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1_Pooling/config.json

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+{
+    "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
+}

README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:19759758
+- loss:CoSENTLoss
+base_model: KhaledReda/all-MiniLM-L6-v34-pair_score
+widget:
+- source_sentence: wide leg pants
+  sentences:
+  - carefree d.inti.wash gre.tea a.vera 200 m category pharmacies women s care feminine
+    hygiene feminine hygiene tags carefree carefree feminine wash daily intimate wash
+    feminine wash intimate wash keywords carefree carefree feminine wash daily intimate
+    wash feminine wash intimate wash attrs units 200 m
+  - blue star category fashion jewelry necklace necklace tags blue star necklaces
+    women necklaces sterling silver 925 necklaces sterling silver necklaces silver
+    necklaces necklaces star necklaces keywords necklaces star necklaces attrs gender
+    women brand holley jewelry generic name necklaces product name blue star size
+    free size types of fashion styles everyday wear casual material sterling silver
+    925 silver color blue description sterling silver 925.
+  - porland navy blue dinner plate - 27 cm category home and garden tableware plate
+    and bowl dinner plate tags navy blue plate blue plate dinnerware dinner plate
+    plate porland porland plate keywords dinner plate plate porland porland plate
+    attrs color navy blue description discover the deepest shade of blue with porland
+    s fine and minimal navy blue series. po-nb 18 cp 27
+- source_sentence: gym jersi
+  sentences:
+  - mood tahiti moist. body milk 200 m category beauty skincare body moisturizer body
+    moisturizer tags body milk moisturizing body milk mood body milk mood tahiti body
+    milk keywords body milk moisturizing body milk mood body milk mood tahiti body
+    milk attrs units 200 m
+  - t.denim pants buckled leg 419412 category fashion casual wear trousers pants tags
+    women buckled leg pants pants t pants keywords buckled leg pants pants t pants
+    attrs gender women brand venti generic name pants size s types of fashion styles
+    casual everyday wear cut buckled leg material denim
+  - world citizen hoodie - light blue category fashion casual wear top sweatshirt
+    tags unisex hoodie men hoodie women hoodie cotton hoodie oversized hoodie hoodie
+    world citizen hoodie keywords hoodie world citizen hoodie attrs gender unisex
+    women men brand unlabeled cult generic name hoodie product name world citizen
+    size s types of fashion styles casual streetwear everyday wear sleeve style long
+    fit loose oversized material cotton color light blue description unisex 100 cotton
+    hoodie. cold wash. wash inside out. oversized fit.
+- source_sentence: sekem
+  sentences:
+  - y by ysl le parfum 100 ml for men category beauty fragrances perfume perfume tags
+    men eau de parfum fragrances perfume y by ysl perfume yves saint laurent yves
+    saint laurent perfume cologne fragrance y by ysl cologne y by ysl fragrance yves
+    saint laurent cologne yves saint laurent fragrance keywords perfume y by ysl perfume
+    yves saint laurent yves saint laurent perfume cologne fragrance y by ysl cologne
+    y by ysl fragrance yves saint laurent cologne yves saint laurent fragrance attrs
+    units 100 millilitre target group men
+  - hawawshi sandwich with cheese category restaurants egyptian deli hawawshi tags
+    hawawshi hawawshi cheese hawawshi gbnah hawawshi with gbnah keywords hawawshi
+    hawawshi cheese hawawshi gbnah hawawshi with gbnah attrs restaurants modifier
+    sandwich
+  - yoga wheel category sports fitness and training yoga yoga tags flexana yoga wheel
+    flexibility yoga wheel natural rubber yoga wheel flexana wheel keywords wheel
+    attrs sport yoga description flexana s yoga wheel gives you the assistance you
+    need to comfortably get into yoga poses that require a higher scale of flexibility
+    with a bonus benefit of experiencing longer and wider stretches throughout the
+    poses. material natural rubber non flexing abs.
+- source_sentence: cortado kahwa
+  sentences:
+  - ice cream torte with nutella category restaurants bakery and cakes sweet sweet
+    tags ice cream torte nutella ice cream torte torte torte nutella keywords ice
+    cream torte nutella ice cream torte torte torte nutella
+  - ash grey heather yoga flare pants - xxl category fashion sportswear trousers pants
+    tags highwaisted pants yoga pants highresistance fabric pants nylon yoga pants
+    elastane yoga pants grey pants women pants ash flare pants ash pants flare pants
+    pants keywords ash flare pants ash pants flare pants pants attrs gender women
+    brand fit freak generic name pants size xxl types of fashion styles comfortable
+    waist line high-waisted cut flared material nylon elastane color grey activity
+    yoga country of origin egypt sport yoga description high-waisted flared comfort
+    pants. high-resistance fabric. 80 nylon 20 elastane. comfortable. flared. made
+    in egypt.
+  - roasted garlic mayonnaise category restaurants street food condiment mayonnaise
+    tags garlic mayonnaise mayonnaise roasted garlic mayonnaise keywords garlic mayonnaise
+    mayonnaise roasted garlic mayonnaise
+- source_sentence: sanosan
+  sentences:
+  - v-neck cotton vest black category fashion designer wear outerwear vest tags classic
+    suit vest buttonup front pockets vest false welt pockets vest adjustable strap
+    vest cotton vest vest vneck vest gilet vneck gilet keywords vest vneck vest gilet
+    vneck gilet attrs gender women brand palma generic name vest size s features adjustable
+    strap with buckle types of fashion styles classic formal collar v-neck closure
+    style button-up pocket style false welt pockets sleeve style sleeveless material
+    cotton color black fashion style v-neck description our v-neck cotton vest inspired
+    by classic suit vests offers versatility and style. the button-up front and false
+    welt pockets on the front add a touch of elegance while the adjustable strap with
+    a buckle on the back ensures comfort and customization. whether for formal occasions
+    or adding a polished touch to your everyday attire this refined cotton vest is
+    a stylish choice and a perfect addition to your wardrobe. material 100 cotton.
+    model is wearing size m.
+  - coca cola category restaurants juices and drinks beverage soda tags coca cola
+    soft drinks coke cola soft beverages keywords coca cola soft drinks coke cola
+    soft beverages
+  - lash brow category beauty skincare eye treatment eye treatment tags lashes growth
+    serum brow serum lash serum keywords brow serum lash serum description this natural
+    growth serum causes existing lashes to become longer and stimulates growth in
+    hair follicles not currently producing lashes. it has shown effects after 4 weeks
+    of usage which is why this product is considered to be highly effective.
+datasets:
+- KhaledReda/pairs_with_scores_v30
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# all-MiniLM-L6-v35-pair_score
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [KhaledReda/all-MiniLM-L6-v34-pair_score](https://huggingface.co/KhaledReda/all-MiniLM-L6-v34-pair_score) on the [pairs_with_scores_v30](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v30) dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [KhaledReda/all-MiniLM-L6-v34-pair_score](https://huggingface.co/KhaledReda/all-MiniLM-L6-v34-pair_score) <!-- at revision d2b91e1849f686679f9d3c36b709aba13855e02f -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [pairs_with_scores_v30](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v30)
+- **Language:** en
+- **License:** apache-2.0
+
+### 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': 256, 'do_lower_case': False, 'architecture': '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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'sanosan',
+    'v-neck cotton vest black category fashion designer wear outerwear vest tags classic suit vest buttonup front pockets vest false welt pockets vest adjustable strap vest cotton vest vest vneck vest gilet vneck gilet keywords vest vneck vest gilet vneck gilet attrs gender women brand palma generic name vest size s features adjustable strap with buckle types of fashion styles classic formal collar v-neck closure style button-up pocket style false welt pockets sleeve style sleeveless material cotton color black fashion style v-neck description our v-neck cotton vest inspired by classic suit vests offers versatility and style. the button-up front and false welt pockets on the front add a touch of elegance while the adjustable strap with a buckle on the back ensures comfort and customization. whether for formal occasions or adding a polished touch to your everyday attire this refined cotton vest is a stylish choice and a perfect addition to your wardrobe. material 100 cotton. model is wearing size m.',
+    'lash brow category beauty skincare eye treatment eye treatment tags lashes growth serum brow serum lash serum keywords brow serum lash serum description this natural growth serum causes existing lashes to become longer and stimulates growth in hair follicles not currently producing lashes. it has shown effects after 4 weeks of usage which is why this product is considered to be highly effective.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[ 1.0000, -0.0478, -0.1530],
+#         [-0.0478,  1.0000, -0.0644],
+#         [-0.1530, -0.0644,  1.0000]])
+```
+
+<!--
+### 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.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### pairs_with_scores_v30
+
+* Dataset: [pairs_with_scores_v30](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v30) at [33eac44](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v30/tree/33eac44364ee0cea401205e283b87ef7b3efbef3)
+* Size: 19,759,758 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                           | score                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                              | float                                                          |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 5.54 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 86.78 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.04</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                      | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | score            |
+  |:-------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>boots 3780 nort</code>   | <code>mediterranean blue mug category home and garden drinkware mug mug tags blue mug porcelain mug mediterranean mug mug keywords mediterranean mug mug attrs brand inches home generic name mug product name mediterranean measurements 350 ml number of pieces 1 shape mug purpose drinking material porcelain color blue description embrace the warmth and affection with mediterranean blue mug that makes your heartwarming moments even more special. product specifications material high quality porcelain.</code> | <code>0.0</code> |
+  | <code>men shorts</code>        | <code>ardell natural lashes black /125 category beauty cosmetics eye make-up eyelash tags ardell ardell lashes ardell natural lashes natural lashes keywords ardell ardell lashes ardell natural lashes natural lashes attrs color black</code>                                                                                                                                                                                                                                                                              | <code>0.0</code> |
+  | <code>boots 2316 b-16-j</code> | <code>fresh iced pink grapefruit juice category restaurants juices and drinks beverage juice tags fresh iced juice fresh juice iced juice juice pink grapefruit juice keywords fresh juice iced juice juice pink grapefruit juice</code>                                                                                                                                                                                                                                                                                     | <code>0.0</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### pairs_with_scores_v30
+
+* Dataset: [pairs_with_scores_v30](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v30) at [33eac44](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v30/tree/33eac44364ee0cea401205e283b87ef7b3efbef3)
+* Size: 99,296 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                           | score                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                              | float                                                          |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 5.33 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 86.78 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.05</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                      | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | score            |
+  |:-------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>accuchek guide me</code> | <code>bright peach cover up category fashion swimwear bathing cover bathing cover tags peach cover up cover-up bright cover up cover up keywords bright cover up cover up attrs gender women brand pepla generic name cover up color bright peach</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | <code>0.0</code> |
+  | <code>medical accessory</code> | <code>ferragamo for men edt 100 ml category beauty fragrances perfume perfume tags men eau de toilette fragrances eau de toilette ferragamo ferragamo eau de toilette perfume cologne fragrance keywords eau de toilette ferragamo ferragamo eau de toilette perfume cologne fragrance attrs units 100 millilitre target group men</code>                                                                                                                                                                                                                                                                                                                                                                                                             | <code>0.0</code> |
+  | <code>muslin kimono</code>     | <code>fake date cookies category restaurants keto sweet biscuit tags coconut flour cookies grass fed butter cookies ghee cookies cinnamon powder cookies raw cacao cookies sesame cookies low carb cookies diabetic friendly cookies keto friendly cookies gluten free cookies almond flour cookies cookies date cookies fake date cookies keywords almond flour cookies cookies date cookies fake date cookies description almond flour - coconut flour - grass fed butter - ghee - egg - cinnamon powder - raw cacao - sesame nutritional facts per 10 gr 58 kcal - 4.6 gr fat - 1 gr net carb - 1.2 gr fiber - 1.9 gr protein suitable for low carb - diabetic friendly - keto friendly - gluten free 14 day on shelf air tight container .</code> | <code>0.0</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: 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`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: 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`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `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
+- `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
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step   | Training Loss |
+|:------:|:------:|:-------------:|
+| 0.0006 | 100    | 0.0576        |
+| 0.0013 | 200    | 0.097         |
+| 0.0019 | 300    | 0.0513        |
+| 0.0026 | 400    | 0.0723        |
+| 0.0032 | 500    | 0.0808        |
+| 0.0039 | 600    | 0.0828        |
+| 0.0045 | 700    | 0.0394        |
+| 0.0052 | 800    | 0.0565        |
+| 0.0058 | 900    | 0.0699        |
+| 0.0065 | 1000   | 0.0595        |
+| 0.0071 | 1100   | 0.0206        |
+| 0.0078 | 1200   | 0.0709        |
+| 0.0084 | 1300   | 0.034         |
+| 0.0091 | 1400   | 0.0795        |
+| 0.0097 | 1500   | 0.0271        |
+| 0.0104 | 1600   | 0.0301        |
+| 0.0110 | 1700   | 0.076         |
+| 0.0117 | 1800   | 0.0763        |
+| 0.0123 | 1900   | 0.0233        |
+| 0.0130 | 2000   | 0.0178        |
+| 0.0136 | 2100   | 0.0271        |
+| 0.0143 | 2200   | 0.1313        |
+| 0.0149 | 2300   | 0.0174        |
+| 0.0155 | 2400   | 0.0171        |
+| 0.0162 | 2500   | 0.0112        |
+| 0.0168 | 2600   | 0.0196        |
+| 0.0175 | 2700   | 0.0114        |
+| 0.0181 | 2800   | 0.1054        |
+| 0.0188 | 2900   | 0.0223        |
+| 0.0194 | 3000   | 0.0224        |
+| 0.0201 | 3100   | 0.0289        |
+| 0.0207 | 3200   | 0.0322        |
+| 0.0214 | 3300   | 0.0534        |
+| 0.0220 | 3400   | 0.0184        |
+| 0.0227 | 3500   | 0.0416        |
+| 0.0233 | 3600   | 0.0334        |
+| 0.0240 | 3700   | 0.0429        |
+| 0.0246 | 3800   | 0.0137        |
+| 0.0253 | 3900   | 0.0164        |
+| 0.0259 | 4000   | 0.0565        |
+| 0.0266 | 4100   | 0.0302        |
+| 0.0272 | 4200   | 0.0232        |
+| 0.0279 | 4300   | 0.0117        |
+| 0.0285 | 4400   | 0.0188        |
+| 0.0291 | 4500   | 0.0161        |
+| 0.0298 | 4600   | 0.0484        |
+| 0.0304 | 4700   | 0.0085        |
+| 0.0311 | 4800   | 0.0142        |
+| 0.0317 | 4900   | 0.0083        |
+| 0.0324 | 5000   | 0.0245        |
+| 0.0330 | 5100   | 0.0277        |
+| 0.0337 | 5200   | 0.0085        |
+| 0.0343 | 5300   | 0.0711        |
+| 0.0350 | 5400   | 0.0072        |
+| 0.0356 | 5500   | 0.0376        |
+| 0.0363 | 5600   | 0.0143        |
+| 0.0369 | 5700   | 0.0074        |
+| 0.0376 | 5800   | 0.0207        |
+| 0.0382 | 5900   | 0.0085        |
+| 0.0389 | 6000   | 0.0262        |
+| 0.0395 | 6100   | 0.0115        |
+| 0.0402 | 6200   | 0.0137        |
+| 0.0408 | 6300   | 0.0217        |
+| 0.0415 | 6400   | 0.0566        |
+| 0.0421 | 6500   | 0.0112        |
+| 0.0428 | 6600   | 0.0151        |
+| 0.0434 | 6700   | 0.0085        |
+| 0.0440 | 6800   | 0.037         |
+| 0.0447 | 6900   | 0.0077        |
+| 0.0453 | 7000   | 0.0093        |
+| 0.0460 | 7100   | 0.016         |
+| 0.0466 | 7200   | 0.0135        |
+| 0.0473 | 7300   | 0.063         |
+| 0.0479 | 7400   | 0.0097        |
+| 0.0486 | 7500   | 0.0418        |
+| 0.0492 | 7600   | 0.0161        |
+| 0.0499 | 7700   | 0.0113        |
+| 0.0505 | 7800   | 0.0069        |
+| 0.0512 | 7900   | 0.0578        |
+| 0.0518 | 8000   | 0.0131        |
+| 0.0525 | 8100   | 0.0331        |
+| 0.0531 | 8200   | 0.0164        |
+| 0.0538 | 8300   | 0.0155        |
+| 0.0544 | 8400   | 0.0061        |
+| 0.0551 | 8500   | 0.0174        |
+| 0.0557 | 8600   | 0.0116        |
+| 0.0564 | 8700   | 0.0089        |
+| 0.0570 | 8800   | 0.0192        |
+| 0.0577 | 8900   | 0.0073        |
+| 0.0583 | 9000   | 0.0466        |
+| 0.0589 | 9100   | 0.0156        |
+| 0.0596 | 9200   | 0.0136        |
+| 0.0602 | 9300   | 0.007         |
+| 0.0609 | 9400   | 0.0064        |
+| 0.0615 | 9500   | 0.0362        |
+| 0.0622 | 9600   | 0.0034        |
+| 0.0628 | 9700   | 0.0287        |
+| 0.0635 | 9800   | 0.0148        |
+| 0.0641 | 9900   | 0.0096        |
+| 0.0648 | 10000  | 0.0084        |
+| 0.0654 | 10100  | 0.0584        |
+| 0.0661 | 10200  | 0.0096        |
+| 0.0667 | 10300  | 0.0103        |
+| 0.0674 | 10400  | 0.0086        |
+| 0.0680 | 10500  | 0.0098        |
+| 0.0687 | 10600  | 0.0105        |
+| 0.0693 | 10700  | 0.013         |
+| 0.0700 | 10800  | 0.0246        |
+| 0.0706 | 10900  | 0.0067        |
+| 0.0713 | 11000  | 0.0043        |
+| 0.0719 | 11100  | 0.0524        |
+| 0.0726 | 11200  | 0.0061        |
+| 0.0732 | 11300  | 0.0179        |
+| 0.0738 | 11400  | 0.0275        |
+| 0.0745 | 11500  | 0.0194        |
+| 0.0751 | 11600  | 0.009         |
+| 0.0758 | 11700  | 0.0272        |
+| 0.0764 | 11800  | 0.0086        |
+| 0.0771 | 11900  | 0.0413        |
+| 0.0777 | 12000  | 0.0289        |
+| 0.0784 | 12100  | 0.0229        |
+| 0.0790 | 12200  | 0.0078        |
+| 0.0797 | 12300  | 0.0638        |
+| 0.0803 | 12400  | 0.0077        |
+| 0.0810 | 12500  | 0.0051        |
+| 0.0816 | 12600  | 0.04          |
+| 0.0823 | 12700  | 0.0163        |
+| 0.0829 | 12800  | 0.0621        |
+| 0.0836 | 12900  | 0.0243        |
+| 0.0842 | 13000  | 0.0133        |
+| 0.0849 | 13100  | 0.0071        |
+| 0.0855 | 13200  | 0.0083        |
+| 0.0862 | 13300  | 0.0167        |
+| 0.0868 | 13400  | 0.0249        |
+| 0.0874 | 13500  | 0.0058        |
+| 0.0881 | 13600  | 0.0113        |
+| 0.0887 | 13700  | 0.0349        |
+| 0.0894 | 13800  | 0.0216        |
+| 0.0900 | 13900  | 0.0327        |
+| 0.0907 | 14000  | 0.0151        |
+| 0.0913 | 14100  | 0.0469        |
+| 0.0920 | 14200  | 0.0073        |
+| 0.0926 | 14300  | 0.0212        |
+| 0.0933 | 14400  | 0.0092        |
+| 0.0939 | 14500  | 0.0045        |
+| 0.0946 | 14600  | 0.0234        |
+| 0.0952 | 14700  | 0.0131        |
+| 0.0959 | 14800  | 0.0062        |
+| 0.0965 | 14900  | 0.0088        |
+| 0.0972 | 15000  | 0.0102        |
+| 0.0978 | 15100  | 0.0107        |
+| 0.0985 | 15200  | 0.0063        |
+| 0.0991 | 15300  | 0.0418        |
+| 0.0998 | 15400  | 0.0136        |
+| 0.1004 | 15500  | 0.0105        |
+| 0.1011 | 15600  | 0.0276        |
+| 0.1017 | 15700  | 0.0084        |
+| 0.1023 | 15800  | 0.0114        |
+| 0.1030 | 15900  | 0.0099        |
+| 0.1036 | 16000  | 0.0087        |
+| 0.1043 | 16100  | 0.0044        |
+| 0.1049 | 16200  | 0.0081        |
+| 0.1056 | 16300  | 0.0054        |
+| 0.1062 | 16400  | 0.0445        |
+| 0.1069 | 16500  | 0.0322        |
+| 0.1075 | 16600  | 0.0262        |
+| 0.1082 | 16700  | 0.0181        |
+| 0.1088 | 16800  | 0.011         |
+| 0.1095 | 16900  | 0.0118        |
+| 0.1101 | 17000  | 0.0116        |
+| 0.1108 | 17100  | 0.0053        |
+| 0.1114 | 17200  | 0.0205        |
+| 0.1121 | 17300  | 0.0112        |
+| 0.1127 | 17400  | 0.0189        |
+| 0.1134 | 17500  | 0.007         |
+| 0.1140 | 17600  | 0.0221        |
+| 0.1147 | 17700  | 0.0313        |
+| 0.1153 | 17800  | 0.008         |
+| 0.1160 | 17900  | 0.0082        |
+| 0.1166 | 18000  | 0.0519        |
+| 0.1172 | 18100  | 0.0343        |
+| 0.1179 | 18200  | 0.0051        |
+| 0.1185 | 18300  | 0.0129        |
+| 0.1192 | 18400  | 0.024         |
+| 0.1198 | 18500  | 0.0478        |
+| 0.1205 | 18600  | 0.0044        |
+| 0.1211 | 18700  | 0.0066        |
+| 0.1218 | 18800  | 0.0202        |
+| 0.1224 | 18900  | 0.0253        |
+| 0.1231 | 19000  | 0.0235        |
+| 0.1237 | 19100  | 0.0056        |
+| 0.1244 | 19200  | 0.0186        |
+| 0.1250 | 19300  | 0.0388        |
+| 0.1257 | 19400  | 0.054         |
+| 0.1263 | 19500  | 0.0206        |
+| 0.1270 | 19600  | 0.011         |
+| 0.1276 | 19700  | 0.0076        |
+| 0.1283 | 19800  | 0.0431        |
+| 0.1289 | 19900  | 0.0055        |
+| 0.1296 | 20000  | 0.0079        |
+| 0.1302 | 20100  | 0.0082        |
+| 0.1309 | 20200  | 0.0281        |
+| 0.1315 | 20300  | 0.0187        |
+| 0.1321 | 20400  | 0.0561        |
+| 0.1328 | 20500  | 0.0622        |
+| 0.1334 | 20600  | 0.0063        |
+| 0.1341 | 20700  | 0.0167        |
+| 0.1347 | 20800  | 0.0169        |
+| 0.1354 | 20900  | 0.0044        |
+| 0.1360 | 21000  | 0.0122        |
+| 0.1367 | 21100  | 0.0099        |
+| 0.1373 | 21200  | 0.0099        |
+| 0.1380 | 21300  | 0.0061        |
+| 0.1386 | 21400  | 0.0518        |
+| 0.1393 | 21500  | 0.0192        |
+| 0.1399 | 21600  | 0.0274        |
+| 0.1406 | 21700  | 0.0156        |
+| 0.1412 | 21800  | 0.01          |
+| 0.1419 | 21900  | 0.0055        |
+| 0.1425 | 22000  | 0.0119        |
+| 0.1432 | 22100  | 0.0087        |
+| 0.1438 | 22200  | 0.0071        |
+| 0.1445 | 22300  | 0.0073        |
+| 0.1451 | 22400  | 0.0107        |
+| 0.1457 | 22500  | 0.0059        |
+| 0.1464 | 22600  | 0.0082        |
+| 0.1470 | 22700  | 0.0173        |
+| 0.1477 | 22800  | 0.0218        |
+| 0.1483 | 22900  | 0.0173        |
+| 0.1490 | 23000  | 0.0037        |
+| 0.1496 | 23100  | 0.0056        |
+| 0.1503 | 23200  | 0.0031        |
+| 0.1509 | 23300  | 0.0136        |
+| 0.1516 | 23400  | 0.0073        |
+| 0.1522 | 23500  | 0.0478        |
+| 0.1529 | 23600  | 0.0034        |
+| 0.1535 | 23700  | 0.011         |
+| 0.1542 | 23800  | 0.0147        |
+| 0.1548 | 23900  | 0.0369        |
+| 0.1555 | 24000  | 0.017         |
+| 0.1561 | 24100  | 0.0144        |
+| 0.1568 | 24200  | 0.0065        |
+| 0.1574 | 24300  | 0.0862        |
+| 0.1581 | 24400  | 0.0064        |
+| 0.1587 | 24500  | 0.0076        |
+| 0.1594 | 24600  | 0.0077        |
+| 0.1600 | 24700  | 0.0105        |
+| 0.1606 | 24800  | 0.0211        |
+| 0.1613 | 24900  | 0.0099        |
+| 0.1619 | 25000  | 0.0065        |
+| 0.1626 | 25100  | 0.0088        |
+| 0.1632 | 25200  | 0.0049        |
+| 0.1639 | 25300  | 0.0045        |
+| 0.1645 | 25400  | 0.0377        |
+| 0.1652 | 25500  | 0.0106        |
+| 0.1658 | 25600  | 0.0056        |
+| 0.1665 | 25700  | 0.0055        |
+| 0.1671 | 25800  | 0.0049        |
+| 0.1678 | 25900  | 0.0096        |
+| 0.1684 | 26000  | 0.0041        |
+| 0.1691 | 26100  | 0.0256        |
+| 0.1697 | 26200  | 0.006         |
+| 0.1704 | 26300  | 0.0155        |
+| 0.1710 | 26400  | 0.009         |
+| 0.1717 | 26500  | 0.0171        |
+| 0.1723 | 26600  | 0.0228        |
+| 0.1730 | 26700  | 0.0207        |
+| 0.1736 | 26800  | 0.0064        |
+| 0.1743 | 26900  | 0.0044        |
+| 0.1749 | 27000  | 0.0052        |
+| 0.1755 | 27100  | 0.0121        |
+| 0.1762 | 27200  | 0.0128        |
+| 0.1768 | 27300  | 0.0389        |
+| 0.1775 | 27400  | 0.0177        |
+| 0.1781 | 27500  | 0.0153        |
+| 0.1788 | 27600  | 0.0211        |
+| 0.1794 | 27700  | 0.0145        |
+| 0.1801 | 27800  | 0.0238        |
+| 0.1807 | 27900  | 0.0077        |
+| 0.1814 | 28000  | 0.0048        |
+| 0.1820 | 28100  | 0.0096        |
+| 0.1827 | 28200  | 0.0085        |
+| 0.1833 | 28300  | 0.0045        |
+| 0.1840 | 28400  | 0.0101        |
+| 0.1846 | 28500  | 0.006         |
+| 0.1853 | 28600  | 0.0124        |
+| 0.1859 | 28700  | 0.0284        |
+| 0.1866 | 28800  | 0.0078        |
+| 0.1872 | 28900  | 0.0095        |
+| 0.1879 | 29000  | 0.0246        |
+| 0.1885 | 29100  | 0.0109        |
+| 0.1892 | 29200  | 0.0036        |
+| 0.1898 | 29300  | 0.012         |
+| 0.1904 | 29400  | 0.0036        |
+| 0.1911 | 29500  | 0.0183        |
+| 0.1917 | 29600  | 0.005         |
+| 0.1924 | 29700  | 0.0135        |
+| 0.1930 | 29800  | 0.0048        |
+| 0.1937 | 29900  | 0.0202        |
+| 0.1943 | 30000  | 0.0087        |
+| 0.1950 | 30100  | 0.0041        |
+| 0.1956 | 30200  | 0.0105        |
+| 0.1963 | 30300  | 0.0031        |
+| 0.1969 | 30400  | 0.0096        |
+| 0.1976 | 30500  | 0.0206        |
+| 0.1982 | 30600  | 0.0045        |
+| 0.1989 | 30700  | 0.0181        |
+| 0.1995 | 30800  | 0.006         |
+| 0.2002 | 30900  | 0.0065        |
+| 0.2008 | 31000  | 0.0087        |
+| 0.2015 | 31100  | 0.0057        |
+| 0.2021 | 31200  | 0.0235        |
+| 0.2028 | 31300  | 0.0097        |
+| 0.2034 | 31400  | 0.0053        |
+| 0.2040 | 31500  | 0.0144        |
+| 0.2047 | 31600  | 0.039         |
+| 0.2053 | 31700  | 0.0048        |
+| 0.2060 | 31800  | 0.007         |
+| 0.2066 | 31900  | 0.0164        |
+| 0.2073 | 32000  | 0.0079        |
+| 0.2079 | 32100  | 0.0083        |
+| 0.2086 | 32200  | 0.0432        |
+| 0.2092 | 32300  | 0.0061        |
+| 0.2099 | 32400  | 0.0179        |
+| 0.2105 | 32500  | 0.0173        |
+| 0.2112 | 32600  | 0.0122        |
+| 0.2118 | 32700  | 0.0198        |
+| 0.2125 | 32800  | 0.0044        |
+| 0.2131 | 32900  | 0.0079        |
+| 0.2138 | 33000  | 0.006         |
+| 0.2144 | 33100  | 0.0052        |
+| 0.2151 | 33200  | 0.0268        |
+| 0.2157 | 33300  | 0.0111        |
+| 0.2164 | 33400  | 0.0143        |
+| 0.2170 | 33500  | 0.0171        |
+| 0.2177 | 33600  | 0.0204        |
+| 0.2183 | 33700  | 0.0132        |
+| 0.2189 | 33800  | 0.0081        |
+| 0.2196 | 33900  | 0.0112        |
+| 0.2202 | 34000  | 0.0273        |
+| 0.2209 | 34100  | 0.006         |
+| 0.2215 | 34200  | 0.0054        |
+| 0.2222 | 34300  | 0.0051        |
+| 0.2228 | 34400  | 0.0032        |
+| 0.2235 | 34500  | 0.0128        |
+| 0.2241 | 34600  | 0.0203        |
+| 0.2248 | 34700  | 0.007         |
+| 0.2254 | 34800  | 0.0224        |
+| 0.2261 | 34900  | 0.0366        |
+| 0.2267 | 35000  | 0.0111        |
+| 0.2274 | 35100  | 0.0073        |
+| 0.2280 | 35200  | 0.0183        |
+| 0.2287 | 35300  | 0.0276        |
+| 0.2293 | 35400  | 0.0085        |
+| 0.2300 | 35500  | 0.0185        |
+| 0.2306 | 35600  | 0.0065        |
+| 0.2313 | 35700  | 0.0033        |
+| 0.2319 | 35800  | 0.0324        |
+| 0.2326 | 35900  | 0.0044        |
+| 0.2332 | 36000  | 0.0095        |
+| 0.2338 | 36100  | 0.0146        |
+| 0.2345 | 36200  | 0.0074        |
+| 0.2351 | 36300  | 0.0036        |
+| 0.2358 | 36400  | 0.007         |
+| 0.2364 | 36500  | 0.007         |
+| 0.2371 | 36600  | 0.0027        |
+| 0.2377 | 36700  | 0.0099        |
+| 0.2384 | 36800  | 0.0076        |
+| 0.2390 | 36900  | 0.0129        |
+| 0.2397 | 37000  | 0.0067        |
+| 0.2403 | 37100  | 0.0138        |
+| 0.2410 | 37200  | 0.0412        |
+| 0.2416 | 37300  | 0.044         |
+| 0.2423 | 37400  | 0.0075        |
+| 0.2429 | 37500  | 0.0304        |
+| 0.2436 | 37600  | 0.0157        |
+| 0.2442 | 37700  | 0.0357        |
+| 0.2449 | 37800  | 0.0108        |
+| 0.2455 | 37900  | 0.0051        |
+| 0.2462 | 38000  | 0.0058        |
+| 0.2468 | 38100  | 0.0149        |
+| 0.2475 | 38200  | 0.0157        |
+| 0.2481 | 38300  | 0.0077        |
+| 0.2487 | 38400  | 0.0044        |
+| 0.2494 | 38500  | 0.0099        |
+| 0.2500 | 38600  | 0.0038        |
+| 0.2507 | 38700  | 0.0037        |
+| 0.2513 | 38800  | 0.0028        |
+| 0.2520 | 38900  | 0.0074        |
+| 0.2526 | 39000  | 0.0092        |
+| 0.2533 | 39100  | 0.0279        |
+| 0.2539 | 39200  | 0.0038        |
+| 0.2546 | 39300  | 0.0128        |
+| 0.2552 | 39400  | 0.0059        |
+| 0.2559 | 39500  | 0.0036        |
+| 0.2565 | 39600  | 0.0053        |
+| 0.2572 | 39700  | 0.0111        |
+| 0.2578 | 39800  | 0.0032        |
+| 0.2585 | 39900  | 0.0154        |
+| 0.2591 | 40000  | 0.0052        |
+| 0.2598 | 40100  | 0.0042        |
+| 0.2604 | 40200  | 0.0076        |
+| 0.2611 | 40300  | 0.0091        |
+| 0.2617 | 40400  | 0.0098        |
+| 0.2623 | 40500  | 0.0052        |
+| 0.2630 | 40600  | 0.0098        |
+| 0.2636 | 40700  | 0.0098        |
+| 0.2643 | 40800  | 0.0111        |
+| 0.2649 | 40900  | 0.0111        |
+| 0.2656 | 41000  | 0.0155        |
+| 0.2662 | 41100  | 0.007         |
+| 0.2669 | 41200  | 0.0038        |
+| 0.2675 | 41300  | 0.0041        |
+| 0.2682 | 41400  | 0.0085        |
+| 0.2688 | 41500  | 0.0063        |
+| 0.2695 | 41600  | 0.0065        |
+| 0.2701 | 41700  | 0.0134        |
+| 0.2708 | 41800  | 0.0058        |
+| 0.2714 | 41900  | 0.006         |
+| 0.2721 | 42000  | 0.017         |
+| 0.2727 | 42100  | 0.0157        |
+| 0.2734 | 42200  | 0.0036        |
+| 0.2740 | 42300  | 0.0065        |
+| 0.2747 | 42400  | 0.0188        |
+| 0.2753 | 42500  | 0.0085        |
+| 0.2760 | 42600  | 0.009         |
+| 0.2766 | 42700  | 0.0037        |
+| 0.2772 | 42800  | 0.0531        |
+| 0.2779 | 42900  | 0.0046        |
+| 0.2785 | 43000  | 0.0044        |
+| 0.2792 | 43100  | 0.0036        |
+| 0.2798 | 43200  | 0.0369        |
+| 0.2805 | 43300  | 0.0049        |
+| 0.2811 | 43400  | 0.0068        |
+| 0.2818 | 43500  | 0.0199        |
+| 0.2824 | 43600  | 0.0053        |
+| 0.2831 | 43700  | 0.0036        |
+| 0.2837 | 43800  | 0.0145        |
+| 0.2844 | 43900  | 0.0412        |
+| 0.2850 | 44000  | 0.0215        |
+| 0.2857 | 44100  | 0.0071        |
+| 0.2863 | 44200  | 0.0056        |
+| 0.2870 | 44300  | 0.0151        |
+| 0.2876 | 44400  | 0.0105        |
+| 0.2883 | 44500  | 0.0126        |
+| 0.2889 | 44600  | 0.015         |
+| 0.2896 | 44700  | 0.0082        |
+| 0.2902 | 44800  | 0.011         |
+| 0.2909 | 44900  | 0.0053        |
+| 0.2915 | 45000  | 0.0077        |
+| 0.2921 | 45100  | 0.0039        |
+| 0.2928 | 45200  | 0.0045        |
+| 0.2934 | 45300  | 0.003         |
+| 0.2941 | 45400  | 0.0191        |
+| 0.2947 | 45500  | 0.0226        |
+| 0.2954 | 45600  | 0.0103        |
+| 0.2960 | 45700  | 0.0081        |
+| 0.2967 | 45800  | 0.0114        |
+| 0.2973 | 45900  | 0.0075        |
+| 0.2980 | 46000  | 0.0046        |
+| 0.2986 | 46100  | 0.0088        |
+| 0.2993 | 46200  | 0.0083        |
+| 0.2999 | 46300  | 0.0042        |
+| 0.3006 | 46400  | 0.006         |
+| 0.3012 | 46500  | 0.0034        |
+| 0.3019 | 46600  | 0.0127        |
+| 0.3025 | 46700  | 0.0067        |
+| 0.3032 | 46800  | 0.0048        |
+| 0.3038 | 46900  | 0.0591        |
+| 0.3045 | 47000  | 0.0068        |
+| 0.3051 | 47100  | 0.0309        |
+| 0.3058 | 47200  | 0.0175        |
+| 0.3064 | 47300  | 0.0451        |
+| 0.3070 | 47400  | 0.0031        |
+| 0.3077 | 47500  | 0.0059        |
+| 0.3083 | 47600  | 0.0032        |
+| 0.3090 | 47700  | 0.0031        |
+| 0.3096 | 47800  | 0.0062        |
+| 0.3103 | 47900  | 0.0142        |
+| 0.3109 | 48000  | 0.0103        |
+| 0.3116 | 48100  | 0.006         |
+| 0.3122 | 48200  | 0.005         |
+| 0.3129 | 48300  | 0.0095        |
+| 0.3135 | 48400  | 0.0062        |
+| 0.3142 | 48500  | 0.0112        |
+| 0.3148 | 48600  | 0.0178        |
+| 0.3155 | 48700  | 0.0038        |
+| 0.3161 | 48800  | 0.0023        |
+| 0.3168 | 48900  | 0.0041        |
+| 0.3174 | 49000  | 0.0067        |
+| 0.3181 | 49100  | 0.0285        |
+| 0.3187 | 49200  | 0.0036        |
+| 0.3194 | 49300  | 0.0039        |
+| 0.3200 | 49400  | 0.0035        |
+| 0.3206 | 49500  | 0.014         |
+| 0.3213 | 49600  | 0.0043        |
+| 0.3219 | 49700  | 0.005         |
+| 0.3226 | 49800  | 0.0046        |
+| 0.3232 | 49900  | 0.0082        |
+| 0.3239 | 50000  | 0.0044        |
+| 0.3245 | 50100  | 0.0059        |
+| 0.3252 | 50200  | 0.0221        |
+| 0.3258 | 50300  | 0.0192        |
+| 0.3265 | 50400  | 0.0053        |
+| 0.3271 | 50500  | 0.0193        |
+| 0.3278 | 50600  | 0.0066        |
+| 0.3284 | 50700  | 0.0034        |
+| 0.3291 | 50800  | 0.0125        |
+| 0.3297 | 50900  | 0.0036        |
+| 0.3304 | 51000  | 0.0049        |
+| 0.3310 | 51100  | 0.0034        |
+| 0.3317 | 51200  | 0.0246        |
+| 0.3323 | 51300  | 0.0527        |
+| 0.3330 | 51400  | 0.0068        |
+| 0.3336 | 51500  | 0.004         |
+| 0.3343 | 51600  | 0.0112        |
+| 0.3349 | 51700  | 0.0025        |
+| 0.3355 | 51800  | 0.0119        |
+| 0.3362 | 51900  | 0.0027        |
+| 0.3368 | 52000  | 0.0189        |
+| 0.3375 | 52100  | 0.0282        |
+| 0.3381 | 52200  | 0.0183        |
+| 0.3388 | 52300  | 0.0046        |
+| 0.3394 | 52400  | 0.0091        |
+| 0.3401 | 52500  | 0.0103        |
+| 0.3407 | 52600  | 0.0056        |
+| 0.3414 | 52700  | 0.0035        |
+| 0.3420 | 52800  | 0.0036        |
+| 0.3427 | 52900  | 0.0184        |
+| 0.3433 | 53000  | 0.0076        |
+| 0.3440 | 53100  | 0.0022        |
+| 0.3446 | 53200  | 0.0046        |
+| 0.3453 | 53300  | 0.0182        |
+| 0.3459 | 53400  | 0.0045        |
+| 0.3466 | 53500  | 0.0021        |
+| 0.3472 | 53600  | 0.0128        |
+| 0.3479 | 53700  | 0.0027        |
+| 0.3485 | 53800  | 0.0144        |
+| 0.3492 | 53900  | 0.0027        |
+| 0.3498 | 54000  | 0.0265        |
+| 0.3504 | 54100  | 0.0053        |
+| 0.3511 | 54200  | 0.0041        |
+| 0.3517 | 54300  | 0.0042        |
+| 0.3524 | 54400  | 0.016         |
+| 0.3530 | 54500  | 0.0196        |
+| 0.3537 | 54600  | 0.0024        |
+| 0.3543 | 54700  | 0.0074        |
+| 0.3550 | 54800  | 0.0055        |
+| 0.3556 | 54900  | 0.0431        |
+| 0.3563 | 55000  | 0.0016        |
+| 0.3569 | 55100  | 0.0055        |
+| 0.3576 | 55200  | 0.0042        |
+| 0.3582 | 55300  | 0.0063        |
+| 0.3589 | 55400  | 0.0054        |
+| 0.3595 | 55500  | 0.0101        |
+| 0.3602 | 55600  | 0.0029        |
+| 0.3608 | 55700  | 0.004         |
+| 0.3615 | 55800  | 0.0046        |
+| 0.3621 | 55900  | 0.0035        |
+| 0.3628 | 56000  | 0.005         |
+| 0.3634 | 56100  | 0.0065        |
+| 0.3641 | 56200  | 0.0275        |
+| 0.3647 | 56300  | 0.0127        |
+| 0.3653 | 56400  | 0.0069        |
+| 0.3660 | 56500  | 0.0047        |
+| 0.3666 | 56600  | 0.007         |
+| 0.3673 | 56700  | 0.0042        |
+| 0.3679 | 56800  | 0.0141        |
+| 0.3686 | 56900  | 0.0102        |
+| 0.3692 | 57000  | 0.0062        |
+| 0.3699 | 57100  | 0.0034        |
+| 0.3705 | 57200  | 0.003         |
+| 0.3712 | 57300  | 0.0049        |
+| 0.3718 | 57400  | 0.0223        |
+| 0.3725 | 57500  | 0.0046        |
+| 0.3731 | 57600  | 0.004         |
+| 0.3738 | 57700  | 0.0117        |
+| 0.3744 | 57800  | 0.0029        |
+| 0.3751 | 57900  | 0.0033        |
+| 0.3757 | 58000  | 0.025         |
+| 0.3764 | 58100  | 0.004         |
+| 0.3770 | 58200  | 0.0097        |
+| 0.3777 | 58300  | 0.009         |
+| 0.3783 | 58400  | 0.0077        |
+| 0.3789 | 58500  | 0.0084        |
+| 0.3796 | 58600  | 0.0061        |
+| 0.3802 | 58700  | 0.0049        |
+| 0.3809 | 58800  | 0.0029        |
+| 0.3815 | 58900  | 0.0018        |
+| 0.3822 | 59000  | 0.0039        |
+| 0.3828 | 59100  | 0.0037        |
+| 0.3835 | 59200  | 0.0054        |
+| 0.3841 | 59300  | 0.0152        |
+| 0.3848 | 59400  | 0.0084        |
+| 0.3854 | 59500  | 0.008         |
+| 0.3861 | 59600  | 0.0057        |
+| 0.3867 | 59700  | 0.011         |
+| 0.3874 | 59800  | 0.0035        |
+| 0.3880 | 59900  | 0.0244        |
+| 0.3887 | 60000  | 0.0105        |
+| 0.3893 | 60100  | 0.003         |
+| 0.3900 | 60200  | 0.0037        |
+| 0.3906 | 60300  | 0.0032        |
+| 0.3913 | 60400  | 0.0079        |
+| 0.3919 | 60500  | 0.0027        |
+| 0.3926 | 60600  | 0.0102        |
+| 0.3932 | 60700  | 0.0041        |
+| 0.3938 | 60800  | 0.0058        |
+| 0.3945 | 60900  | 0.0064        |
+| 0.3951 | 61000  | 0.0103        |
+| 0.3958 | 61100  | 0.0054        |
+| 0.3964 | 61200  | 0.0079        |
+| 0.3971 | 61300  | 0.0025        |
+| 0.3977 | 61400  | 0.002         |
+| 0.3984 | 61500  | 0.0043        |
+| 0.3990 | 61600  | 0.0017        |
+| 0.3997 | 61700  | 0.0094        |
+| 0.4003 | 61800  | 0.0102        |
+| 0.4010 | 61900  | 0.0042        |
+| 0.4016 | 62000  | 0.0046        |
+| 0.4023 | 62100  | 0.0056        |
+| 0.4029 | 62200  | 0.0051        |
+| 0.4036 | 62300  | 0.0072        |
+| 0.4042 | 62400  | 0.0048        |
+| 0.4049 | 62500  | 0.0027        |
+| 0.4055 | 62600  | 0.0048        |
+| 0.4062 | 62700  | 0.0143        |
+| 0.4068 | 62800  | 0.0057        |
+| 0.4075 | 62900  | 0.0104        |
+| 0.4081 | 63000  | 0.0026        |
+| 0.4087 | 63100  | 0.0036        |
+| 0.4094 | 63200  | 0.0069        |
+| 0.4100 | 63300  | 0.0028        |
+| 0.4107 | 63400  | 0.0033        |
+| 0.4113 | 63500  | 0.0113        |
+| 0.4120 | 63600  | 0.0051        |
+| 0.4126 | 63700  | 0.0045        |
+| 0.4133 | 63800  | 0.0034        |
+| 0.4139 | 63900  | 0.0188        |
+| 0.4146 | 64000  | 0.0048        |
+| 0.4152 | 64100  | 0.0046        |
+| 0.4159 | 64200  | 0.0053        |
+| 0.4165 | 64300  | 0.0033        |
+| 0.4172 | 64400  | 0.004         |
+| 0.4178 | 64500  | 0.0114        |
+| 0.4185 | 64600  | 0.0037        |
+| 0.4191 | 64700  | 0.0089        |
+| 0.4198 | 64800  | 0.054         |
+| 0.4204 | 64900  | 0.0057        |
+| 0.4211 | 65000  | 0.0031        |
+| 0.4217 | 65100  | 0.0114        |
+| 0.4224 | 65200  | 0.0048        |
+| 0.4230 | 65300  | 0.0022        |
+| 0.4236 | 65400  | 0.0035        |
+| 0.4243 | 65500  | 0.0071        |
+| 0.4249 | 65600  | 0.0023        |
+| 0.4256 | 65700  | 0.0199        |
+| 0.4262 | 65800  | 0.0106        |
+| 0.4269 | 65900  | 0.0035        |
+| 0.4275 | 66000  | 0.0021        |
+| 0.4282 | 66100  | 0.0037        |
+| 0.4288 | 66200  | 0.009         |
+| 0.4295 | 66300  | 0.0153        |
+| 0.4301 | 66400  | 0.0034        |
+| 0.4308 | 66500  | 0.0023        |
+| 0.4314 | 66600  | 0.0032        |
+| 0.4321 | 66700  | 0.0056        |
+| 0.4327 | 66800  | 0.0089        |
+| 0.4334 | 66900  | 0.0067        |
+| 0.4340 | 67000  | 0.0025        |
+| 0.4347 | 67100  | 0.004         |
+| 0.4353 | 67200  | 0.0026        |
+| 0.4360 | 67300  | 0.0038        |
+| 0.4366 | 67400  | 0.0037        |
+| 0.4372 | 67500  | 0.0089        |
+| 0.4379 | 67600  | 0.0055        |
+| 0.4385 | 67700  | 0.0028        |
+| 0.4392 | 67800  | 0.004         |
+| 0.4398 | 67900  | 0.0032        |
+| 0.4405 | 68000  | 0.032         |
+| 0.4411 | 68100  | 0.0032        |
+| 0.4418 | 68200  | 0.0163        |
+| 0.4424 | 68300  | 0.0106        |
+| 0.4431 | 68400  | 0.0556        |
+| 0.4437 | 68500  | 0.0038        |
+| 0.4444 | 68600  | 0.0217        |
+| 0.4450 | 68700  | 0.005         |
+| 0.4457 | 68800  | 0.0021        |
+| 0.4463 | 68900  | 0.0095        |
+| 0.4470 | 69000  | 0.0047        |
+| 0.4476 | 69100  | 0.0031        |
+| 0.4483 | 69200  | 0.0211        |
+| 0.4489 | 69300  | 0.0022        |
+| 0.4496 | 69400  | 0.032         |
+| 0.4502 | 69500  | 0.0124        |
+| 0.4509 | 69600  | 0.0015        |
+| 0.4515 | 69700  | 0.0355        |
+| 0.4521 | 69800  | 0.0026        |
+| 0.4528 | 69900  | 0.0122        |
+| 0.4534 | 70000  | 0.0353        |
+| 0.4541 | 70100  | 0.0132        |
+| 0.4547 | 70200  | 0.0083        |
+| 0.4554 | 70300  | 0.0272        |
+| 0.4560 | 70400  | 0.0104        |
+| 0.4567 | 70500  | 0.0022        |
+| 0.4573 | 70600  | 0.0017        |
+| 0.4580 | 70700  | 0.0045        |
+| 0.4586 | 70800  | 0.0037        |
+| 0.4593 | 70900  | 0.0054        |
+| 0.4599 | 71000  | 0.004         |
+| 0.4606 | 71100  | 0.0087        |
+| 0.4612 | 71200  | 0.0038        |
+| 0.4619 | 71300  | 0.0023        |
+| 0.4625 | 71400  | 0.0013        |
+| 0.4632 | 71500  | 0.0075        |
+| 0.4638 | 71600  | 0.0032        |
+| 0.4645 | 71700  | 0.0024        |
+| 0.4651 | 71800  | 0.0022        |
+| 0.4658 | 71900  | 0.0062        |
+| 0.4664 | 72000  | 0.0062        |
+| 0.4670 | 72100  | 0.0033        |
+| 0.4677 | 72200  | 0.0059        |
+| 0.4683 | 72300  | 0.0034        |
+| 0.4690 | 72400  | 0.0032        |
+| 0.4696 | 72500  | 0.0052        |
+| 0.4703 | 72600  | 0.0072        |
+| 0.4709 | 72700  | 0.0034        |
+| 0.4716 | 72800  | 0.0023        |
+| 0.4722 | 72900  | 0.0026        |
+| 0.4729 | 73000  | 0.0086        |
+| 0.4735 | 73100  | 0.0052        |
+| 0.4742 | 73200  | 0.0034        |
+| 0.4748 | 73300  | 0.0018        |
+| 0.4755 | 73400  | 0.0013        |
+| 0.4761 | 73500  | 0.0031        |
+| 0.4768 | 73600  | 0.0028        |
+| 0.4774 | 73700  | 0.0031        |
+| 0.4781 | 73800  | 0.0462        |
+| 0.4787 | 73900  | 0.0025        |
+| 0.4794 | 74000  | 0.0051        |
+| 0.4800 | 74100  | 0.0139        |
+| 0.4807 | 74200  | 0.0125        |
+| 0.4813 | 74300  | 0.0195        |
+| 0.4819 | 74400  | 0.0026        |
+| 0.4826 | 74500  | 0.0016        |
+| 0.4832 | 74600  | 0.0022        |
+| 0.4839 | 74700  | 0.002         |
+| 0.4845 | 74800  | 0.006         |
+| 0.4852 | 74900  | 0.0028        |
+| 0.4858 | 75000  | 0.0073        |
+| 0.4865 | 75100  | 0.0021        |
+| 0.4871 | 75200  | 0.0027        |
+| 0.4878 | 75300  | 0.0029        |
+| 0.4884 | 75400  | 0.0043        |
+| 0.4891 | 75500  | 0.004         |
+| 0.4897 | 75600  | 0.0055        |
+| 0.4904 | 75700  | 0.0117        |
+| 0.4910 | 75800  | 0.0042        |
+| 0.4917 | 75900  | 0.0021        |
+| 0.4923 | 76000  | 0.054         |
+| 0.4930 | 76100  | 0.0081        |
+| 0.4936 | 76200  | 0.007         |
+| 0.4943 | 76300  | 0.0025        |
+| 0.4949 | 76400  | 0.0039        |
+| 0.4955 | 76500  | 0.0023        |
+| 0.4962 | 76600  | 0.0094        |
+| 0.4968 | 76700  | 0.0109        |
+| 0.4975 | 76800  | 0.008         |
+| 0.4981 | 76900  | 0.0038        |
+| 0.4988 | 77000  | 0.0094        |
+| 0.4994 | 77100  | 0.0086        |
+| 0.5001 | 77200  | 0.0036        |
+| 0.5007 | 77300  | 0.0154        |
+| 0.5014 | 77400  | 0.0013        |
+| 0.5020 | 77500  | 0.0051        |
+| 0.5027 | 77600  | 0.0045        |
+| 0.5033 | 77700  | 0.0224        |
+| 0.5040 | 77800  | 0.0032        |
+| 0.5046 | 77900  | 0.0092        |
+| 0.5053 | 78000  | 0.0049        |
+| 0.5059 | 78100  | 0.0026        |
+| 0.5066 | 78200  | 0.0022        |
+| 0.5072 | 78300  | 0.0028        |
+| 0.5079 | 78400  | 0.0017        |
+| 0.5085 | 78500  | 0.0079        |
+| 0.5092 | 78600  | 0.0078        |
+| 0.5098 | 78700  | 0.0024        |
+| 0.5104 | 78800  | 0.0032        |
+| 0.5111 | 78900  | 0.0028        |
+| 0.5117 | 79000  | 0.0036        |
+| 0.5124 | 79100  | 0.0024        |
+| 0.5130 | 79200  | 0.0062        |
+| 0.5137 | 79300  | 0.0177        |
+| 0.5143 | 79400  | 0.0087        |
+| 0.5150 | 79500  | 0.0029        |
+| 0.5156 | 79600  | 0.0039        |
+| 0.5163 | 79700  | 0.0017        |
+| 0.5169 | 79800  | 0.0159        |
+| 0.5176 | 79900  | 0.0021        |
+| 0.5182 | 80000  | 0.0359        |
+| 0.5189 | 80100  | 0.0021        |
+| 0.5195 | 80200  | 0.0113        |
+| 0.5202 | 80300  | 0.0279        |
+| 0.5208 | 80400  | 0.0046        |
+| 0.5215 | 80500  | 0.0029        |
+| 0.5221 | 80600  | 0.0031        |
+| 0.5228 | 80700  | 0.0013        |
+| 0.5234 | 80800  | 0.0022        |
+| 0.5241 | 80900  | 0.004         |
+| 0.5247 | 81000  | 0.0131        |
+| 0.5253 | 81100  | 0.0035        |
+| 0.5260 | 81200  | 0.0042        |
+| 0.5266 | 81300  | 0.014         |
+| 0.5273 | 81400  | 0.0021        |
+| 0.5279 | 81500  | 0.0022        |
+| 0.5286 | 81600  | 0.0076        |
+| 0.5292 | 81700  | 0.0017        |
+| 0.5299 | 81800  | 0.0041        |
+| 0.5305 | 81900  | 0.0014        |
+| 0.5312 | 82000  | 0.003         |
+| 0.5318 | 82100  | 0.0044        |
+| 0.5325 | 82200  | 0.003         |
+| 0.5331 | 82300  | 0.0099        |
+| 0.5338 | 82400  | 0.0273        |
+| 0.5344 | 82500  | 0.0081        |
+| 0.5351 | 82600  | 0.0018        |
+| 0.5357 | 82700  | 0.0019        |
+| 0.5364 | 82800  | 0.002         |
+| 0.5370 | 82900  | 0.0195        |
+| 0.5377 | 83000  | 0.0031        |
+| 0.5383 | 83100  | 0.0035        |
+| 0.5390 | 83200  | 0.003         |
+| 0.5396 | 83300  | 0.0135        |
+| 0.5402 | 83400  | 0.0037        |
+| 0.5409 | 83500  | 0.0053        |
+| 0.5415 | 83600  | 0.0017        |
+| 0.5422 | 83700  | 0.0022        |
+| 0.5428 | 83800  | 0.0037        |
+| 0.5435 | 83900  | 0.0058        |
+| 0.5441 | 84000  | 0.004         |
+| 0.5448 | 84100  | 0.0026        |
+| 0.5454 | 84200  | 0.0046        |
+| 0.5461 | 84300  | 0.0038        |
+| 0.5467 | 84400  | 0.0025        |
+| 0.5474 | 84500  | 0.0017        |
+| 0.5480 | 84600  | 0.002         |
+| 0.5487 | 84700  | 0.001         |
+| 0.5493 | 84800  | 0.007         |
+| 0.5500 | 84900  | 0.0051        |
+| 0.5506 | 85000  | 0.003         |
+| 0.5513 | 85100  | 0.0022        |
+| 0.5519 | 85200  | 0.0036        |
+| 0.5526 | 85300  | 0.0019        |
+| 0.5532 | 85400  | 0.002         |
+| 0.5538 | 85500  | 0.0284        |
+| 0.5545 | 85600  | 0.0555        |
+| 0.5551 | 85700  | 0.0024        |
+| 0.5558 | 85800  | 0.0023        |
+| 0.5564 | 85900  | 0.0101        |
+| 0.5571 | 86000  | 0.002         |
+| 0.5577 | 86100  | 0.0034        |
+| 0.5584 | 86200  | 0.0033        |
+| 0.5590 | 86300  | 0.0254        |
+| 0.5597 | 86400  | 0.0067        |
+| 0.5603 | 86500  | 0.0071        |
+| 0.5610 | 86600  | 0.0137        |
+| 0.5616 | 86700  | 0.0018        |
+| 0.5623 | 86800  | 0.0028        |
+| 0.5629 | 86900  | 0.0029        |
+| 0.5636 | 87000  | 0.0085        |
+| 0.5642 | 87100  | 0.0021        |
+| 0.5649 | 87200  | 0.0024        |
+| 0.5655 | 87300  | 0.0122        |
+| 0.5662 | 87400  | 0.0054        |
+| 0.5668 | 87500  | 0.0082        |
+| 0.5675 | 87600  | 0.0015        |
+| 0.5681 | 87700  | 0.0025        |
+| 0.5687 | 87800  | 0.011         |
+| 0.5694 | 87900  | 0.0021        |
+| 0.5700 | 88000  | 0.0019        |
+| 0.5707 | 88100  | 0.0165        |
+| 0.5713 | 88200  | 0.0032        |
+| 0.5720 | 88300  | 0.0036        |
+| 0.5726 | 88400  | 0.023         |
+| 0.5733 | 88500  | 0.0016        |
+| 0.5739 | 88600  | 0.0034        |
+| 0.5746 | 88700  | 0.0046        |
+| 0.5752 | 88800  | 0.0312        |
+| 0.5759 | 88900  | 0.0012        |
+| 0.5765 | 89000  | 0.004         |
+| 0.5772 | 89100  | 0.0029        |
+| 0.5778 | 89200  | 0.0042        |
+| 0.5785 | 89300  | 0.0014        |
+| 0.5791 | 89400  | 0.0046        |
+| 0.5798 | 89500  | 0.0041        |
+| 0.5804 | 89600  | 0.0028        |
+| 0.5811 | 89700  | 0.0108        |
+| 0.5817 | 89800  | 0.0043        |
+| 0.5824 | 89900  | 0.0034        |
+| 0.5830 | 90000  | 0.0096        |
+| 0.5836 | 90100  | 0.0022        |
+| 0.5843 | 90200  | 0.0105        |
+| 0.5849 | 90300  | 0.0109        |
+| 0.5856 | 90400  | 0.0056        |
+| 0.5862 | 90500  | 0.0093        |
+| 0.5869 | 90600  | 0.0218        |
+| 0.5875 | 90700  | 0.0026        |
+| 0.5882 | 90800  | 0.0036        |
+| 0.5888 | 90900  | 0.0019        |
+| 0.5895 | 91000  | 0.0027        |
+| 0.5901 | 91100  | 0.0014        |
+| 0.5908 | 91200  | 0.002         |
+| 0.5914 | 91300  | 0.0016        |
+| 0.5921 | 91400  | 0.0037        |
+| 0.5927 | 91500  | 0.0037        |
+| 0.5934 | 91600  | 0.0031        |
+| 0.5940 | 91700  | 0.0017        |
+| 0.5947 | 91800  | 0.0014        |
+| 0.5953 | 91900  | 0.0033        |
+| 0.5960 | 92000  | 0.0024        |
+| 0.5966 | 92100  | 0.004         |
+| 0.5973 | 92200  | 0.0188        |
+| 0.5979 | 92300  | 0.0024        |
+| 0.5985 | 92400  | 0.0049        |
+| 0.5992 | 92500  | 0.0035        |
+| 0.5998 | 92600  | 0.0104        |
+| 0.6005 | 92700  | 0.0053        |
+| 0.6011 | 92800  | 0.0039        |
+| 0.6018 | 92900  | 0.0016        |
+| 0.6024 | 93000  | 0.0043        |
+| 0.6031 | 93100  | 0.0034        |
+| 0.6037 | 93200  | 0.0043        |
+| 0.6044 | 93300  | 0.0033        |
+| 0.6050 | 93400  | 0.0021        |
+| 0.6057 | 93500  | 0.0021        |
+| 0.6063 | 93600  | 0.0018        |
+| 0.6070 | 93700  | 0.0084        |
+| 0.6076 | 93800  | 0.0337        |
+| 0.6083 | 93900  | 0.007         |
+| 0.6089 | 94000  | 0.0036        |
+| 0.6096 | 94100  | 0.017         |
+| 0.6102 | 94200  | 0.0017        |
+| 0.6109 | 94300  | 0.0026        |
+| 0.6115 | 94400  | 0.0044        |
+| 0.6121 | 94500  | 0.0026        |
+| 0.6128 | 94600  | 0.0035        |
+| 0.6134 | 94700  | 0.0022        |
+| 0.6141 | 94800  | 0.0029        |
+| 0.6147 | 94900  | 0.0033        |
+| 0.6154 | 95000  | 0.0061        |
+| 0.6160 | 95100  | 0.0023        |
+| 0.6167 | 95200  | 0.0012        |
+| 0.6173 | 95300  | 0.0025        |
+| 0.6180 | 95400  | 0.0014        |
+| 0.6186 | 95500  | 0.0081        |
+| 0.6193 | 95600  | 0.0049        |
+| 0.6199 | 95700  | 0.0053        |
+| 0.6206 | 95800  | 0.0046        |
+| 0.6212 | 95900  | 0.0204        |
+| 0.6219 | 96000  | 0.0208        |
+| 0.6225 | 96100  | 0.0081        |
+| 0.6232 | 96200  | 0.015         |
+| 0.6238 | 96300  | 0.0075        |
+| 0.6245 | 96400  | 0.0037        |
+| 0.6251 | 96500  | 0.0042        |
+| 0.6258 | 96600  | 0.0018        |
+| 0.6264 | 96700  | 0.005         |
+| 0.6270 | 96800  | 0.0038        |
+| 0.6277 | 96900  | 0.0037        |
+| 0.6283 | 97000  | 0.001         |
+| 0.6290 | 97100  | 0.0049        |
+| 0.6296 | 97200  | 0.004         |
+| 0.6303 | 97300  | 0.0013        |
+| 0.6309 | 97400  | 0.003         |
+| 0.6316 | 97500  | 0.003         |
+| 0.6322 | 97600  | 0.003         |
+| 0.6329 | 97700  | 0.0212        |
+| 0.6335 | 97800  | 0.0038        |
+| 0.6342 | 97900  | 0.0028        |
+| 0.6348 | 98000  | 0.0126        |
+| 0.6355 | 98100  | 0.0074        |
+| 0.6361 | 98200  | 0.0031        |
+| 0.6368 | 98300  | 0.0011        |
+| 0.6374 | 98400  | 0.0066        |
+| 0.6381 | 98500  | 0.0011        |
+| 0.6387 | 98600  | 0.0015        |
+| 0.6394 | 98700  | 0.0071        |
+| 0.6400 | 98800  | 0.0026        |
+| 0.6407 | 98900  | 0.0091        |
+| 0.6413 | 99000  | 0.003         |
+| 0.6419 | 99100  | 0.0144        |
+| 0.6426 | 99200  | 0.0027        |
+| 0.6432 | 99300  | 0.005         |
+| 0.6439 | 99400  | 0.0024        |
+| 0.6445 | 99500  | 0.0102        |
+| 0.6452 | 99600  | 0.0013        |
+| 0.6458 | 99700  | 0.0037        |
+| 0.6465 | 99800  | 0.0031        |
+| 0.6471 | 99900  | 0.004         |
+| 0.6478 | 100000 | 0.0052        |
+| 0.6484 | 100100 | 0.0064        |
+| 0.6491 | 100200 | 0.0044        |
+| 0.6497 | 100300 | 0.0026        |
+| 0.6504 | 100400 | 0.0044        |
+| 0.6510 | 100500 | 0.0032        |
+| 0.6517 | 100600 | 0.0124        |
+| 0.6523 | 100700 | 0.0215        |
+| 0.6530 | 100800 | 0.0035        |
+| 0.6536 | 100900 | 0.0056        |
+| 0.6543 | 101000 | 0.0043        |
+| 0.6549 | 101100 | 0.0076        |
+| 0.6556 | 101200 | 0.0013        |
+| 0.6562 | 101300 | 0.0366        |
+| 0.6568 | 101400 | 0.0018        |
+| 0.6575 | 101500 | 0.0051        |
+| 0.6581 | 101600 | 0.0016        |
+| 0.6588 | 101700 | 0.0018        |
+| 0.6594 | 101800 | 0.0016        |
+| 0.6601 | 101900 | 0.006         |
+| 0.6607 | 102000 | 0.0035        |
+| 0.6614 | 102100 | 0.0023        |
+| 0.6620 | 102200 | 0.0031        |
+| 0.6627 | 102300 | 0.0029        |
+| 0.6633 | 102400 | 0.0019        |
+| 0.6640 | 102500 | 0.0012        |
+| 0.6646 | 102600 | 0.0016        |
+| 0.6653 | 102700 | 0.0166        |
+| 0.6659 | 102800 | 0.0022        |
+| 0.6666 | 102900 | 0.0023        |
+| 0.6672 | 103000 | 0.0039        |
+| 0.6679 | 103100 | 0.0057        |
+| 0.6685 | 103200 | 0.005         |
+| 0.6692 | 103300 | 0.0035        |
+| 0.6698 | 103400 | 0.0024        |
+| 0.6704 | 103500 | 0.0017        |
+| 0.6711 | 103600 | 0.0036        |
+| 0.6717 | 103700 | 0.0154        |
+| 0.6724 | 103800 | 0.0043        |
+| 0.6730 | 103900 | 0.02          |
+| 0.6737 | 104000 | 0.0042        |
+| 0.6743 | 104100 | 0.0023        |
+| 0.6750 | 104200 | 0.0035        |
+| 0.6756 | 104300 | 0.0035        |
+| 0.6763 | 104400 | 0.0016        |
+| 0.6769 | 104500 | 0.0016        |
+| 0.6776 | 104600 | 0.0018        |
+| 0.6782 | 104700 | 0.0045        |
+| 0.6789 | 104800 | 0.0022        |
+| 0.6795 | 104900 | 0.002         |
+| 0.6802 | 105000 | 0.0054        |
+| 0.6808 | 105100 | 0.005         |
+| 0.6815 | 105200 | 0.0076        |
+| 0.6821 | 105300 | 0.0014        |
+| 0.6828 | 105400 | 0.0013        |
+| 0.6834 | 105500 | 0.0015        |
+| 0.6841 | 105600 | 0.002         |
+| 0.6847 | 105700 | 0.0019        |
+| 0.6853 | 105800 | 0.022         |
+| 0.6860 | 105900 | 0.0016        |
+| 0.6866 | 106000 | 0.0108        |
+| 0.6873 | 106100 | 0.0139        |
+| 0.6879 | 106200 | 0.0017        |
+| 0.6886 | 106300 | 0.0013        |
+| 0.6892 | 106400 | 0.0036        |
+| 0.6899 | 106500 | 0.0055        |
+| 0.6905 | 106600 | 0.0049        |
+| 0.6912 | 106700 | 0.0018        |
+| 0.6918 | 106800 | 0.008         |
+| 0.6925 | 106900 | 0.002         |
+| 0.6931 | 107000 | 0.002         |
+| 0.6938 | 107100 | 0.0018        |
+| 0.6944 | 107200 | 0.003         |
+| 0.6951 | 107300 | 0.0017        |
+| 0.6957 | 107400 | 0.0014        |
+| 0.6964 | 107500 | 0.0017        |
+| 0.6970 | 107600 | 0.0014        |
+| 0.6977 | 107700 | 0.0066        |
+| 0.6983 | 107800 | 0.0017        |
+| 0.6990 | 107900 | 0.0077        |
+| 0.6996 | 108000 | 0.0024        |
+| 0.7002 | 108100 | 0.0025        |
+| 0.7009 | 108200 | 0.0031        |
+| 0.7015 | 108300 | 0.0012        |
+| 0.7022 | 108400 | 0.0048        |
+| 0.7028 | 108500 | 0.0086        |
+| 0.7035 | 108600 | 0.0087        |
+| 0.7041 | 108700 | 0.0016        |
+| 0.7048 | 108800 | 0.0019        |
+| 0.7054 | 108900 | 0.0019        |
+| 0.7061 | 109000 | 0.0021        |
+| 0.7067 | 109100 | 0.0014        |
+| 0.7074 | 109200 | 0.0033        |
+| 0.7080 | 109300 | 0.003         |
+| 0.7087 | 109400 | 0.0028        |
+| 0.7093 | 109500 | 0.0183        |
+| 0.7100 | 109600 | 0.0025        |
+| 0.7106 | 109700 | 0.0027        |
+| 0.7113 | 109800 | 0.0012        |
+| 0.7119 | 109900 | 0.005         |
+| 0.7126 | 110000 | 0.0041        |
+| 0.7132 | 110100 | 0.0024        |
+| 0.7139 | 110200 | 0.0033        |
+| 0.7145 | 110300 | 0.0027        |
+| 0.7151 | 110400 | 0.0024        |
+| 0.7158 | 110500 | 0.0015        |
+| 0.7164 | 110600 | 0.0047        |
+| 0.7171 | 110700 | 0.0775        |
+| 0.7177 | 110800 | 0.0041        |
+| 0.7184 | 110900 | 0.0024        |
+| 0.7190 | 111000 | 0.0031        |
+| 0.7197 | 111100 | 0.0057        |
+| 0.7203 | 111200 | 0.0033        |
+| 0.7210 | 111300 | 0.0035        |
+| 0.7216 | 111400 | 0.0016        |
+| 0.7223 | 111500 | 0.0022        |
+| 0.7229 | 111600 | 0.0026        |
+| 0.7236 | 111700 | 0.0198        |
+| 0.7242 | 111800 | 0.0027        |
+| 0.7249 | 111900 | 0.0043        |
+| 0.7255 | 112000 | 0.0097        |
+| 0.7262 | 112100 | 0.0039        |
+| 0.7268 | 112200 | 0.0079        |
+| 0.7275 | 112300 | 0.0024        |
+| 0.7281 | 112400 | 0.0028        |
+| 0.7287 | 112500 | 0.0018        |
+| 0.7294 | 112600 | 0.0015        |
+| 0.7300 | 112700 | 0.0023        |
+| 0.7307 | 112800 | 0.0008        |
+| 0.7313 | 112900 | 0.0021        |
+| 0.7320 | 113000 | 0.0017        |
+| 0.7326 | 113100 | 0.0012        |
+| 0.7333 | 113200 | 0.0011        |
+| 0.7339 | 113300 | 0.0014        |
+| 0.7346 | 113400 | 0.0013        |
+| 0.7352 | 113500 | 0.0023        |
+| 0.7359 | 113600 | 0.0024        |
+| 0.7365 | 113700 | 0.002         |
+| 0.7372 | 113800 | 0.0013        |
+| 0.7378 | 113900 | 0.0019        |
+| 0.7385 | 114000 | 0.0014        |
+| 0.7391 | 114100 | 0.0012        |
+| 0.7398 | 114200 | 0.0017        |
+| 0.7404 | 114300 | 0.0016        |
+| 0.7411 | 114400 | 0.0023        |
+| 0.7417 | 114500 | 0.0019        |
+| 0.7424 | 114600 | 0.0073        |
+| 0.7430 | 114700 | 0.002         |
+| 0.7436 | 114800 | 0.0011        |
+| 0.7443 | 114900 | 0.017         |
+| 0.7449 | 115000 | 0.0032        |
+| 0.7456 | 115100 | 0.0014        |
+| 0.7462 | 115200 | 0.006         |
+| 0.7469 | 115300 | 0.0012        |
+| 0.7475 | 115400 | 0.0039        |
+| 0.7482 | 115500 | 0.0034        |
+| 0.7488 | 115600 | 0.0015        |
+| 0.7495 | 115700 | 0.0026        |
+| 0.7501 | 115800 | 0.0017        |
+| 0.7508 | 115900 | 0.007         |
+| 0.7514 | 116000 | 0.0049        |
+| 0.7521 | 116100 | 0.0024        |
+| 0.7527 | 116200 | 0.0029        |
+| 0.7534 | 116300 | 0.0048        |
+| 0.7540 | 116400 | 0.001         |
+| 0.7547 | 116500 | 0.0034        |
+| 0.7553 | 116600 | 0.0019        |
+| 0.7560 | 116700 | 0.0015        |
+| 0.7566 | 116800 | 0.0034        |
+| 0.7573 | 116900 | 0.0011        |
+| 0.7579 | 117000 | 0.0013        |
+| 0.7585 | 117100 | 0.0026        |
+| 0.7592 | 117200 | 0.002         |
+| 0.7598 | 117300 | 0.0022        |
+| 0.7605 | 117400 | 0.002         |
+| 0.7611 | 117500 | 0.0023        |
+| 0.7618 | 117600 | 0.0028        |
+| 0.7624 | 117700 | 0.0106        |
+| 0.7631 | 117800 | 0.0013        |
+| 0.7637 | 117900 | 0.0027        |
+| 0.7644 | 118000 | 0.0149        |
+| 0.7650 | 118100 | 0.0081        |
+| 0.7657 | 118200 | 0.0011        |
+| 0.7663 | 118300 | 0.0027        |
+| 0.7670 | 118400 | 0.0011        |
+| 0.7676 | 118500 | 0.0018        |
+| 0.7683 | 118600 | 0.0076        |
+| 0.7689 | 118700 | 0.0036        |
+| 0.7696 | 118800 | 0.0052        |
+| 0.7702 | 118900 | 0.0056        |
+| 0.7709 | 119000 | 0.0019        |
+| 0.7715 | 119100 | 0.023         |
+| 0.7722 | 119200 | 0.0022        |
+| 0.7728 | 119300 | 0.0014        |
+| 0.7734 | 119400 | 0.0012        |
+| 0.7741 | 119500 | 0.001         |
+| 0.7747 | 119600 | 0.0018        |
+| 0.7754 | 119700 | 0.0045        |
+| 0.7760 | 119800 | 0.0026        |
+| 0.7767 | 119900 | 0.0011        |
+| 0.7773 | 120000 | 0.0028        |
+| 0.7780 | 120100 | 0.0019        |
+| 0.7786 | 120200 | 0.0201        |
+| 0.7793 | 120300 | 0.0012        |
+| 0.7799 | 120400 | 0.0027        |
+| 0.7806 | 120500 | 0.0021        |
+| 0.7812 | 120600 | 0.0025        |
+| 0.7819 | 120700 | 0.0013        |
+| 0.7825 | 120800 | 0.0039        |
+| 0.7832 | 120900 | 0.0019        |
+| 0.7838 | 121000 | 0.0121        |
+| 0.7845 | 121100 | 0.0013        |
+| 0.7851 | 121200 | 0.0012        |
+| 0.7858 | 121300 | 0.002         |
+| 0.7864 | 121400 | 0.0052        |
+| 0.7870 | 121500 | 0.002         |
+| 0.7877 | 121600 | 0.0012        |
+| 0.7883 | 121700 | 0.0013        |
+| 0.7890 | 121800 | 0.0029        |
+| 0.7896 | 121900 | 0.001         |
+| 0.7903 | 122000 | 0.0145        |
+| 0.7909 | 122100 | 0.0038        |
+| 0.7916 | 122200 | 0.0009        |
+| 0.7922 | 122300 | 0.0027        |
+| 0.7929 | 122400 | 0.0021        |
+| 0.7935 | 122500 | 0.0009        |
+| 0.7942 | 122600 | 0.0017        |
+| 0.7948 | 122700 | 0.001         |
+| 0.7955 | 122800 | 0.0021        |
+| 0.7961 | 122900 | 0.0176        |
+| 0.7968 | 123000 | 0.0014        |
+| 0.7974 | 123100 | 0.0025        |
+| 0.7981 | 123200 | 0.0015        |
+| 0.7987 | 123300 | 0.0055        |
+| 0.7994 | 123400 | 0.0024        |
+| 0.8000 | 123500 | 0.0125        |
+| 0.8007 | 123600 | 0.0052        |
+| 0.8013 | 123700 | 0.0025        |
+| 0.8019 | 123800 | 0.003         |
+| 0.8026 | 123900 | 0.0082        |
+| 0.8032 | 124000 | 0.0014        |
+| 0.8039 | 124100 | 0.0014        |
+| 0.8045 | 124200 | 0.0464        |
+| 0.8052 | 124300 | 0.0113        |
+| 0.8058 | 124400 | 0.0035        |
+| 0.8065 | 124500 | 0.0019        |
+| 0.8071 | 124600 | 0.0016        |
+| 0.8078 | 124700 | 0.0026        |
+| 0.8084 | 124800 | 0.0012        |
+| 0.8091 | 124900 | 0.0021        |
+| 0.8097 | 125000 | 0.0024        |
+| 0.8104 | 125100 | 0.0032        |
+| 0.8110 | 125200 | 0.0153        |
+| 0.8117 | 125300 | 0.0028        |
+| 0.8123 | 125400 | 0.0017        |
+| 0.8130 | 125500 | 0.0036        |
+| 0.8136 | 125600 | 0.0023        |
+| 0.8143 | 125700 | 0.0029        |
+| 0.8149 | 125800 | 0.0014        |
+| 0.8156 | 125900 | 0.002         |
+| 0.8162 | 126000 | 0.004         |
+| 0.8168 | 126100 | 0.0156        |
+| 0.8175 | 126200 | 0.0012        |
+| 0.8181 | 126300 | 0.0051        |
+| 0.8188 | 126400 | 0.0027        |
+| 0.8194 | 126500 | 0.0056        |
+| 0.8201 | 126600 | 0.0011        |
+| 0.8207 | 126700 | 0.0036        |
+| 0.8214 | 126800 | 0.0014        |
+| 0.8220 | 126900 | 0.0017        |
+| 0.8227 | 127000 | 0.0045        |
+| 0.8233 | 127100 | 0.0092        |
+| 0.8240 | 127200 | 0.0053        |
+| 0.8246 | 127300 | 0.0023        |
+| 0.8253 | 127400 | 0.0053        |
+| 0.8259 | 127500 | 0.0022        |
+| 0.8266 | 127600 | 0.0012        |
+| 0.8272 | 127700 | 0.0028        |
+| 0.8279 | 127800 | 0.0022        |
+| 0.8285 | 127900 | 0.0011        |
+| 0.8292 | 128000 | 0.0074        |
+| 0.8298 | 128100 | 0.0021        |
+| 0.8305 | 128200 | 0.0009        |
+| 0.8311 | 128300 | 0.0029        |
+| 0.8317 | 128400 | 0.0011        |
+| 0.8324 | 128500 | 0.0014        |
+| 0.8330 | 128600 | 0.0015        |
+| 0.8337 | 128700 | 0.0011        |
+| 0.8343 | 128800 | 0.0022        |
+| 0.8350 | 128900 | 0.004         |
+| 0.8356 | 129000 | 0.0026        |
+| 0.8363 | 129100 | 0.0045        |
+| 0.8369 | 129200 | 0.0037        |
+| 0.8376 | 129300 | 0.0015        |
+| 0.8382 | 129400 | 0.0018        |
+| 0.8389 | 129500 | 0.0027        |
+| 0.8395 | 129600 | 0.0011        |
+| 0.8402 | 129700 | 0.0098        |
+| 0.8408 | 129800 | 0.0061        |
+| 0.8415 | 129900 | 0.0124        |
+| 0.8421 | 130000 | 0.0022        |
+| 0.8428 | 130100 | 0.0013        |
+| 0.8434 | 130200 | 0.0008        |
+| 0.8441 | 130300 | 0.0132        |
+| 0.8447 | 130400 | 0.0015        |
+| 0.8453 | 130500 | 0.0084        |
+| 0.8460 | 130600 | 0.0016        |
+| 0.8466 | 130700 | 0.0088        |
+| 0.8473 | 130800 | 0.0109        |
+| 0.8479 | 130900 | 0.0026        |
+| 0.8486 | 131000 | 0.0022        |
+| 0.8492 | 131100 | 0.0017        |
+| 0.8499 | 131200 | 0.0038        |
+| 0.8505 | 131300 | 0.0029        |
+| 0.8512 | 131400 | 0.0016        |
+| 0.8518 | 131500 | 0.0026        |
+| 0.8525 | 131600 | 0.0019        |
+| 0.8531 | 131700 | 0.0016        |
+| 0.8538 | 131800 | 0.0015        |
+| 0.8544 | 131900 | 0.0015        |
+| 0.8551 | 132000 | 0.0025        |
+| 0.8557 | 132100 | 0.0248        |
+| 0.8564 | 132200 | 0.0012        |
+| 0.8570 | 132300 | 0.0022        |
+| 0.8577 | 132400 | 0.0098        |
+| 0.8583 | 132500 | 0.0009        |
+| 0.8590 | 132600 | 0.0023        |
+| 0.8596 | 132700 | 0.0117        |
+| 0.8602 | 132800 | 0.0028        |
+| 0.8609 | 132900 | 0.0011        |
+| 0.8615 | 133000 | 0.0028        |
+| 0.8622 | 133100 | 0.0012        |
+| 0.8628 | 133200 | 0.0029        |
+| 0.8635 | 133300 | 0.0015        |
+| 0.8641 | 133400 | 0.0106        |
+| 0.8648 | 133500 | 0.0014        |
+| 0.8654 | 133600 | 0.0025        |
+| 0.8661 | 133700 | 0.0036        |
+| 0.8667 | 133800 | 0.0012        |
+| 0.8674 | 133900 | 0.0031        |
+| 0.8680 | 134000 | 0.0031        |
+| 0.8687 | 134100 | 0.0032        |
+| 0.8693 | 134200 | 0.0013        |
+| 0.8700 | 134300 | 0.0013        |
+| 0.8706 | 134400 | 0.0011        |
+| 0.8713 | 134500 | 0.0039        |
+| 0.8719 | 134600 | 0.0014        |
+| 0.8726 | 134700 | 0.0013        |
+| 0.8732 | 134800 | 0.001         |
+| 0.8739 | 134900 | 0.005         |
+| 0.8745 | 135000 | 0.0021        |
+| 0.8751 | 135100 | 0.0024        |
+| 0.8758 | 135200 | 0.0015        |
+| 0.8764 | 135300 | 0.0018        |
+| 0.8771 | 135400 | 0.0033        |
+| 0.8777 | 135500 | 0.0016        |
+| 0.8784 | 135600 | 0.0016        |
+| 0.8790 | 135700 | 0.004         |
+| 0.8797 | 135800 | 0.0011        |
+| 0.8803 | 135900 | 0.0022        |
+| 0.8810 | 136000 | 0.0009        |
+| 0.8816 | 136100 | 0.016         |
+| 0.8823 | 136200 | 0.0019        |
+| 0.8829 | 136300 | 0.0014        |
+| 0.8836 | 136400 | 0.0015        |
+| 0.8842 | 136500 | 0.0022        |
+| 0.8849 | 136600 | 0.0013        |
+| 0.8855 | 136700 | 0.0027        |
+| 0.8862 | 136800 | 0.0034        |
+| 0.8868 | 136900 | 0.0011        |
+| 0.8875 | 137000 | 0.002         |
+| 0.8881 | 137100 | 0.0032        |
+| 0.8888 | 137200 | 0.0054        |
+| 0.8894 | 137300 | 0.0023        |
+| 0.8900 | 137400 | 0.0009        |
+| 0.8907 | 137500 | 0.0022        |
+| 0.8913 | 137600 | 0.0009        |
+| 0.8920 | 137700 | 0.0019        |
+| 0.8926 | 137800 | 0.002         |
+| 0.8933 | 137900 | 0.0065        |
+| 0.8939 | 138000 | 0.0013        |
+| 0.8946 | 138100 | 0.0017        |
+| 0.8952 | 138200 | 0.0063        |
+| 0.8959 | 138300 | 0.0174        |
+| 0.8965 | 138400 | 0.0113        |
+| 0.8972 | 138500 | 0.0012        |
+| 0.8978 | 138600 | 0.0016        |
+| 0.8985 | 138700 | 0.01          |
+| 0.8991 | 138800 | 0.0043        |
+| 0.8998 | 138900 | 0.0017        |
+| 0.9004 | 139000 | 0.0018        |
+| 0.9011 | 139100 | 0.0011        |
+| 0.9017 | 139200 | 0.0014        |
+| 0.9024 | 139300 | 0.0021        |
+| 0.9030 | 139400 | 0.0008        |
+| 0.9036 | 139500 | 0.0013        |
+| 0.9043 | 139600 | 0.0009        |
+| 0.9049 | 139700 | 0.0056        |
+| 0.9056 | 139800 | 0.0014        |
+| 0.9062 | 139900 | 0.0065        |
+| 0.9069 | 140000 | 0.001         |
+| 0.9075 | 140100 | 0.0026        |
+| 0.9082 | 140200 | 0.0016        |
+| 0.9088 | 140300 | 0.0093        |
+| 0.9095 | 140400 | 0.0029        |
+| 0.9101 | 140500 | 0.0018        |
+| 0.9108 | 140600 | 0.0037        |
+| 0.9114 | 140700 | 0.0094        |
+| 0.9121 | 140800 | 0.0016        |
+| 0.9127 | 140900 | 0.0027        |
+| 0.9134 | 141000 | 0.0015        |
+| 0.9140 | 141100 | 0.0018        |
+| 0.9147 | 141200 | 0.001         |
+| 0.9153 | 141300 | 0.0021        |
+| 0.9160 | 141400 | 0.0014        |
+| 0.9166 | 141500 | 0.0046        |
+| 0.9173 | 141600 | 0.0035        |
+| 0.9179 | 141700 | 0.0011        |
+| 0.9185 | 141800 | 0.0011        |
+| 0.9192 | 141900 | 0.001         |
+| 0.9198 | 142000 | 0.005         |
+| 0.9205 | 142100 | 0.0013        |
+| 0.9211 | 142200 | 0.0022        |
+| 0.9218 | 142300 | 0.0012        |
+| 0.9224 | 142400 | 0.0029        |
+| 0.9231 | 142500 | 0.0009        |
+| 0.9237 | 142600 | 0.003         |
+| 0.9244 | 142700 | 0.0021        |
+| 0.9250 | 142800 | 0.0008        |
+| 0.9257 | 142900 | 0.0051        |
+| 0.9263 | 143000 | 0.0015        |
+| 0.9270 | 143100 | 0.0016        |
+| 0.9276 | 143200 | 0.0015        |
+| 0.9283 | 143300 | 0.0018        |
+| 0.9289 | 143400 | 0.0235        |
+| 0.9296 | 143500 | 0.0012        |
+| 0.9302 | 143600 | 0.0018        |
+| 0.9309 | 143700 | 0.0016        |
+| 0.9315 | 143800 | 0.0013        |
+| 0.9322 | 143900 | 0.0036        |
+| 0.9328 | 144000 | 0.0009        |
+| 0.9334 | 144100 | 0.0013        |
+| 0.9341 | 144200 | 0.0021        |
+| 0.9347 | 144300 | 0.0013        |
+| 0.9354 | 144400 | 0.0007        |
+| 0.9360 | 144500 | 0.0055        |
+| 0.9367 | 144600 | 0.0063        |
+| 0.9373 | 144700 | 0.0028        |
+| 0.9380 | 144800 | 0.001         |
+| 0.9386 | 144900 | 0.0011        |
+| 0.9393 | 145000 | 0.0036        |
+| 0.9399 | 145100 | 0.001         |
+| 0.9406 | 145200 | 0.002         |
+| 0.9412 | 145300 | 0.0016        |
+| 0.9419 | 145400 | 0.0024        |
+| 0.9425 | 145500 | 0.0016        |
+| 0.9432 | 145600 | 0.0018        |
+| 0.9438 | 145700 | 0.0018        |
+| 0.9445 | 145800 | 0.0028        |
+| 0.9451 | 145900 | 0.0015        |
+| 0.9458 | 146000 | 0.0044        |
+| 0.9464 | 146100 | 0.0017        |
+| 0.9471 | 146200 | 0.0007        |
+| 0.9477 | 146300 | 0.001         |
+| 0.9483 | 146400 | 0.0033        |
+| 0.9490 | 146500 | 0.0017        |
+| 0.9496 | 146600 | 0.0016        |
+| 0.9503 | 146700 | 0.0019        |
+| 0.9509 | 146800 | 0.0361        |
+| 0.9516 | 146900 | 0.0031        |
+| 0.9522 | 147000 | 0.0061        |
+| 0.9529 | 147100 | 0.0013        |
+| 0.9535 | 147200 | 0.0018        |
+| 0.9542 | 147300 | 0.0022        |
+| 0.9548 | 147400 | 0.0034        |
+| 0.9555 | 147500 | 0.0026        |
+| 0.9561 | 147600 | 0.0019        |
+| 0.9568 | 147700 | 0.001         |
+| 0.9574 | 147800 | 0.0063        |
+| 0.9581 | 147900 | 0.0027        |
+| 0.9587 | 148000 | 0.0021        |
+| 0.9594 | 148100 | 0.0027        |
+| 0.9600 | 148200 | 0.0014        |
+| 0.9607 | 148300 | 0.0017        |
+| 0.9613 | 148400 | 0.0044        |
+| 0.9619 | 148500 | 0.0017        |
+| 0.9626 | 148600 | 0.0029        |
+| 0.9632 | 148700 | 0.002         |
+| 0.9639 | 148800 | 0.001         |
+| 0.9645 | 148900 | 0.0012        |
+| 0.9652 | 149000 | 0.0024        |
+| 0.9658 | 149100 | 0.0022        |
+| 0.9665 | 149200 | 0.0027        |
+| 0.9671 | 149300 | 0.0012        |
+| 0.9678 | 149400 | 0.0055        |
+| 0.9684 | 149500 | 0.001         |
+| 0.9691 | 149600 | 0.0026        |
+| 0.9697 | 149700 | 0.001         |
+| 0.9704 | 149800 | 0.0011        |
+| 0.9710 | 149900 | 0.0036        |
+| 0.9717 | 150000 | 0.0023        |
+| 0.9723 | 150100 | 0.002         |
+| 0.9730 | 150200 | 0.0012        |
+| 0.9736 | 150300 | 0.0017        |
+| 0.9743 | 150400 | 0.001         |
+| 0.9749 | 150500 | 0.0015        |
+| 0.9756 | 150600 | 0.0036        |
+| 0.9762 | 150700 | 0.0022        |
+| 0.9768 | 150800 | 0.0009        |
+| 0.9775 | 150900 | 0.0225        |
+| 0.9781 | 151000 | 0.0026        |
+| 0.9788 | 151100 | 0.001         |
+| 0.9794 | 151200 | 0.0009        |
+| 0.9801 | 151300 | 0.0023        |
+| 0.9807 | 151400 | 0.0011        |
+| 0.9814 | 151500 | 0.0028        |
+| 0.9820 | 151600 | 0.0088        |
+| 0.9827 | 151700 | 0.0018        |
+| 0.9833 | 151800 | 0.0028        |
+| 0.9840 | 151900 | 0.0011        |
+| 0.9846 | 152000 | 0.0036        |
+| 0.9853 | 152100 | 0.0016        |
+| 0.9859 | 152200 | 0.0015        |
+| 0.9866 | 152300 | 0.0107        |
+| 0.9872 | 152400 | 0.0038        |
+| 0.9879 | 152500 | 0.0017        |
+| 0.9885 | 152600 | 0.0015        |
+| 0.9892 | 152700 | 0.0023        |
+| 0.9898 | 152800 | 0.002         |
+| 0.9905 | 152900 | 0.0018        |
+| 0.9911 | 153000 | 0.001         |
+| 0.9917 | 153100 | 0.0015        |
+| 0.9924 | 153200 | 0.0045        |
+| 0.9930 | 153300 | 0.009         |
+| 0.9937 | 153400 | 0.0008        |
+| 0.9943 | 153500 | 0.0016        |
+| 0.9950 | 153600 | 0.0007        |
+| 0.9956 | 153700 | 0.0014        |
+| 0.9963 | 153800 | 0.005         |
+| 0.9969 | 153900 | 0.0018        |
+| 0.9976 | 154000 | 0.0097        |
+| 0.9982 | 154100 | 0.001         |
+| 0.9989 | 154200 | 0.0016        |
+| 0.9995 | 154300 | 0.0032        |
+
+</details>
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.1.0
+- Transformers: 4.55.4
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.10.1
+- Datasets: 4.0.0
+- Tokenizers: 0.21.4
+
+## 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",
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@online{kexuefm-8847,
+    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
+    author={Su Jianlin},
+    year={2022},
+    month={Jan},
+    url={https://kexue.fm/archives/8847},
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.55.4",
+    "pytorch": "2.6.0+cu124"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d0cf0bf7533c59a90f74220867655dc6dd8aaae6940e89763b3e07c1fb7eaf6b
+size 90864192

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 256,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}