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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:9164180
+- loss:CoSENTLoss
+base_model: Remonatef/pairs_with_scores_sampled_category_v25.1
+widget:
+- source_sentence: don canino dog training pads
+  sentences:
+  - bear crochete toy
+  - mexican salsa shrimps
+  - egg container
+- source_sentence: side sweatpants
+  sentences:
+  - light sweatshirt
+  - uv protection puzzle
+  - pottery bowl
+- source_sentence: handblended coffee
+  sentences:
+  - leggings
+  - immune system medicine
+  - gold coffee capsules
+- source_sentence: fully cooked mahshi
+  sentences:
+  - open style coverup
+  - alcoholfree scent for all ages pets
+  - first aid medicine
+- source_sentence: pasabah
+  sentences:
+  - thermos mug
+  - sports tracksuit
+  - buttoned up bedding
+datasets:
+- KhaledReda/pairs_three_scores_v8
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# all-MiniLM-L6-v8-pair_score
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Remonatef/pairs_with_scores_sampled_category_v25.1](https://huggingface.co/Remonatef/pairs_with_scores_sampled_category_v25.1) on the [pairs_three_scores_v8](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v8) 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:** [Remonatef/pairs_with_scores_sampled_category_v25.1](https://huggingface.co/Remonatef/pairs_with_scores_sampled_category_v25.1) <!-- at revision 361606cd00e01756133edc168a8c917901bb70a4 -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [pairs_three_scores_v8](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v8)
+- **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 = [
+    'pasabah',
+    'thermos mug',
+    'sports tracksuit',
+]
+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, 0.8039, 0.5806],
+#         [0.8039, 1.0000, 0.5902],
+#         [0.5806, 0.5902, 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_three_scores_v8
+
+* Dataset: [pairs_three_scores_v8](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v8) at [7e8a1e6](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v8/tree/7e8a1e615f346087cf002e27418f5e6756d266cf)
+* Size: 9,164,180 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: 5.6 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.79 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 0.15</li><li>mean: 0.43</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                          | sentence2                               | score             |
+  |:-----------------------------------|:----------------------------------------|:------------------|
+  | <code>booster face cleanser</code> | <code>pizza cutter</code>               | <code>0.24</code> |
+  | <code>line sinker</code>           | <code>accessories</code>                | <code>1.0</code>  |
+  | <code>tricovel</code>              | <code>cot bed mattress protector</code> | <code>0.28</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_three_scores_v8
+
+* Dataset: [pairs_three_scores_v8](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v8) at [7e8a1e6](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v8/tree/7e8a1e615f346087cf002e27418f5e6756d266cf)
+* Size: 46,052 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: 5.66 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.6 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 0.15</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                | sentence2                        | score             |
+  |:-------------------------|:---------------------------------|:------------------|
+  | <code>printed set</code> | <code>crushed outfit</code>      | <code>1.0</code>  |
+  | <code>value</code>       | <code>eva cosmetics serum</code> | <code>0.23</code> |
+  | <code>zino shakes</code> | <code>candy</code>               | <code>0.27</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`: 2
+- `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`: 2
+- `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 |
+|:------:|:------:|:-------------:|
+| 0.0014 | 100    | 8.8538        |
+| 0.0028 | 200    | 8.8627        |
+| 0.0042 | 300    | 8.6961        |
+| 0.0056 | 400    | 8.5595        |
+| 0.0070 | 500    | 8.5157        |
+| 0.0084 | 600    | 8.4187        |
+| 0.0098 | 700    | 8.3768        |
+| 0.0112 | 800    | 8.3611        |
+| 0.0126 | 900    | 8.2891        |
+| 0.0140 | 1000   | 8.2687        |
+| 0.0154 | 1100   | 8.2398        |
+| 0.0168 | 1200   | 8.2112        |
+| 0.0182 | 1300   | 8.1916        |
+| 0.0196 | 1400   | 8.1942        |
+| 0.0210 | 1500   | 8.172         |
+| 0.0223 | 1600   | 8.1808        |
+| 0.0237 | 1700   | 8.153         |
+| 0.0251 | 1800   | 8.1209        |
+| 0.0265 | 1900   | 8.1309        |
+| 0.0279 | 2000   | 8.1563        |
+| 0.0293 | 2100   | 8.1438        |
+| 0.0307 | 2200   | 8.1089        |
+| 0.0321 | 2300   | 8.1216        |
+| 0.0335 | 2400   | 8.1216        |
+| 0.0349 | 2500   | 8.0719        |
+| 0.0363 | 2600   | 8.1142        |
+| 0.0377 | 2700   | 8.0906        |
+| 0.0391 | 2800   | 8.062         |
+| 0.0405 | 2900   | 8.0963        |
+| 0.0419 | 3000   | 8.0635        |
+| 0.0433 | 3100   | 8.065         |
+| 0.0447 | 3200   | 8.0464        |
+| 0.0461 | 3300   | 8.0614        |
+| 0.0475 | 3400   | 8.0602        |
+| 0.0489 | 3500   | 8.0672        |
+| 0.0503 | 3600   | 8.0667        |
+| 0.0517 | 3700   | 8.0249        |
+| 0.0531 | 3800   | 8.0615        |
+| 0.0545 | 3900   | 8.0482        |
+| 0.0559 | 4000   | 8.0344        |
+| 0.0573 | 4100   | 8.0474        |
+| 0.0587 | 4200   | 8.0254        |
+| 0.0601 | 4300   | 8.0358        |
+| 0.0615 | 4400   | 8.0259        |
+| 0.0629 | 4500   | 8.0242        |
+| 0.0642 | 4600   | 7.9949        |
+| 0.0656 | 4700   | 8.0489        |
+| 0.0670 | 4800   | 8.0133        |
+| 0.0684 | 4900   | 7.983         |
+| 0.0698 | 5000   | 7.9952        |
+| 0.0712 | 5100   | 7.9972        |
+| 0.0726 | 5200   | 7.9932        |
+| 0.0740 | 5300   | 7.9921        |
+| 0.0754 | 5400   | 8.0143        |
+| 0.0768 | 5500   | 7.9816        |
+| 0.0782 | 5600   | 7.9691        |
+| 0.0796 | 5700   | 8.0094        |
+| 0.0810 | 5800   | 7.9749        |
+| 0.0824 | 5900   | 7.9938        |
+| 0.0838 | 6000   | 7.9718        |
+| 0.0852 | 6100   | 7.9842        |
+| 0.0866 | 6200   | 7.9649        |
+| 0.0880 | 6300   | 7.9719        |
+| 0.0894 | 6400   | 7.9676        |
+| 0.0908 | 6500   | 7.9628        |
+| 0.0922 | 6600   | 7.9626        |
+| 0.0936 | 6700   | 7.9601        |
+| 0.0950 | 6800   | 7.974         |
+| 0.0964 | 6900   | 7.9646        |
+| 0.0978 | 7000   | 7.9379        |
+| 0.0992 | 7100   | 7.9565        |
+| 0.1006 | 7200   | 7.9388        |
+| 0.1020 | 7300   | 7.9471        |
+| 0.1034 | 7400   | 7.9171        |
+| 0.1048 | 7500   | 7.915         |
+| 0.1062 | 7600   | 7.919         |
+| 0.1075 | 7700   | 7.9579        |
+| 0.1089 | 7800   | 7.9275        |
+| 0.1103 | 7900   | 7.9273        |
+| 0.1117 | 8000   | 7.9294        |
+| 0.1131 | 8100   | 7.9233        |
+| 0.1145 | 8200   | 7.9247        |
+| 0.1159 | 8300   | 7.9166        |
+| 0.1173 | 8400   | 7.928         |
+| 0.1187 | 8500   | 7.9068        |
+| 0.1201 | 8600   | 7.919         |
+| 0.1215 | 8700   | 7.8929        |
+| 0.1229 | 8800   | 7.9122        |
+| 0.1243 | 8900   | 7.9036        |
+| 0.1257 | 9000   | 7.8954        |
+| 0.1271 | 9100   | 7.8803        |
+| 0.1285 | 9200   | 7.9096        |
+| 0.1299 | 9300   | 7.9059        |
+| 0.1313 | 9400   | 7.8716        |
+| 0.1327 | 9500   | 7.8965        |
+| 0.1341 | 9600   | 7.9248        |
+| 0.1355 | 9700   | 7.8804        |
+| 0.1369 | 9800   | 7.8841        |
+| 0.1383 | 9900   | 7.8787        |
+| 0.1397 | 10000  | 7.8671        |
+| 0.1411 | 10100  | 7.8988        |
+| 0.1425 | 10200  | 7.8662        |
+| 0.1439 | 10300  | 7.8631        |
+| 0.1453 | 10400  | 7.8759        |
+| 0.1467 | 10500  | 7.8634        |
+| 0.1481 | 10600  | 7.8621        |
+| 0.1494 | 10700  | 7.8509        |
+| 0.1508 | 10800  | 7.8437        |
+| 0.1522 | 10900  | 7.8361        |
+| 0.1536 | 11000  | 7.868         |
+| 0.1550 | 11100  | 7.8887        |
+| 0.1564 | 11200  | 7.8747        |
+| 0.1578 | 11300  | 7.8719        |
+| 0.1592 | 11400  | 7.828         |
+| 0.1606 | 11500  | 7.8268        |
+| 0.1620 | 11600  | 7.8638        |
+| 0.1634 | 11700  | 7.8466        |
+| 0.1648 | 11800  | 7.8856        |
+| 0.1662 | 11900  | 7.8746        |
+| 0.1676 | 12000  | 7.8293        |
+| 0.1690 | 12100  | 7.8357        |
+| 0.1704 | 12200  | 7.8192        |
+| 0.1718 | 12300  | 7.8348        |
+| 0.1732 | 12400  | 7.8417        |
+| 0.1746 | 12500  | 7.8415        |
+| 0.1760 | 12600  | 7.8095        |
+| 0.1774 | 12700  | 7.8149        |
+| 0.1788 | 12800  | 7.8309        |
+| 0.1802 | 12900  | 7.8356        |
+| 0.1816 | 13000  | 7.8243        |
+| 0.1830 | 13100  | 7.8378        |
+| 0.1844 | 13200  | 7.8265        |
+| 0.1858 | 13300  | 7.8258        |
+| 0.1872 | 13400  | 7.808         |
+| 0.1886 | 13500  | 7.827         |
+| 0.1900 | 13600  | 7.8264        |
+| 0.1914 | 13700  | 7.8158        |
+| 0.1927 | 13800  | 7.8034        |
+| 0.1941 | 13900  | 7.8207        |
+| 0.1955 | 14000  | 7.7986        |
+| 0.1969 | 14100  | 7.8126        |
+| 0.1983 | 14200  | 7.8103        |
+| 0.1997 | 14300  | 7.7929        |
+| 0.2011 | 14400  | 7.8229        |
+| 0.2025 | 14500  | 7.8112        |
+| 0.2039 | 14600  | 7.8117        |
+| 0.2053 | 14700  | 7.8409        |
+| 0.2067 | 14800  | 7.7997        |
+| 0.2081 | 14900  | 7.8059        |
+| 0.2095 | 15000  | 7.7917        |
+| 0.2109 | 15100  | 7.8273        |
+| 0.2123 | 15200  | 7.8068        |
+| 0.2137 | 15300  | 7.8066        |
+| 0.2151 | 15400  | 7.7925        |
+| 0.2165 | 15500  | 7.7894        |
+| 0.2179 | 15600  | 7.7946        |
+| 0.2193 | 15700  | 7.7737        |
+| 0.2207 | 15800  | 7.7471        |
+| 0.2221 | 15900  | 7.7875        |
+| 0.2235 | 16000  | 7.7844        |
+| 0.2249 | 16100  | 7.7909        |
+| 0.2263 | 16200  | 7.7729        |
+| 0.2277 | 16300  | 7.7359        |
+| 0.2291 | 16400  | 7.7713        |
+| 0.2305 | 16500  | 7.7568        |
+| 0.2319 | 16600  | 7.7483        |
+| 0.2333 | 16700  | 7.8248        |
+| 0.2346 | 16800  | 7.76          |
+| 0.2360 | 16900  | 7.7332        |
+| 0.2374 | 17000  | 7.7894        |
+| 0.2388 | 17100  | 7.7706        |
+| 0.2402 | 17200  | 7.7997        |
+| 0.2416 | 17300  | 7.7674        |
+| 0.2430 | 17400  | 7.7531        |
+| 0.2444 | 17500  | 7.7372        |
+| 0.2458 | 17600  | 7.7449        |
+| 0.2472 | 17700  | 7.7369        |
+| 0.2486 | 17800  | 7.7559        |
+| 0.2500 | 17900  | 7.7437        |
+| 0.2514 | 18000  | 7.7738        |
+| 0.2528 | 18100  | 7.731         |
+| 0.2542 | 18200  | 7.7466        |
+| 0.2556 | 18300  | 7.7231        |
+| 0.2570 | 18400  | 7.7509        |
+| 0.2584 | 18500  | 7.712         |
+| 0.2598 | 18600  | 7.7415        |
+| 0.2612 | 18700  | 7.7097        |
+| 0.2626 | 18800  | 7.741         |
+| 0.2640 | 18900  | 7.7323        |
+| 0.2654 | 19000  | 7.7421        |
+| 0.2668 | 19100  | 7.7221        |
+| 0.2682 | 19200  | 7.7138        |
+| 0.2696 | 19300  | 7.7496        |
+| 0.2710 | 19400  | 7.7311        |
+| 0.2724 | 19500  | 7.7119        |
+| 0.2738 | 19600  | 7.6982        |
+| 0.2752 | 19700  | 7.7307        |
+| 0.2766 | 19800  | 7.7392        |
+| 0.2779 | 19900  | 7.7192        |
+| 0.2793 | 20000  | 7.711         |
+| 0.2807 | 20100  | 7.7051        |
+| 0.2821 | 20200  | 7.7276        |
+| 0.2835 | 20300  | 7.7433        |
+| 0.2849 | 20400  | 7.6985        |
+| 0.2863 | 20500  | 7.7243        |
+| 0.2877 | 20600  | 7.7004        |
+| 0.2891 | 20700  | 7.702         |
+| 0.2905 | 20800  | 7.7282        |
+| 0.2919 | 20900  | 7.7184        |
+| 0.2933 | 21000  | 7.7244        |
+| 0.2947 | 21100  | 7.7026        |
+| 0.2961 | 21200  | 7.7052        |
+| 0.2975 | 21300  | 7.7139        |
+| 0.2989 | 21400  | 7.7409        |
+| 0.3003 | 21500  | 7.7089        |
+| 0.3017 | 21600  | 7.7075        |
+| 0.3031 | 21700  | 7.7087        |
+| 0.3045 | 21800  | 7.6938        |
+| 0.3059 | 21900  | 7.7021        |
+| 0.3073 | 22000  | 7.7086        |
+| 0.3087 | 22100  | 7.7108        |
+| 0.3101 | 22200  | 7.7107        |
+| 0.3115 | 22300  | 7.6803        |
+| 0.3129 | 22400  | 7.7361        |
+| 0.3143 | 22500  | 7.7141        |
+| 0.3157 | 22600  | 7.7032        |
+| 0.3171 | 22700  | 7.6982        |
+| 0.3185 | 22800  | 7.704         |
+| 0.3199 | 22900  | 7.7382        |
+| 0.3212 | 23000  | 7.6877        |
+| 0.3226 | 23100  | 7.6841        |
+| 0.3240 | 23200  | 7.6967        |
+| 0.3254 | 23300  | 7.6743        |
+| 0.3268 | 23400  | 7.6883        |
+| 0.3282 | 23500  | 7.6817        |
+| 0.3296 | 23600  | 7.6974        |
+| 0.3310 | 23700  | 7.6739        |
+| 0.3324 | 23800  | 7.6901        |
+| 0.3338 | 23900  | 7.6787        |
+| 0.3352 | 24000  | 7.6588        |
+| 0.3366 | 24100  | 7.6395        |
+| 0.3380 | 24200  | 7.6823        |
+| 0.3394 | 24300  | 7.6869        |
+| 0.3408 | 24400  | 7.6955        |
+| 0.3422 | 24500  | 7.6883        |
+| 0.3436 | 24600  | 7.6559        |
+| 0.3450 | 24700  | 7.6749        |
+| 0.3464 | 24800  | 7.7027        |
+| 0.3478 | 24900  | 7.66          |
+| 0.3492 | 25000  | 7.6944        |
+| 0.3506 | 25100  | 7.6579        |
+| 0.3520 | 25200  | 7.6785        |
+| 0.3534 | 25300  | 7.6256        |
+| 0.3548 | 25400  | 7.6397        |
+| 0.3562 | 25500  | 7.6876        |
+| 0.3576 | 25600  | 7.6048        |
+| 0.3590 | 25700  | 7.6552        |
+| 0.3604 | 25800  | 7.6586        |
+| 0.3618 | 25900  | 7.684         |
+| 0.3631 | 26000  | 7.6663        |
+| 0.3645 | 26100  | 7.6365        |
+| 0.3659 | 26200  | 7.6428        |
+| 0.3673 | 26300  | 7.664         |
+| 0.3687 | 26400  | 7.667         |
+| 0.3701 | 26500  | 7.6688        |
+| 0.3715 | 26600  | 7.6851        |
+| 0.3729 | 26700  | 7.6363        |
+| 0.3743 | 26800  | 7.6653        |
+| 0.3757 | 26900  | 7.6045        |
+| 0.3771 | 27000  | 7.6808        |
+| 0.3785 | 27100  | 7.6702        |
+| 0.3799 | 27200  | 7.6865        |
+| 0.3813 | 27300  | 7.6526        |
+| 0.3827 | 27400  | 7.6366        |
+| 0.3841 | 27500  | 7.6531        |
+| 0.3855 | 27600  | 7.6101        |
+| 0.3869 | 27700  | 7.7053        |
+| 0.3883 | 27800  | 7.6464        |
+| 0.3897 | 27900  | 7.6573        |
+| 0.3911 | 28000  | 7.6346        |
+| 0.3925 | 28100  | 7.6761        |
+| 0.3939 | 28200  | 7.5953        |
+| 0.3953 | 28300  | 7.6597        |
+| 0.3967 | 28400  | 7.7015        |
+| 0.3981 | 28500  | 7.6681        |
+| 0.3995 | 28600  | 7.6132        |
+| 0.4009 | 28700  | 7.6378        |
+| 0.4023 | 28800  | 7.6311        |
+| 0.4037 | 28900  | 7.6349        |
+| 0.4051 | 29000  | 7.6295        |
+| 0.4064 | 29100  | 7.6114        |
+| 0.4078 | 29200  | 7.6839        |
+| 0.4092 | 29300  | 7.5965        |
+| 0.4106 | 29400  | 7.6351        |
+| 0.4120 | 29500  | 7.6114        |
+| 0.4134 | 29600  | 7.6111        |
+| 0.4148 | 29700  | 7.6095        |
+| 0.4162 | 29800  | 7.5959        |
+| 0.4176 | 29900  | 7.6009        |
+| 0.4190 | 30000  | 7.6542        |
+| 0.4204 | 30100  | 7.6446        |
+| 0.4218 | 30200  | 7.6177        |
+| 0.4232 | 30300  | 7.6361        |
+| 0.4246 | 30400  | 7.6426        |
+| 0.4260 | 30500  | 7.6226        |
+| 0.4274 | 30600  | 7.6074        |
+| 0.4288 | 30700  | 7.6257        |
+| 0.4302 | 30800  | 7.6683        |
+| 0.4316 | 30900  | 7.6385        |
+| 0.4330 | 31000  | 7.5898        |
+| 0.4344 | 31100  | 7.6374        |
+| 0.4358 | 31200  | 7.6052        |
+| 0.4372 | 31300  | 7.6044        |
+| 0.4386 | 31400  | 7.6226        |
+| 0.4400 | 31500  | 7.6415        |
+| 0.4414 | 31600  | 7.6226        |
+| 0.4428 | 31700  | 7.6336        |
+| 0.4442 | 31800  | 7.6106        |
+| 0.4456 | 31900  | 7.6771        |
+| 0.4470 | 32000  | 7.6026        |
+| 0.4483 | 32100  | 7.6129        |
+| 0.4497 | 32200  | 7.602         |
+| 0.4511 | 32300  | 7.6007        |
+| 0.4525 | 32400  | 7.6113        |
+| 0.4539 | 32500  | 7.6293        |
+| 0.4553 | 32600  | 7.5768        |
+| 0.4567 | 32700  | 7.6144        |
+| 0.4581 | 32800  | 7.6178        |
+| 0.4595 | 32900  | 7.6418        |
+| 0.4609 | 33000  | 7.5932        |
+| 0.4623 | 33100  | 7.6414        |
+| 0.4637 | 33200  | 7.5972        |
+| 0.4651 | 33300  | 7.6166        |
+| 0.4665 | 33400  | 7.5887        |
+| 0.4679 | 33500  | 7.64          |
+| 0.4693 | 33600  | 7.5756        |
+| 0.4707 | 33700  | 7.6185        |
+| 0.4721 | 33800  | 7.5707        |
+| 0.4735 | 33900  | 7.617         |
+| 0.4749 | 34000  | 7.6021        |
+| 0.4763 | 34100  | 7.5814        |
+| 0.4777 | 34200  | 7.5848        |
+| 0.4791 | 34300  | 7.6053        |
+| 0.4805 | 34400  | 7.6331        |
+| 0.4819 | 34500  | 7.5848        |
+| 0.4833 | 34600  | 7.5706        |
+| 0.4847 | 34700  | 7.5536        |
+| 0.4861 | 34800  | 7.5782        |
+| 0.4875 | 34900  | 7.6322        |
+| 0.4889 | 35000  | 7.6031        |
+| 0.4903 | 35100  | 7.5994        |
+| 0.4916 | 35200  | 7.5684        |
+| 0.4930 | 35300  | 7.5978        |
+| 0.4944 | 35400  | 7.5918        |
+| 0.4958 | 35500  | 7.575         |
+| 0.4972 | 35600  | 7.5607        |
+| 0.4986 | 35700  | 7.5845        |
+| 0.5000 | 35800  | 7.5853        |
+| 0.5014 | 35900  | 7.571         |
+| 0.5028 | 36000  | 7.6007        |
+| 0.5042 | 36100  | 7.5946        |
+| 0.5056 | 36200  | 7.5754        |
+| 0.5070 | 36300  | 7.5845        |
+| 0.5084 | 36400  | 7.5302        |
+| 0.5098 | 36500  | 7.6311        |
+| 0.5112 | 36600  | 7.5893        |
+| 0.5126 | 36700  | 7.6081        |
+| 0.5140 | 36800  | 7.586         |
+| 0.5154 | 36900  | 7.6083        |
+| 0.5168 | 37000  | 7.5997        |
+| 0.5182 | 37100  | 7.5887        |
+| 0.5196 | 37200  | 7.5844        |
+| 0.5210 | 37300  | 7.5787        |
+| 0.5224 | 37400  | 7.5669        |
+| 0.5238 | 37500  | 7.5913        |
+| 0.5252 | 37600  | 7.5495        |
+| 0.5266 | 37700  | 7.6103        |
+| 0.5280 | 37800  | 7.5815        |
+| 0.5294 | 37900  | 7.6002        |
+| 0.5308 | 38000  | 7.5576        |
+| 0.5322 | 38100  | 7.5679        |
+| 0.5335 | 38200  | 7.5552        |
+| 0.5349 | 38300  | 7.548         |
+| 0.5363 | 38400  | 7.6028        |
+| 0.5377 | 38500  | 7.5662        |
+| 0.5391 | 38600  | 7.6073        |
+| 0.5405 | 38700  | 7.5565        |
+| 0.5419 | 38800  | 7.5448        |
+| 0.5433 | 38900  | 7.5782        |
+| 0.5447 | 39000  | 7.5911        |
+| 0.5461 | 39100  | 7.5661        |
+| 0.5475 | 39200  | 7.5355        |
+| 0.5489 | 39300  | 7.5251        |
+| 0.5503 | 39400  | 7.5508        |
+| 0.5517 | 39500  | 7.5893        |
+| 0.5531 | 39600  | 7.5748        |
+| 0.5545 | 39700  | 7.5789        |
+| 0.5559 | 39800  | 7.5622        |
+| 0.5573 | 39900  | 7.5162        |
+| 0.5587 | 40000  | 7.5782        |
+| 0.5601 | 40100  | 7.5872        |
+| 0.5615 | 40200  | 7.5919        |
+| 0.5629 | 40300  | 7.5753        |
+| 0.5643 | 40400  | 7.5727        |
+| 0.5657 | 40500  | 7.5961        |
+| 0.5671 | 40600  | 7.5512        |
+| 0.5685 | 40700  | 7.5434        |
+| 0.5699 | 40800  | 7.518         |
+| 0.5713 | 40900  | 7.5662        |
+| 0.5727 | 41000  | 7.5496        |
+| 0.5741 | 41100  | 7.5421        |
+| 0.5755 | 41200  | 7.575         |
+| 0.5768 | 41300  | 7.5667        |
+| 0.5782 | 41400  | 7.5781        |
+| 0.5796 | 41500  | 7.5606        |
+| 0.5810 | 41600  | 7.5699        |
+| 0.5824 | 41700  | 7.543         |
+| 0.5838 | 41800  | 7.589         |
+| 0.5852 | 41900  | 7.5587        |
+| 0.5866 | 42000  | 7.5488        |
+| 0.5880 | 42100  | 7.5919        |
+| 0.5894 | 42200  | 7.5495        |
+| 0.5908 | 42300  | 7.5454        |
+| 0.5922 | 42400  | 7.5522        |
+| 0.5936 | 42500  | 7.5552        |
+| 0.5950 | 42600  | 7.5715        |
+| 0.5964 | 42700  | 7.5471        |
+| 0.5978 | 42800  | 7.5359        |
+| 0.5992 | 42900  | 7.5817        |
+| 0.6006 | 43000  | 7.5375        |
+| 0.6020 | 43100  | 7.5538        |
+| 0.6034 | 43200  | 7.574         |
+| 0.6048 | 43300  | 7.5564        |
+| 0.6062 | 43400  | 7.5597        |
+| 0.6076 | 43500  | 7.5498        |
+| 0.6090 | 43600  | 7.5369        |
+| 0.6104 | 43700  | 7.562         |
+| 0.6118 | 43800  | 7.5521        |
+| 0.6132 | 43900  | 7.5482        |
+| 0.6146 | 44000  | 7.5298        |
+| 0.6160 | 44100  | 7.5174        |
+| 0.6174 | 44200  | 7.5421        |
+| 0.6187 | 44300  | 7.563         |
+| 0.6201 | 44400  | 7.5165        |
+| 0.6215 | 44500  | 7.5284        |
+| 0.6229 | 44600  | 7.5089        |
+| 0.6243 | 44700  | 7.5352        |
+| 0.6257 | 44800  | 7.5407        |
+| 0.6271 | 44900  | 7.5336        |
+| 0.6285 | 45000  | 7.5842        |
+| 0.6299 | 45100  | 7.5239        |
+| 0.6313 | 45200  | 7.5428        |
+| 0.6327 | 45300  | 7.526         |
+| 0.6341 | 45400  | 7.5117        |
+| 0.6355 | 45500  | 7.5288        |
+| 0.6369 | 45600  | 7.515         |
+| 0.6383 | 45700  | 7.5327        |
+| 0.6397 | 45800  | 7.5148        |
+| 0.6411 | 45900  | 7.5304        |
+| 0.6425 | 46000  | 7.5363        |
+| 0.6439 | 46100  | 7.5321        |
+| 0.6453 | 46200  | 7.522         |
+| 0.6467 | 46300  | 7.526         |
+| 0.6481 | 46400  | 7.4987        |
+| 0.6495 | 46500  | 7.4985        |
+| 0.6509 | 46600  | 7.5122        |
+| 0.6523 | 46700  | 7.4879        |
+| 0.6537 | 46800  | 7.5162        |
+| 0.6551 | 46900  | 7.5705        |
+| 0.6565 | 47000  | 7.5324        |
+| 0.6579 | 47100  | 7.5239        |
+| 0.6593 | 47200  | 7.5208        |
+| 0.6607 | 47300  | 7.5647        |
+| 0.6620 | 47400  | 7.527         |
+| 0.6634 | 47500  | 7.5208        |
+| 0.6648 | 47600  | 7.5256        |
+| 0.6662 | 47700  | 7.5009        |
+| 0.6676 | 47800  | 7.5164        |
+| 0.6690 | 47900  | 7.5387        |
+| 0.6704 | 48000  | 7.5217        |
+| 0.6718 | 48100  | 7.5388        |
+| 0.6732 | 48200  | 7.5101        |
+| 0.6746 | 48300  | 7.5101        |
+| 0.6760 | 48400  | 7.5359        |
+| 0.6774 | 48500  | 7.5372        |
+| 0.6788 | 48600  | 7.555         |
+| 0.6802 | 48700  | 7.4809        |
+| 0.6816 | 48800  | 7.5207        |
+| 0.6830 | 48900  | 7.4817        |
+| 0.6844 | 49000  | 7.4865        |
+| 0.6858 | 49100  | 7.5301        |
+| 0.6872 | 49200  | 7.5143        |
+| 0.6886 | 49300  | 7.4739        |
+| 0.6900 | 49400  | 7.515         |
+| 0.6914 | 49500  | 7.5255        |
+| 0.6928 | 49600  | 7.5368        |
+| 0.6942 | 49700  | 7.4903        |
+| 0.6956 | 49800  | 7.5401        |
+| 0.6970 | 49900  | 7.5503        |
+| 0.6984 | 50000  | 7.5551        |
+| 0.6998 | 50100  | 7.4994        |
+| 0.7012 | 50200  | 7.4759        |
+| 0.7026 | 50300  | 7.4932        |
+| 0.7039 | 50400  | 7.534         |
+| 0.7053 | 50500  | 7.5128        |
+| 0.7067 | 50600  | 7.4932        |
+| 0.7081 | 50700  | 7.4578        |
+| 0.7095 | 50800  | 7.4798        |
+| 0.7109 | 50900  | 7.5085        |
+| 0.7123 | 51000  | 7.4953        |
+| 0.7137 | 51100  | 7.4764        |
+| 0.7151 | 51200  | 7.5076        |
+| 0.7165 | 51300  | 7.5167        |
+| 0.7179 | 51400  | 7.5326        |
+| 0.7193 | 51500  | 7.5192        |
+| 0.7207 | 51600  | 7.548         |
+| 0.7221 | 51700  | 7.506         |
+| 0.7235 | 51800  | 7.5137        |
+| 0.7249 | 51900  | 7.5348        |
+| 0.7263 | 52000  | 7.4981        |
+| 0.7277 | 52100  | 7.4936        |
+| 0.7291 | 52200  | 7.4745        |
+| 0.7305 | 52300  | 7.4885        |
+| 0.7319 | 52400  | 7.522         |
+| 0.7333 | 52500  | 7.5265        |
+| 0.7347 | 52600  | 7.4989        |
+| 0.7361 | 52700  | 7.5173        |
+| 0.7375 | 52800  | 7.4638        |
+| 0.7389 | 52900  | 7.4649        |
+| 0.7403 | 53000  | 7.5227        |
+| 0.7417 | 53100  | 7.4796        |
+| 0.7431 | 53200  | 7.5207        |
+| 0.7445 | 53300  | 7.4882        |
+| 0.7459 | 53400  | 7.5516        |
+| 0.7472 | 53500  | 7.5309        |
+| 0.7486 | 53600  | 7.5256        |
+| 0.7500 | 53700  | 7.4669        |
+| 0.7514 | 53800  | 7.5096        |
+| 0.7528 | 53900  | 7.501         |
+| 0.7542 | 54000  | 7.4802        |
+| 0.7556 | 54100  | 7.5025        |
+| 0.7570 | 54200  | 7.4766        |
+| 0.7584 | 54300  | 7.4708        |
+| 0.7598 | 54400  | 7.4649        |
+| 0.7612 | 54500  | 7.4744        |
+| 0.7626 | 54600  | 7.5057        |
+| 0.7640 | 54700  | 7.5358        |
+| 0.7654 | 54800  | 7.5062        |
+| 0.7668 | 54900  | 7.4968        |
+| 0.7682 | 55000  | 7.4995        |
+| 0.7696 | 55100  | 7.4972        |
+| 0.7710 | 55200  | 7.4894        |
+| 0.7724 | 55300  | 7.5039        |
+| 0.7738 | 55400  | 7.4545        |
+| 0.7752 | 55500  | 7.4652        |
+| 0.7766 | 55600  | 7.5129        |
+| 0.7780 | 55700  | 7.4947        |
+| 0.7794 | 55800  | 7.4579        |
+| 0.7808 | 55900  | 7.5092        |
+| 0.7822 | 56000  | 7.483         |
+| 0.7836 | 56100  | 7.5105        |
+| 0.7850 | 56200  | 7.4952        |
+| 0.7864 | 56300  | 7.5019        |
+| 0.7878 | 56400  | 7.4451        |
+| 0.7892 | 56500  | 7.4659        |
+| 0.7905 | 56600  | 7.4908        |
+| 0.7919 | 56700  | 7.4606        |
+| 0.7933 | 56800  | 7.5125        |
+| 0.7947 | 56900  | 7.4814        |
+| 0.7961 | 57000  | 7.4796        |
+| 0.7975 | 57100  | 7.4848        |
+| 0.7989 | 57200  | 7.504         |
+| 0.8003 | 57300  | 7.5136        |
+| 0.8017 | 57400  | 7.5122        |
+| 0.8031 | 57500  | 7.4649        |
+| 0.8045 | 57600  | 7.454         |
+| 0.8059 | 57700  | 7.453         |
+| 0.8073 | 57800  | 7.4835        |
+| 0.8087 | 57900  | 7.5299        |
+| 0.8101 | 58000  | 7.513         |
+| 0.8115 | 58100  | 7.4369        |
+| 0.8129 | 58200  | 7.5271        |
+| 0.8143 | 58300  | 7.4935        |
+| 0.8157 | 58400  | 7.476         |
+| 0.8171 | 58500  | 7.4865        |
+| 0.8185 | 58600  | 7.519         |
+| 0.8199 | 58700  | 7.4615        |
+| 0.8213 | 58800  | 7.4702        |
+| 0.8227 | 58900  | 7.4628        |
+| 0.8241 | 59000  | 7.5267        |
+| 0.8255 | 59100  | 7.489         |
+| 0.8269 | 59200  | 7.4826        |
+| 0.8283 | 59300  | 7.4526        |
+| 0.8297 | 59400  | 7.4631        |
+| 0.8311 | 59500  | 7.4789        |
+| 0.8324 | 59600  | 7.5113        |
+| 0.8338 | 59700  | 7.4752        |
+| 0.8352 | 59800  | 7.5079        |
+| 0.8366 | 59900  | 7.4918        |
+| 0.8380 | 60000  | 7.4491        |
+| 0.8394 | 60100  | 7.501         |
+| 0.8408 | 60200  | 7.4831        |
+| 0.8422 | 60300  | 7.4731        |
+| 0.8436 | 60400  | 7.4578        |
+| 0.8450 | 60500  | 7.4536        |
+| 0.8464 | 60600  | 7.4684        |
+| 0.8478 | 60700  | 7.4523        |
+| 0.8492 | 60800  | 7.4843        |
+| 0.8506 | 60900  | 7.4367        |
+| 0.8520 | 61000  | 7.5007        |
+| 0.8534 | 61100  | 7.5152        |
+| 0.8548 | 61200  | 7.5043        |
+| 0.8562 | 61300  | 7.4663        |
+| 0.8576 | 61400  | 7.4824        |
+| 0.8590 | 61500  | 7.4828        |
+| 0.8604 | 61600  | 7.5054        |
+| 0.8618 | 61700  | 7.5155        |
+| 0.8632 | 61800  | 7.5139        |
+| 0.8646 | 61900  | 7.4603        |
+| 0.8660 | 62000  | 7.4706        |
+| 0.8674 | 62100  | 7.4581        |
+| 0.8688 | 62200  | 7.4556        |
+| 0.8702 | 62300  | 7.4949        |
+| 0.8716 | 62400  | 7.4812        |
+| 0.8730 | 62500  | 7.4586        |
+| 0.8744 | 62600  | 7.459         |
+| 0.8757 | 62700  | 7.4496        |
+| 0.8771 | 62800  | 7.4393        |
+| 0.8785 | 62900  | 7.4564        |
+| 0.8799 | 63000  | 7.4627        |
+| 0.8813 | 63100  | 7.4513        |
+| 0.8827 | 63200  | 7.4596        |
+| 0.8841 | 63300  | 7.4805        |
+| 0.8855 | 63400  | 7.4279        |
+| 0.8869 | 63500  | 7.4887        |
+| 0.8883 | 63600  | 7.4519        |
+| 0.8897 | 63700  | 7.4566        |
+| 0.8911 | 63800  | 7.4547        |
+| 0.8925 | 63900  | 7.4422        |
+| 0.8939 | 64000  | 7.4777        |
+| 0.8953 | 64100  | 7.4437        |
+| 0.8967 | 64200  | 7.502         |
+| 0.8981 | 64300  | 7.4349        |
+| 0.8995 | 64400  | 7.4929        |
+| 0.9009 | 64500  | 7.4436        |
+| 0.9023 | 64600  | 7.4263        |
+| 0.9037 | 64700  | 7.5016        |
+| 0.9051 | 64800  | 7.4396        |
+| 0.9065 | 64900  | 7.4313        |
+| 0.9079 | 65000  | 7.4591        |
+| 0.9093 | 65100  | 7.4261        |
+| 0.9107 | 65200  | 7.4038        |
+| 0.9121 | 65300  | 7.4548        |
+| 0.9135 | 65400  | 7.4305        |
+| 0.9149 | 65500  | 7.4705        |
+| 0.9163 | 65600  | 7.4293        |
+| 0.9176 | 65700  | 7.447         |
+| 0.9190 | 65800  | 7.4771        |
+| 0.9204 | 65900  | 7.4565        |
+| 0.9218 | 66000  | 7.4388        |
+| 0.9232 | 66100  | 7.4339        |
+| 0.9246 | 66200  | 7.4576        |
+| 0.9260 | 66300  | 7.4399        |
+| 0.9274 | 66400  | 7.4484        |
+| 0.9288 | 66500  | 7.4819        |
+| 0.9302 | 66600  | 7.4511        |
+| 0.9316 | 66700  | 7.4931        |
+| 0.9330 | 66800  | 7.3958        |
+| 0.9344 | 66900  | 7.4145        |
+| 0.9358 | 67000  | 7.4576        |
+| 0.9372 | 67100  | 7.4762        |
+| 0.9386 | 67200  | 7.4915        |
+| 0.9400 | 67300  | 7.4482        |
+| 0.9414 | 67400  | 7.405         |
+| 0.9428 | 67500  | 7.4455        |
+| 0.9442 | 67600  | 7.4361        |
+| 0.9456 | 67700  | 7.4063        |
+| 0.9470 | 67800  | 7.471         |
+| 0.9484 | 67900  | 7.4358        |
+| 0.9498 | 68000  | 7.4549        |
+| 0.9512 | 68100  | 7.4377        |
+| 0.9526 | 68200  | 7.4748        |
+| 0.9540 | 68300  | 7.4464        |
+| 0.9554 | 68400  | 7.4521        |
+| 0.9568 | 68500  | 7.4831        |
+| 0.9582 | 68600  | 7.477         |
+| 0.9596 | 68700  | 7.444         |
+| 0.9609 | 68800  | 7.4955        |
+| 0.9623 | 68900  | 7.4182        |
+| 0.9637 | 69000  | 7.4425        |
+| 0.9651 | 69100  | 7.4489        |
+| 0.9665 | 69200  | 7.4088        |
+| 0.9679 | 69300  | 7.396         |
+| 0.9693 | 69400  | 7.4612        |
+| 0.9707 | 69500  | 7.3897        |
+| 0.9721 | 69600  | 7.42          |
+| 0.9735 | 69700  | 7.4507        |
+| 0.9749 | 69800  | 7.4757        |
+| 0.9763 | 69900  | 7.4319        |
+| 0.9777 | 70000  | 7.4576        |
+| 0.9791 | 70100  | 7.4226        |
+| 0.9805 | 70200  | 7.4274        |
+| 0.9819 | 70300  | 7.4741        |
+| 0.9833 | 70400  | 7.4664        |
+| 0.9847 | 70500  | 7.4765        |
+| 0.9861 | 70600  | 7.4254        |
+| 0.9875 | 70700  | 7.4066        |
+| 0.9889 | 70800  | 7.4404        |
+| 0.9903 | 70900  | 7.4147        |
+| 0.9917 | 71000  | 7.4029        |
+| 0.9931 | 71100  | 7.4446        |
+| 0.9945 | 71200  | 7.3877        |
+| 0.9959 | 71300  | 7.4271        |
+| 0.9973 | 71400  | 7.4438        |
+| 0.9987 | 71500  | 7.4293        |
+| 1.0001 | 71600  | 7.39          |
+| 1.0015 | 71700  | 7.386         |
+| 1.0028 | 71800  | 7.4045        |
+| 1.0042 | 71900  | 7.4088        |
+| 1.0056 | 72000  | 7.4289        |
+| 1.0070 | 72100  | 7.3862        |
+| 1.0084 | 72200  | 7.4724        |
+| 1.0098 | 72300  | 7.4537        |
+| 1.0112 | 72400  | 7.3952        |
+| 1.0126 | 72500  | 7.4542        |
+| 1.0140 | 72600  | 7.409         |
+| 1.0154 | 72700  | 7.4636        |
+| 1.0168 | 72800  | 7.3787        |
+| 1.0182 | 72900  | 7.4571        |
+| 1.0196 | 73000  | 7.4341        |
+| 1.0210 | 73100  | 7.4312        |
+| 1.0224 | 73200  | 7.455         |
+| 1.0238 | 73300  | 7.4504        |
+| 1.0252 | 73400  | 7.4112        |
+| 1.0266 | 73500  | 7.3949        |
+| 1.0280 | 73600  | 7.4156        |
+| 1.0294 | 73700  | 7.3914        |
+| 1.0308 | 73800  | 7.4168        |
+| 1.0322 | 73900  | 7.4476        |
+| 1.0336 | 74000  | 7.4095        |
+| 1.0350 | 74100  | 7.3721        |
+| 1.0364 | 74200  | 7.4356        |
+| 1.0378 | 74300  | 7.3945        |
+| 1.0392 | 74400  | 7.3911        |
+| 1.0406 | 74500  | 7.3959        |
+| 1.0420 | 74600  | 7.4217        |
+| 1.0434 | 74700  | 7.4075        |
+| 1.0448 | 74800  | 7.4174        |
+| 1.0461 | 74900  | 7.42          |
+| 1.0475 | 75000  | 7.4214        |
+| 1.0489 | 75100  | 7.3647        |
+| 1.0503 | 75200  | 7.3671        |
+| 1.0517 | 75300  | 7.4565        |
+| 1.0531 | 75400  | 7.404         |
+| 1.0545 | 75500  | 7.4019        |
+| 1.0559 | 75600  | 7.3844        |
+| 1.0573 | 75700  | 7.4298        |
+| 1.0587 | 75800  | 7.4311        |
+| 1.0601 | 75900  | 7.4047        |
+| 1.0615 | 76000  | 7.4078        |
+| 1.0629 | 76100  | 7.4742        |
+| 1.0643 | 76200  | 7.4481        |
+| 1.0657 | 76300  | 7.3559        |
+| 1.0671 | 76400  | 7.3878        |
+| 1.0685 | 76500  | 7.3945        |
+| 1.0699 | 76600  | 7.4159        |
+| 1.0713 | 76700  | 7.4341        |
+| 1.0727 | 76800  | 7.4221        |
+| 1.0741 | 76900  | 7.4849        |
+| 1.0755 | 77000  | 7.4075        |
+| 1.0769 | 77100  | 7.394         |
+| 1.0783 | 77200  | 7.3852        |
+| 1.0797 | 77300  | 7.3751        |
+| 1.0811 | 77400  | 7.4226        |
+| 1.0825 | 77500  | 7.38          |
+| 1.0839 | 77600  | 7.4084        |
+| 1.0853 | 77700  | 7.3755        |
+| 1.0867 | 77800  | 7.3879        |
+| 1.0880 | 77900  | 7.375         |
+| 1.0894 | 78000  | 7.3899        |
+| 1.0908 | 78100  | 7.4605        |
+| 1.0922 | 78200  | 7.4317        |
+| 1.0936 | 78300  | 7.413         |
+| 1.0950 | 78400  | 7.4031        |
+| 1.0964 | 78500  | 7.4094        |
+| 1.0978 | 78600  | 7.4168        |
+| 1.0992 | 78700  | 7.3952        |
+| 1.1006 | 78800  | 7.4401        |
+| 1.1020 | 78900  | 7.3392        |
+| 1.1034 | 79000  | 7.4333        |
+| 1.1048 | 79100  | 7.4113        |
+| 1.1062 | 79200  | 7.3885        |
+| 1.1076 | 79300  | 7.4112        |
+| 1.1090 | 79400  | 7.4182        |
+| 1.1104 | 79500  | 7.4378        |
+| 1.1118 | 79600  | 7.4014        |
+| 1.1132 | 79700  | 7.3949        |
+| 1.1146 | 79800  | 7.3834        |
+| 1.1160 | 79900  | 7.3816        |
+| 1.1174 | 80000  | 7.3646        |
+| 1.1188 | 80100  | 7.3784        |
+| 1.1202 | 80200  | 7.3724        |
+| 1.1216 | 80300  | 7.3906        |
+| 1.1230 | 80400  | 7.4309        |
+| 1.1244 | 80500  | 7.4435        |
+| 1.1258 | 80600  | 7.4319        |
+| 1.1272 | 80700  | 7.3935        |
+| 1.1286 | 80800  | 7.3904        |
+| 1.1300 | 80900  | 7.4068        |
+| 1.1313 | 81000  | 7.4046        |
+| 1.1327 | 81100  | 7.3928        |
+| 1.1341 | 81200  | 7.4125        |
+| 1.1355 | 81300  | 7.399         |
+| 1.1369 | 81400  | 7.3605        |
+| 1.1383 | 81500  | 7.4077        |
+| 1.1397 | 81600  | 7.4355        |
+| 1.1411 | 81700  | 7.41          |
+| 1.1425 | 81800  | 7.3839        |
+| 1.1439 | 81900  | 7.3621        |
+| 1.1453 | 82000  | 7.3875        |
+| 1.1467 | 82100  | 7.4002        |
+| 1.1481 | 82200  | 7.3748        |
+| 1.1495 | 82300  | 7.4021        |
+| 1.1509 | 82400  | 7.4159        |
+| 1.1523 | 82500  | 7.398         |
+| 1.1537 | 82600  | 7.4328        |
+| 1.1551 | 82700  | 7.416         |
+| 1.1565 | 82800  | 7.4136        |
+| 1.1579 | 82900  | 7.417         |
+| 1.1593 | 83000  | 7.3902        |
+| 1.1607 | 83100  | 7.4206        |
+| 1.1621 | 83200  | 7.4269        |
+| 1.1635 | 83300  | 7.3838        |
+| 1.1649 | 83400  | 7.3465        |
+| 1.1663 | 83500  | 7.3732        |
+| 1.1677 | 83600  | 7.3781        |
+| 1.1691 | 83700  | 7.4024        |
+| 1.1705 | 83800  | 7.4153        |
+| 1.1719 | 83900  | 7.3828        |
+| 1.1732 | 84000  | 7.3904        |
+| 1.1746 | 84100  | 7.3515        |
+| 1.1760 | 84200  | 7.3461        |
+| 1.1774 | 84300  | 7.399         |
+| 1.1788 | 84400  | 7.3873        |
+| 1.1802 | 84500  | 7.3708        |
+| 1.1816 | 84600  | 7.391         |
+| 1.1830 | 84700  | 7.3806        |
+| 1.1844 | 84800  | 7.451         |
+| 1.1858 | 84900  | 7.4261        |
+| 1.1872 | 85000  | 7.41          |
+| 1.1886 | 85100  | 7.3995        |
+| 1.1900 | 85200  | 7.367         |
+| 1.1914 | 85300  | 7.4446        |
+| 1.1928 | 85400  | 7.3949        |
+| 1.1942 | 85500  | 7.3417        |
+| 1.1956 | 85600  | 7.3965        |
+| 1.1970 | 85700  | 7.3651        |
+| 1.1984 | 85800  | 7.4063        |
+| 1.1998 | 85900  | 7.4011        |
+| 1.2012 | 86000  | 7.3924        |
+| 1.2026 | 86100  | 7.3655        |
+| 1.2040 | 86200  | 7.3998        |
+| 1.2054 | 86300  | 7.3935        |
+| 1.2068 | 86400  | 7.4119        |
+| 1.2082 | 86500  | 7.4146        |
+| 1.2096 | 86600  | 7.3772        |
+| 1.2110 | 86700  | 7.4039        |
+| 1.2124 | 86800  | 7.3705        |
+| 1.2138 | 86900  | 7.3799        |
+| 1.2152 | 87000  | 7.3924        |
+| 1.2165 | 87100  | 7.3793        |
+| 1.2179 | 87200  | 7.3873        |
+| 1.2193 | 87300  | 7.4083        |
+| 1.2207 | 87400  | 7.3512        |
+| 1.2221 | 87500  | 7.3968        |
+| 1.2235 | 87600  | 7.3709        |
+| 1.2249 | 87700  | 7.4605        |
+| 1.2263 | 87800  | 7.4164        |
+| 1.2277 | 87900  | 7.3767        |
+| 1.2291 | 88000  | 7.3862        |
+| 1.2305 | 88100  | 7.3939        |
+| 1.2319 | 88200  | 7.4537        |
+| 1.2333 | 88300  | 7.3728        |
+| 1.2347 | 88400  | 7.3853        |
+| 1.2361 | 88500  | 7.3196        |
+| 1.2375 | 88600  | 7.3334        |
+| 1.2389 | 88700  | 7.4594        |
+| 1.2403 | 88800  | 7.4016        |
+| 1.2417 | 88900  | 7.3674        |
+| 1.2431 | 89000  | 7.4007        |
+| 1.2445 | 89100  | 7.3521        |
+| 1.2459 | 89200  | 7.3883        |
+| 1.2473 | 89300  | 7.4061        |
+| 1.2487 | 89400  | 7.3284        |
+| 1.2501 | 89500  | 7.4131        |
+| 1.2515 | 89600  | 7.4153        |
+| 1.2529 | 89700  | 7.4159        |
+| 1.2543 | 89800  | 7.3622        |
+| 1.2557 | 89900  | 7.3334        |
+| 1.2571 | 90000  | 7.3869        |
+| 1.2585 | 90100  | 7.3759        |
+| 1.2598 | 90200  | 7.3799        |
+| 1.2612 | 90300  | 7.433         |
+| 1.2626 | 90400  | 7.3396        |
+| 1.2640 | 90500  | 7.3765        |
+| 1.2654 | 90600  | 7.4136        |
+| 1.2668 | 90700  | 7.3832        |
+| 1.2682 | 90800  | 7.4149        |
+| 1.2696 | 90900  | 7.4054        |
+| 1.2710 | 91000  | 7.371         |
+| 1.2724 | 91100  | 7.3702        |
+| 1.2738 | 91200  | 7.388         |
+| 1.2752 | 91300  | 7.3775        |
+| 1.2766 | 91400  | 7.3839        |
+| 1.2780 | 91500  | 7.3636        |
+| 1.2794 | 91600  | 7.418         |
+| 1.2808 | 91700  | 7.3682        |
+| 1.2822 | 91800  | 7.3988        |
+| 1.2836 | 91900  | 7.4208        |
+| 1.2850 | 92000  | 7.383         |
+| 1.2864 | 92100  | 7.4278        |
+| 1.2878 | 92200  | 7.3628        |
+| 1.2892 | 92300  | 7.3842        |
+| 1.2906 | 92400  | 7.3431        |
+| 1.2920 | 92500  | 7.3436        |
+| 1.2934 | 92600  | 7.376         |
+| 1.2948 | 92700  | 7.3636        |
+| 1.2962 | 92800  | 7.3848        |
+| 1.2976 | 92900  | 7.3795        |
+| 1.2990 | 93000  | 7.3964        |
+| 1.3004 | 93100  | 7.3881        |
+| 1.3017 | 93200  | 7.4067        |
+| 1.3031 | 93300  | 7.3716        |
+| 1.3045 | 93400  | 7.4296        |
+| 1.3059 | 93500  | 7.3807        |
+| 1.3073 | 93600  | 7.4189        |
+| 1.3087 | 93700  | 7.3814        |
+| 1.3101 | 93800  | 7.4041        |
+| 1.3115 | 93900  | 7.3512        |
+| 1.3129 | 94000  | 7.3569        |
+| 1.3143 | 94100  | 7.3983        |
+| 1.3157 | 94200  | 7.4096        |
+| 1.3171 | 94300  | 7.3406        |
+| 1.3185 | 94400  | 7.3365        |
+| 1.3199 | 94500  | 7.3864        |
+| 1.3213 | 94600  | 7.299         |
+| 1.3227 | 94700  | 7.4536        |
+| 1.3241 | 94800  | 7.3449        |
+| 1.3255 | 94900  | 7.341         |
+| 1.3269 | 95000  | 7.397         |
+| 1.3283 | 95100  | 7.3709        |
+| 1.3297 | 95200  | 7.3635        |
+| 1.3311 | 95300  | 7.375         |
+| 1.3325 | 95400  | 7.3798        |
+| 1.3339 | 95500  | 7.3722        |
+| 1.3353 | 95600  | 7.374         |
+| 1.3367 | 95700  | 7.3381        |
+| 1.3381 | 95800  | 7.4135        |
+| 1.3395 | 95900  | 7.3561        |
+| 1.3409 | 96000  | 7.3843        |
+| 1.3423 | 96100  | 7.387         |
+| 1.3437 | 96200  | 7.3126        |
+| 1.3450 | 96300  | 7.3868        |
+| 1.3464 | 96400  | 7.4043        |
+| 1.3478 | 96500  | 7.3999        |
+| 1.3492 | 96600  | 7.3701        |
+| 1.3506 | 96700  | 7.3605        |
+| 1.3520 | 96800  | 7.3592        |
+| 1.3534 | 96900  | 7.392         |
+| 1.3548 | 97000  | 7.3975        |
+| 1.3562 | 97100  | 7.3544        |
+| 1.3576 | 97200  | 7.3849        |
+| 1.3590 | 97300  | 7.3532        |
+| 1.3604 | 97400  | 7.4159        |
+| 1.3618 | 97500  | 7.3468        |
+| 1.3632 | 97600  | 7.3625        |
+| 1.3646 | 97700  | 7.4235        |
+| 1.3660 | 97800  | 7.3785        |
+| 1.3674 | 97900  | 7.3577        |
+| 1.3688 | 98000  | 7.3659        |
+| 1.3702 | 98100  | 7.428         |
+| 1.3716 | 98200  | 7.3648        |
+| 1.3730 | 98300  | 7.371         |
+| 1.3744 | 98400  | 7.3481        |
+| 1.3758 | 98500  | 7.3622        |
+| 1.3772 | 98600  | 7.3946        |
+| 1.3786 | 98700  | 7.3902        |
+| 1.3800 | 98800  | 7.4024        |
+| 1.3814 | 98900  | 7.3414        |
+| 1.3828 | 99000  | 7.32          |
+| 1.3842 | 99100  | 7.3545        |
+| 1.3856 | 99200  | 7.3425        |
+| 1.3869 | 99300  | 7.3701        |
+| 1.3883 | 99400  | 7.3459        |
+| 1.3897 | 99500  | 7.3837        |
+| 1.3911 | 99600  | 7.3928        |
+| 1.3925 | 99700  | 7.3882        |
+| 1.3939 | 99800  | 7.3833        |
+| 1.3953 | 99900  | 7.3217        |
+| 1.3967 | 100000 | 7.3858        |
+| 1.3981 | 100100 | 7.3856        |
+| 1.3995 | 100200 | 7.3692        |
+| 1.4009 | 100300 | 7.3858        |
+| 1.4023 | 100400 | 7.387         |
+| 1.4037 | 100500 | 7.3794        |
+| 1.4051 | 100600 | 7.3653        |
+| 1.4065 | 100700 | 7.3718        |
+| 1.4079 | 100800 | 7.3826        |
+| 1.4093 | 100900 | 7.3233        |
+| 1.4107 | 101000 | 7.3859        |
+| 1.4121 | 101100 | 7.3866        |
+| 1.4135 | 101200 | 7.3367        |
+| 1.4149 | 101300 | 7.3274        |
+| 1.4163 | 101400 | 7.3774        |
+| 1.4177 | 101500 | 7.3804        |
+| 1.4191 | 101600 | 7.3555        |
+| 1.4205 | 101700 | 7.3781        |
+| 1.4219 | 101800 | 7.3523        |
+| 1.4233 | 101900 | 7.3183        |
+| 1.4247 | 102000 | 7.3597        |
+| 1.4261 | 102100 | 7.431         |
+| 1.4275 | 102200 | 7.3519        |
+| 1.4289 | 102300 | 7.3591        |
+| 1.4302 | 102400 | 7.3533        |
+| 1.4316 | 102500 | 7.3955        |
+| 1.4330 | 102600 | 7.3829        |
+| 1.4344 | 102700 | 7.3542        |
+| 1.4358 | 102800 | 7.3404        |
+| 1.4372 | 102900 | 7.3746        |
+| 1.4386 | 103000 | 7.3924        |
+| 1.4400 | 103100 | 7.3267        |
+| 1.4414 | 103200 | 7.3522        |
+| 1.4428 | 103300 | 7.3496        |
+| 1.4442 | 103400 | 7.3668        |
+| 1.4456 | 103500 | 7.3394        |
+| 1.4470 | 103600 | 7.3758        |
+| 1.4484 | 103700 | 7.3537        |
+| 1.4498 | 103800 | 7.3593        |
+| 1.4512 | 103900 | 7.3289        |
+| 1.4526 | 104000 | 7.3565        |
+| 1.4540 | 104100 | 7.3765        |
+| 1.4554 | 104200 | 7.3392        |
+| 1.4568 | 104300 | 7.3714        |
+| 1.4582 | 104400 | 7.3845        |
+| 1.4596 | 104500 | 7.3639        |
+| 1.4610 | 104600 | 7.3707        |
+| 1.4624 | 104700 | 7.3687        |
+| 1.4638 | 104800 | 7.3566        |
+| 1.4652 | 104900 | 7.4302        |
+| 1.4666 | 105000 | 7.3969        |
+| 1.4680 | 105100 | 7.4001        |
+| 1.4694 | 105200 | 7.3543        |
+| 1.4708 | 105300 | 7.4355        |
+| 1.4721 | 105400 | 7.3844        |
+| 1.4735 | 105500 | 7.3793        |
+| 1.4749 | 105600 | 7.3478        |
+| 1.4763 | 105700 | 7.307         |
+| 1.4777 | 105800 | 7.3224        |
+| 1.4791 | 105900 | 7.3461        |
+| 1.4805 | 106000 | 7.3312        |
+| 1.4819 | 106100 | 7.3633        |
+| 1.4833 | 106200 | 7.3941        |
+| 1.4847 | 106300 | 7.3192        |
+| 1.4861 | 106400 | 7.3662        |
+| 1.4875 | 106500 | 7.3193        |
+| 1.4889 | 106600 | 7.4143        |
+| 1.4903 | 106700 | 7.3118        |
+| 1.4917 | 106800 | 7.3539        |
+| 1.4931 | 106900 | 7.3503        |
+| 1.4945 | 107000 | 7.4115        |
+| 1.4959 | 107100 | 7.3226        |
+| 1.4973 | 107200 | 7.3466        |
+| 1.4987 | 107300 | 7.3552        |
+| 1.5001 | 107400 | 7.3934        |
+| 1.5015 | 107500 | 7.3568        |
+| 1.5029 | 107600 | 7.3349        |
+| 1.5043 | 107700 | 7.3725        |
+| 1.5057 | 107800 | 7.366         |
+| 1.5071 | 107900 | 7.4261        |
+| 1.5085 | 108000 | 7.3676        |
+| 1.5099 | 108100 | 7.3846        |
+| 1.5113 | 108200 | 7.3198        |
+| 1.5127 | 108300 | 7.4015        |
+| 1.5141 | 108400 | 7.3463        |
+| 1.5154 | 108500 | 7.3471        |
+| 1.5168 | 108600 | 7.3487        |
+| 1.5182 | 108700 | 7.3852        |
+| 1.5196 | 108800 | 7.4031        |
+| 1.5210 | 108900 | 7.3399        |
+| 1.5224 | 109000 | 7.4266        |
+| 1.5238 | 109100 | 7.3765        |
+| 1.5252 | 109200 | 7.3603        |
+| 1.5266 | 109300 | 7.3121        |
+| 1.5280 | 109400 | 7.3581        |
+| 1.5294 | 109500 | 7.3258        |
+| 1.5308 | 109600 | 7.3347        |
+| 1.5322 | 109700 | 7.3783        |
+| 1.5336 | 109800 | 7.3532        |
+| 1.5350 | 109900 | 7.3507        |
+| 1.5364 | 110000 | 7.3322        |
+| 1.5378 | 110100 | 7.3451        |
+| 1.5392 | 110200 | 7.3739        |
+| 1.5406 | 110300 | 7.3358        |
+| 1.5420 | 110400 | 7.338         |
+| 1.5434 | 110500 | 7.314         |
+| 1.5448 | 110600 | 7.3417        |
+| 1.5462 | 110700 | 7.322         |
+| 1.5476 | 110800 | 7.3636        |
+| 1.5490 | 110900 | 7.3447        |
+| 1.5504 | 111000 | 7.3611        |
+| 1.5518 | 111100 | 7.3661        |
+| 1.5532 | 111200 | 7.3952        |
+| 1.5546 | 111300 | 7.3853        |
+| 1.5560 | 111400 | 7.3561        |
+| 1.5573 | 111500 | 7.3609        |
+| 1.5587 | 111600 | 7.3485        |
+| 1.5601 | 111700 | 7.3367        |
+| 1.5615 | 111800 | 7.3127        |
+| 1.5629 | 111900 | 7.3783        |
+| 1.5643 | 112000 | 7.3195        |
+| 1.5657 | 112100 | 7.399         |
+| 1.5671 | 112200 | 7.3744        |
+| 1.5685 | 112300 | 7.351         |
+| 1.5699 | 112400 | 7.3392        |
+| 1.5713 | 112500 | 7.3751        |
+| 1.5727 | 112600 | 7.3669        |
+| 1.5741 | 112700 | 7.3052        |
+| 1.5755 | 112800 | 7.2865        |
+| 1.5769 | 112900 | 7.2755        |
+| 1.5783 | 113000 | 7.3691        |
+| 1.5797 | 113100 | 7.3375        |
+| 1.5811 | 113200 | 7.3688        |
+| 1.5825 | 113300 | 7.2989        |
+| 1.5839 | 113400 | 7.4042        |
+| 1.5853 | 113500 | 7.3364        |
+| 1.5867 | 113600 | 7.3106        |
+| 1.5881 | 113700 | 7.3385        |
+| 1.5895 | 113800 | 7.4001        |
+| 1.5909 | 113900 | 7.3243        |
+| 1.5923 | 114000 | 7.3427        |
+| 1.5937 | 114100 | 7.3646        |
+| 1.5951 | 114200 | 7.3357        |
+| 1.5965 | 114300 | 7.3302        |
+| 1.5979 | 114400 | 7.3074        |
+| 1.5993 | 114500 | 7.3873        |
+| 1.6006 | 114600 | 7.3812        |
+| 1.6020 | 114700 | 7.3475        |
+| 1.6034 | 114800 | 7.3356        |
+| 1.6048 | 114900 | 7.3408        |
+| 1.6062 | 115000 | 7.3533        |
+| 1.6076 | 115100 | 7.3956        |
+| 1.6090 | 115200 | 7.3647        |
+| 1.6104 | 115300 | 7.3375        |
+| 1.6118 | 115400 | 7.2829        |
+| 1.6132 | 115500 | 7.3433        |
+| 1.6146 | 115600 | 7.4173        |
+| 1.6160 | 115700 | 7.3286        |
+| 1.6174 | 115800 | 7.3041        |
+| 1.6188 | 115900 | 7.3752        |
+| 1.6202 | 116000 | 7.3558        |
+| 1.6216 | 116100 | 7.3218        |
+| 1.6230 | 116200 | 7.3603        |
+| 1.6244 | 116300 | 7.3036        |
+| 1.6258 | 116400 | 7.3836        |
+| 1.6272 | 116500 | 7.3017        |
+| 1.6286 | 116600 | 7.3106        |
+| 1.6300 | 116700 | 7.3752        |
+| 1.6314 | 116800 | 7.3414        |
+| 1.6328 | 116900 | 7.391         |
+| 1.6342 | 117000 | 7.3658        |
+| 1.6356 | 117100 | 7.3149        |
+| 1.6370 | 117200 | 7.3572        |
+| 1.6384 | 117300 | 7.325         |
+| 1.6398 | 117400 | 7.3126        |
+| 1.6412 | 117500 | 7.3453        |
+| 1.6425 | 117600 | 7.3882        |
+| 1.6439 | 117700 | 7.3486        |
+| 1.6453 | 117800 | 7.3454        |
+| 1.6467 | 117900 | 7.3751        |
+| 1.6481 | 118000 | 7.3227        |
+| 1.6495 | 118100 | 7.3157        |
+| 1.6509 | 118200 | 7.3357        |
+| 1.6523 | 118300 | 7.3274        |
+| 1.6537 | 118400 | 7.3359        |
+| 1.6551 | 118500 | 7.3727        |
+| 1.6565 | 118600 | 7.2998        |
+| 1.6579 | 118700 | 7.3407        |
+| 1.6593 | 118800 | 7.3188        |
+| 1.6607 | 118900 | 7.3848        |
+| 1.6621 | 119000 | 7.3266        |
+| 1.6635 | 119100 | 7.3251        |
+| 1.6649 | 119200 | 7.3311        |
+| 1.6663 | 119300 | 7.3371        |
+| 1.6677 | 119400 | 7.3379        |
+| 1.6691 | 119500 | 7.3376        |
+| 1.6705 | 119600 | 7.3188        |
+| 1.6719 | 119700 | 7.3207        |
+| 1.6733 | 119800 | 7.3887        |
+| 1.6747 | 119900 | 7.3701        |
+| 1.6761 | 120000 | 7.3286        |
+| 1.6775 | 120100 | 7.342         |
+| 1.6789 | 120200 | 7.3573        |
+| 1.6803 | 120300 | 7.3197        |
+| 1.6817 | 120400 | 7.2984        |
+| 1.6831 | 120500 | 7.2911        |
+| 1.6845 | 120600 | 7.3144        |
+| 1.6858 | 120700 | 7.3535        |
+| 1.6872 | 120800 | 7.339         |
+| 1.6886 | 120900 | 7.358         |
+| 1.6900 | 121000 | 7.3328        |
+| 1.6914 | 121100 | 7.3226        |
+| 1.6928 | 121200 | 7.3113        |
+| 1.6942 | 121300 | 7.326         |
+| 1.6956 | 121400 | 7.3151        |
+| 1.6970 | 121500 | 7.3797        |
+| 1.6984 | 121600 | 7.3192        |
+| 1.6998 | 121700 | 7.3442        |
+| 1.7012 | 121800 | 7.3632        |
+| 1.7026 | 121900 | 7.2886        |
+| 1.7040 | 122000 | 7.3824        |
+| 1.7054 | 122100 | 7.3122        |
+| 1.7068 | 122200 | 7.3378        |
+| 1.7082 | 122300 | 7.3721        |
+| 1.7096 | 122400 | 7.2905        |
+| 1.7110 | 122500 | 7.3409        |
+| 1.7124 | 122600 | 7.3362        |
+| 1.7138 | 122700 | 7.3346        |
+| 1.7152 | 122800 | 7.323         |
+| 1.7166 | 122900 | 7.3928        |
+| 1.7180 | 123000 | 7.2963        |
+| 1.7194 | 123100 | 7.3591        |
+| 1.7208 | 123200 | 7.3338        |
+| 1.7222 | 123300 | 7.3551        |
+| 1.7236 | 123400 | 7.2956        |
+| 1.7250 | 123500 | 7.3236        |
+| 1.7264 | 123600 | 7.2921        |
+| 1.7278 | 123700 | 7.3118        |
+| 1.7291 | 123800 | 7.4146        |
+| 1.7305 | 123900 | 7.3321        |
+| 1.7319 | 124000 | 7.3529        |
+| 1.7333 | 124100 | 7.3409        |
+| 1.7347 | 124200 | 7.3837        |
+| 1.7361 | 124300 | 7.3289        |
+| 1.7375 | 124400 | 7.3522        |
+| 1.7389 | 124500 | 7.3246        |
+| 1.7403 | 124600 | 7.3455        |
+| 1.7417 | 124700 | 7.3232        |
+| 1.7431 | 124800 | 7.3793        |
+| 1.7445 | 124900 | 7.2792        |
+| 1.7459 | 125000 | 7.3081        |
+| 1.7473 | 125100 | 7.3441        |
+| 1.7487 | 125200 | 7.3586        |
+| 1.7501 | 125300 | 7.3084        |
+| 1.7515 | 125400 | 7.3783        |
+| 1.7529 | 125500 | 7.303         |
+| 1.7543 | 125600 | 7.3268        |
+| 1.7557 | 125700 | 7.3372        |
+| 1.7571 | 125800 | 7.3209        |
+| 1.7585 | 125900 | 7.2818        |
+| 1.7599 | 126000 | 7.3103        |
+| 1.7613 | 126100 | 7.3891        |
+| 1.7627 | 126200 | 7.3761        |
+| 1.7641 | 126300 | 7.3608        |
+| 1.7655 | 126400 | 7.3872        |
+| 1.7669 | 126500 | 7.3302        |
+| 1.7683 | 126600 | 7.3309        |
+| 1.7697 | 126700 | 7.3192        |
+| 1.7710 | 126800 | 7.3174        |
+| 1.7724 | 126900 | 7.3535        |
+| 1.7738 | 127000 | 7.2949        |
+| 1.7752 | 127100 | 7.2946        |
+| 1.7766 | 127200 | 7.3564        |
+| 1.7780 | 127300 | 7.3119        |
+| 1.7794 | 127400 | 7.3449        |
+| 1.7808 | 127500 | 7.3312        |
+| 1.7822 | 127600 | 7.2877        |
+| 1.7836 | 127700 | 7.3446        |
+| 1.7850 | 127800 | 7.3816        |
+| 1.7864 | 127900 | 7.2985        |
+| 1.7878 | 128000 | 7.333         |
+| 1.7892 | 128100 | 7.3868        |
+| 1.7906 | 128200 | 7.3086        |
+| 1.7920 | 128300 | 7.3502        |
+| 1.7934 | 128400 | 7.321         |
+| 1.7948 | 128500 | 7.2932        |
+| 1.7962 | 128600 | 7.3494        |
+| 1.7976 | 128700 | 7.3729        |
+| 1.7990 | 128800 | 7.3541        |
+| 1.8004 | 128900 | 7.3401        |
+| 1.8018 | 129000 | 7.3247        |
+| 1.8032 | 129100 | 7.3194        |
+| 1.8046 | 129200 | 7.3274        |
+| 1.8060 | 129300 | 7.3815        |
+| 1.8074 | 129400 | 7.3108        |
+| 1.8088 | 129500 | 7.375         |
+| 1.8102 | 129600 | 7.3132        |
+| 1.8116 | 129700 | 7.3787        |
+| 1.8130 | 129800 | 7.2769        |
+| 1.8143 | 129900 | 7.2828        |
+| 1.8157 | 130000 | 7.2993        |
+| 1.8171 | 130100 | 7.3093        |
+| 1.8185 | 130200 | 7.3386        |
+| 1.8199 | 130300 | 7.2818        |
+| 1.8213 | 130400 | 7.3224        |
+| 1.8227 | 130500 | 7.286         |
+| 1.8241 | 130600 | 7.2744        |
+| 1.8255 | 130700 | 7.3759        |
+| 1.8269 | 130800 | 7.3489        |
+| 1.8283 | 130900 | 7.3509        |
+| 1.8297 | 131000 | 7.2824        |
+| 1.8311 | 131100 | 7.3319        |
+| 1.8325 | 131200 | 7.3786        |
+| 1.8339 | 131300 | 7.3119        |
+| 1.8353 | 131400 | 7.3332        |
+| 1.8367 | 131500 | 7.3027        |
+| 1.8381 | 131600 | 7.4188        |
+| 1.8395 | 131700 | 7.3888        |
+| 1.8409 | 131800 | 7.3368        |
+| 1.8423 | 131900 | 7.3144        |
+| 1.8437 | 132000 | 7.3694        |
+| 1.8451 | 132100 | 7.347         |
+| 1.8465 | 132200 | 7.3107        |
+| 1.8479 | 132300 | 7.3205        |
+| 1.8493 | 132400 | 7.3379        |
+| 1.8507 | 132500 | 7.311         |
+| 1.8521 | 132600 | 7.3608        |
+| 1.8535 | 132700 | 7.3318        |
+| 1.8549 | 132800 | 7.3338        |
+| 1.8562 | 132900 | 7.3536        |
+| 1.8576 | 133000 | 7.3381        |
+| 1.8590 | 133100 | 7.2922        |
+| 1.8604 | 133200 | 7.3138        |
+| 1.8618 | 133300 | 7.3381        |
+| 1.8632 | 133400 | 7.3565        |
+| 1.8646 | 133500 | 7.3038        |
+| 1.8660 | 133600 | 7.2952        |
+| 1.8674 | 133700 | 7.3158        |
+| 1.8688 | 133800 | 7.3419        |
+| 1.8702 | 133900 | 7.3849        |
+| 1.8716 | 134000 | 7.3149        |
+| 1.8730 | 134100 | 7.2974        |
+| 1.8744 | 134200 | 7.3267        |
+| 1.8758 | 134300 | 7.3147        |
+| 1.8772 | 134400 | 7.3384        |
+| 1.8786 | 134500 | 7.3188        |
+| 1.8800 | 134600 | 7.3351        |
+| 1.8814 | 134700 | 7.3219        |
+| 1.8828 | 134800 | 7.3039        |
+| 1.8842 | 134900 | 7.3666        |
+| 1.8856 | 135000 | 7.3352        |
+| 1.8870 | 135100 | 7.4204        |
+| 1.8884 | 135200 | 7.3394        |
+| 1.8898 | 135300 | 7.3418        |
+| 1.8912 | 135400 | 7.3613        |
+| 1.8926 | 135500 | 7.3244        |
+| 1.8940 | 135600 | 7.358         |
+| 1.8954 | 135700 | 7.2659        |
+| 1.8968 | 135800 | 7.3035        |
+| 1.8982 | 135900 | 7.3833        |
+| 1.8995 | 136000 | 7.3343        |
+| 1.9009 | 136100 | 7.2412        |
+| 1.9023 | 136200 | 7.3243        |
+| 1.9037 | 136300 | 7.2633        |
+| 1.9051 | 136400 | 7.354         |
+| 1.9065 | 136500 | 7.3549        |
+| 1.9079 | 136600 | 7.3155        |
+| 1.9093 | 136700 | 7.329         |
+| 1.9107 | 136800 | 7.3524        |
+| 1.9121 | 136900 | 7.2965        |
+| 1.9135 | 137000 | 7.3498        |
+| 1.9149 | 137100 | 7.3346        |
+| 1.9163 | 137200 | 7.3336        |
+| 1.9177 | 137300 | 7.3202        |
+| 1.9191 | 137400 | 7.3205        |
+| 1.9205 | 137500 | 7.3243        |
+| 1.9219 | 137600 | 7.2771        |
+| 1.9233 | 137700 | 7.3517        |
+| 1.9247 | 137800 | 7.3368        |
+| 1.9261 | 137900 | 7.3492        |
+| 1.9275 | 138000 | 7.346         |
+| 1.9289 | 138100 | 7.2797        |
+| 1.9303 | 138200 | 7.3126        |
+| 1.9317 | 138300 | 7.3935        |
+| 1.9331 | 138400 | 7.3713        |
+| 1.9345 | 138500 | 7.3447        |
+| 1.9359 | 138600 | 7.305         |
+| 1.9373 | 138700 | 7.3616        |
+| 1.9387 | 138800 | 7.3254        |
+| 1.9401 | 138900 | 7.3129        |
+| 1.9414 | 139000 | 7.3376        |
+| 1.9428 | 139100 | 7.2986        |
+| 1.9442 | 139200 | 7.2991        |
+| 1.9456 | 139300 | 7.3564        |
+| 1.9470 | 139400 | 7.3366        |
+| 1.9484 | 139500 | 7.367         |
+| 1.9498 | 139600 | 7.3233        |
+| 1.9512 | 139700 | 7.3219        |
+| 1.9526 | 139800 | 7.3252        |
+| 1.9540 | 139900 | 7.2752        |
+| 1.9554 | 140000 | 7.3207        |
+| 1.9568 | 140100 | 7.3204        |
+| 1.9582 | 140200 | 7.2825        |
+| 1.9596 | 140300 | 7.2894        |
+| 1.9610 | 140400 | 7.3105        |
+| 1.9624 | 140500 | 7.3736        |
+| 1.9638 | 140600 | 7.3018        |
+| 1.9652 | 140700 | 7.3203        |
+| 1.9666 | 140800 | 7.3091        |
+| 1.9680 | 140900 | 7.407         |
+| 1.9694 | 141000 | 7.3363        |
+| 1.9708 | 141100 | 7.4118        |
+| 1.9722 | 141200 | 7.35          |
+| 1.9736 | 141300 | 7.3229        |
+| 1.9750 | 141400 | 7.3778        |
+| 1.9764 | 141500 | 7.3308        |
+| 1.9778 | 141600 | 7.3342        |
+| 1.9792 | 141700 | 7.3161        |
+| 1.9806 | 141800 | 7.3345        |
+| 1.9820 | 141900 | 7.3556        |
+| 1.9834 | 142000 | 7.3832        |
+| 1.9847 | 142100 | 7.3374        |
+| 1.9861 | 142200 | 7.3003        |
+| 1.9875 | 142300 | 7.3323        |
+| 1.9889 | 142400 | 7.3312        |
+| 1.9903 | 142500 | 7.3292        |
+| 1.9917 | 142600 | 7.3439        |
+| 1.9931 | 142700 | 7.3177        |
+| 1.9945 | 142800 | 7.3478        |
+| 1.9959 | 142900 | 7.3035        |
+| 1.9973 | 143000 | 7.3           |
+| 1.9987 | 143100 | 7.3019        |
+
+</details>
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.1.0
+- Transformers: 4.55.4
+- PyTorch: 2.5.1+cu121
+- 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": "2.5.1+cu121"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine",
+  "model_type": "SentenceTransformer"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:250f9ce54c3054f8e3f1fba35f120f8ef84f7761b7f4c13000e6eee2d2e6c65f
+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]"
+}