.0518 | - | - |
+| 0.7453 | 12250 | 1.2119 | - | - |
+| 0.7459 | 12260 | 1.4779 | - | - |
+| 0.7465 | 12270 | 1.5664 | - | - |
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+| 0.9947 | 16350 | 1.3795 | - | - |
+| 0.9953 | 16360 | 1.1348 | - | - |
+| 0.9959 | 16370 | 1.5053 | - | - |
+| 0.9965 | 16380 | 1.349 | - | - |
+| 0.9971 | 16390 | 1.1635 | - | - |
+| 0.9977 | 16400 | 1.4926 | - | - |
+| 0.9984 | 16410 | 1.676 | - | - |
+| 0.9990 | 16420 | 1.4676 | - | - |
+| 0.9996 | 16430 | 1.053 | - | - |
+
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.2.0
+- Transformers: 4.44.2
+- PyTorch: 2.4.1+cu121
+- Accelerate: 0.34.2
+- Datasets: 3.0.1
+- Tokenizers: 0.19.1
+
+## 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",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+ title={Matryoshka Representation Learning},
+ author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+ year={2024},
+ eprint={2205.13147},
+ archivePrefix={arXiv},
+ primaryClass={cs.LG}
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
+ author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+ year={2017},
+ eprint={1705.00652},
+ archivePrefix={arXiv},
+ primaryClass={cs.CL}
+}
+```
+
+
+
+
+
+
\ No newline at end of file