Multi-return recall-optimized

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "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

@@ -0,0 +1,1770 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:73579
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: Tell me about gaining control for Gauff
+  sentences:
+  - how many winners?
+  - Show me gaining control
+  - Tell me about gaining momentum for Gauff
+- source_sentence: won for the player?
+  sentences:
+  - Tell me about 2026 stats for Gauff
+  - how many titles?
+  - how many winners?
+- source_sentence: Shelton head to head?
+  sentences:
+  - how many winners?
+  - How is Shelton's draw?
+  - Gauff venue name?
+- source_sentence: What is the return this set for Gauff?
+  sentences:
+  - momentum shift?
+  - Tell me about tournament round for Sinner
+  - key factors?
+- source_sentence: What is the overall return for Sinner?
+  sentences:
+  - Djokovic how many titles?
+  - Tell me about sets won for Sinner
+  - career data
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 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("GozdeA/tennis-multi-return-categorizer-v1")
+# Run inference
+sentences = [
+    'What is the overall return for Sinner?',
+    'Djokovic how many titles?',
+    'Tell me about sets won for Sinner',
+]
+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, 0.5331, 0.0219],
+#         [0.5331, 1.0000, 0.1391],
+#         [0.0219, 0.1391, 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
+
+#### Unnamed Dataset
+
+* Size: 73,579 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                         | negative                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | string                                                                           |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 10.03 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.91 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.87 tokens</li><li>max: 22 tokens</li></ul> |
+* Samples:
+  | anchor                                                     | positive                           | negative                                |
+  |:-----------------------------------------------------------|:-----------------------------------|:----------------------------------------|
+  | <code>What is the start time for Swiatek?</code>           | <code>Djokovic what court?</code>  | <code>before the matchup</code>         |
+  | <code>What is the backhand this set for the player?</code> | <code>Djokovic key factors?</code> | <code>What about she's duration?</code> |
+  | <code>the player how many titles?</code>                   | <code>Show me career titles</code> | <code>What about Sinner's games?</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 18,395 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                         | negative                                                                         |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | string                                                                           |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 9.93 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.89 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.91 tokens</li><li>max: 20 tokens</li></ul> |
+* Samples:
+  | anchor                                            | positive                                         | negative                                    |
+  |:--------------------------------------------------|:-------------------------------------------------|:--------------------------------------------|
+  | <code>How is Sinner's previous?</code>            | <code>what venue</code>                          | <code>What about the player's fault?</code> |
+  | <code>likely for Shelton?</code>                  | <code>likely for Nole?</code>                    | <code>title for Shelton?</code>             |
+  | <code>What is the who is she for Djokovic?</code> | <code>What's the who is she for Djokovic?</code> | <code>she who is projected?</code>          |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 15
+- `warmup_ratio`: 0.1
+- `fp16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 8
+- `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`: 15
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `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
+- `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}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `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`: no
+- `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`: True
+- `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 |
+|:-------:|:-----:|:-------------:|
+| 0.0109  | 50    | 4.5343        |
+| 0.0217  | 100   | 4.1503        |
+| 0.0326  | 150   | 4.2094        |
+| 0.0435  | 200   | 3.7119        |
+| 0.0544  | 250   | 3.4992        |
+| 0.0652  | 300   | 3.2812        |
+| 0.0761  | 350   | 2.875         |
+| 0.0870  | 400   | 2.6036        |
+| 0.0978  | 450   | 2.3237        |
+| 0.1087  | 500   | 2.0771        |
+| 0.1196  | 550   | 2.0357        |
+| 0.1305  | 600   | 1.9121        |
+| 0.1413  | 650   | 1.722         |
+| 0.1522  | 700   | 1.6555        |
+| 0.1631  | 750   | 1.5444        |
+| 0.1740  | 800   | 1.6782        |
+| 0.1848  | 850   | 1.4761        |
+| 0.1957  | 900   | 1.4483        |
+| 0.2066  | 950   | 1.3928        |
+| 0.2174  | 1000  | 1.3547        |
+| 0.2283  | 1050  | 1.2807        |
+| 0.2392  | 1100  | 1.214         |
+| 0.2501  | 1150  | 1.2233        |
+| 0.2609  | 1200  | 1.1758        |
+| 0.2718  | 1250  | 1.2455        |
+| 0.2827  | 1300  | 1.1887        |
+| 0.2935  | 1350  | 1.0793        |
+| 0.3044  | 1400  | 1.1442        |
+| 0.3153  | 1450  | 1.0647        |
+| 0.3262  | 1500  | 1.127         |
+| 0.3370  | 1550  | 1.0336        |
+| 0.3479  | 1600  | 0.9882        |
+| 0.3588  | 1650  | 1.084         |
+| 0.3696  | 1700  | 0.9635        |
+| 0.3805  | 1750  | 1.0175        |
+| 0.3914  | 1800  | 1.0337        |
+| 0.4023  | 1850  | 0.9214        |
+| 0.4131  | 1900  | 0.8977        |
+| 0.4240  | 1950  | 0.8724        |
+| 0.4349  | 2000  | 0.9128        |
+| 0.4457  | 2050  | 0.8351        |
+| 0.4566  | 2100  | 0.8709        |
+| 0.4675  | 2150  | 0.8714        |
+| 0.4784  | 2200  | 0.8228        |
+| 0.4892  | 2250  | 0.8768        |
+| 0.5001  | 2300  | 0.8204        |
+| 0.5110  | 2350  | 0.7917        |
+| 0.5219  | 2400  | 0.8571        |
+| 0.5327  | 2450  | 0.7727        |
+| 0.5436  | 2500  | 0.7949        |
+| 0.5545  | 2550  | 0.7218        |
+| 0.5653  | 2600  | 0.7796        |
+| 0.5762  | 2650  | 0.7779        |
+| 0.5871  | 2700  | 0.708         |
+| 0.5980  | 2750  | 0.6822        |
+| 0.6088  | 2800  | 0.7267        |
+| 0.6197  | 2850  | 0.7874        |
+| 0.6306  | 2900  | 0.7183        |
+| 0.6414  | 2950  | 0.7872        |
+| 0.6523  | 3000  | 0.6798        |
+| 0.6632  | 3050  | 0.6589        |
+| 0.6741  | 3100  | 0.7869        |
+| 0.6849  | 3150  | 0.7458        |
+| 0.6958  | 3200  | 0.6518        |
+| 0.7067  | 3250  | 0.6666        |
+| 0.7175  | 3300  | 0.7073        |
+| 0.7284  | 3350  | 0.6737        |
+| 0.7393  | 3400  | 0.6933        |
+| 0.7502  | 3450  | 0.6869        |
+| 0.7610  | 3500  | 0.6713        |
+| 0.7719  | 3550  | 0.6525        |
+| 0.7828  | 3600  | 0.6384        |
+| 0.7937  | 3650  | 0.6467        |
+| 0.8045  | 3700  | 0.5862        |
+| 0.8154  | 3750  | 0.5869        |
+| 0.8263  | 3800  | 0.6548        |
+| 0.8371  | 3850  | 0.6605        |
+| 0.8480  | 3900  | 0.639         |
+| 0.8589  | 3950  | 0.5724        |
+| 0.8698  | 4000  | 0.5488        |
+| 0.8806  | 4050  | 0.6698        |
+| 0.8915  | 4100  | 0.6038        |
+| 0.9024  | 4150  | 0.5981        |
+| 0.9132  | 4200  | 0.6082        |
+| 0.9241  | 4250  | 0.6197        |
+| 0.9350  | 4300  | 0.5462        |
+| 0.9459  | 4350  | 0.6771        |
+| 0.9567  | 4400  | 0.5428        |
+| 0.9676  | 4450  | 0.6265        |
+| 0.9785  | 4500  | 0.5621        |
+| 0.9893  | 4550  | 0.5917        |
+| 1.0002  | 4600  | 0.5517        |
+| 1.0111  | 4650  | 0.5733        |
+| 1.0220  | 4700  | 0.5907        |
+| 1.0328  | 4750  | 0.51          |
+| 1.0437  | 4800  | 0.5592        |
+| 1.0546  | 4850  | 0.5688        |
+| 1.0654  | 4900  | 0.5571        |
+| 1.0763  | 4950  | 0.531         |
+| 1.0872  | 5000  | 0.5062        |
+| 1.0981  | 5050  | 0.5626        |
+| 1.1089  | 5100  | 0.55          |
+| 1.1198  | 5150  | 0.5727        |
+| 1.1307  | 5200  | 0.5253        |
+| 1.1416  | 5250  | 0.5174        |
+| 1.1524  | 5300  | 0.5883        |
+| 1.1633  | 5350  | 0.5333        |
+| 1.1742  | 5400  | 0.5204        |
+| 1.1850  | 5450  | 0.4964        |
+| 1.1959  | 5500  | 0.5192        |
+| 1.2068  | 5550  | 0.5264        |
+| 1.2177  | 5600  | 0.5388        |
+| 1.2285  | 5650  | 0.5505        |
+| 1.2394  | 5700  | 0.5008        |
+| 1.2503  | 5750  | 0.4952        |
+| 1.2611  | 5800  | 0.5656        |
+| 1.2720  | 5850  | 0.5574        |
+| 1.2829  | 5900  | 0.4516        |
+| 1.2938  | 5950  | 0.5438        |
+| 1.3046  | 6000  | 0.48          |
+| 1.3155  | 6050  | 0.5645        |
+| 1.3264  | 6100  | 0.5652        |
+| 1.3372  | 6150  | 0.4538        |
+| 1.3481  | 6200  | 0.522         |
+| 1.3590  | 6250  | 0.5153        |
+| 1.3699  | 6300  | 0.5149        |
+| 1.3807  | 6350  | 0.606         |
+| 1.3916  | 6400  | 0.5366        |
+| 1.4025  | 6450  | 0.4846        |
+| 1.4134  | 6500  | 0.5462        |
+| 1.4242  | 6550  | 0.4505        |
+| 1.4351  | 6600  | 0.4648        |
+| 1.4460  | 6650  | 0.5468        |
+| 1.4568  | 6700  | 0.4822        |
+| 1.4677  | 6750  | 0.5271        |
+| 1.4786  | 6800  | 0.5222        |
+| 1.4895  | 6850  | 0.4843        |
+| 1.5003  | 6900  | 0.4755        |
+| 1.5112  | 6950  | 0.5517        |
+| 1.5221  | 7000  | 0.4793        |
+| 1.5329  | 7050  | 0.5232        |
+| 1.5438  | 7100  | 0.5481        |
+| 1.5547  | 7150  | 0.5477        |
+| 1.5656  | 7200  | 0.5007        |
+| 1.5764  | 7250  | 0.4048        |
+| 1.5873  | 7300  | 0.5295        |
+| 1.5982  | 7350  | 0.4564        |
+| 1.6090  | 7400  | 0.5618        |
+| 1.6199  | 7450  | 0.5855        |
+| 1.6308  | 7500  | 0.5319        |
+| 1.6417  | 7550  | 0.5128        |
+| 1.6525  | 7600  | 0.4669        |
+| 1.6634  | 7650  | 0.4961        |
+| 1.6743  | 7700  | 0.4905        |
+| 1.6851  | 7750  | 0.4959        |
+| 1.6960  | 7800  | 0.4981        |
+| 1.7069  | 7850  | 0.4973        |
+| 1.7178  | 7900  | 0.5029        |
+| 1.7286  | 7950  | 0.5397        |
+| 1.7395  | 8000  | 0.4351        |
+| 1.7504  | 8050  | 0.4897        |
+| 1.7613  | 8100  | 0.4901        |
+| 1.7721  | 8150  | 0.501         |
+| 1.7830  | 8200  | 0.4701        |
+| 1.7939  | 8250  | 0.4508        |
+| 1.8047  | 8300  | 0.4612        |
+| 1.8156  | 8350  | 0.5318        |
+| 1.8265  | 8400  | 0.4846        |
+| 1.8374  | 8450  | 0.4965        |
+| 1.8482  | 8500  | 0.4872        |
+| 1.8591  | 8550  | 0.4902        |
+| 1.8700  | 8600  | 0.4552        |
+| 1.8808  | 8650  | 0.4687        |
+| 1.8917  | 8700  | 0.4839        |
+| 1.9026  | 8750  | 0.4549        |
+| 1.9135  | 8800  | 0.445         |
+| 1.9243  | 8850  | 0.436         |
+| 1.9352  | 8900  | 0.4577        |
+| 1.9461  | 8950  | 0.4301        |
+| 1.9569  | 9000  | 0.5138        |
+| 1.9678  | 9050  | 0.5057        |
+| 1.9787  | 9100  | 0.4725        |
+| 1.9896  | 9150  | 0.4283        |
+| 2.0004  | 9200  | 0.4934        |
+| 2.0113  | 9250  | 0.5033        |
+| 2.0222  | 9300  | 0.4393        |
+| 2.0331  | 9350  | 0.451         |
+| 2.0439  | 9400  | 0.439         |
+| 2.0548  | 9450  | 0.4064        |
+| 2.0657  | 9500  | 0.4708        |
+| 2.0765  | 9550  | 0.4132        |
+| 2.0874  | 9600  | 0.4464        |
+| 2.0983  | 9650  | 0.4531        |
+| 2.1092  | 9700  | 0.4429        |
+| 2.1200  | 9750  | 0.4251        |
+| 2.1309  | 9800  | 0.45          |
+| 2.1418  | 9850  | 0.4252        |
+| 2.1526  | 9900  | 0.424         |
+| 2.1635  | 9950  | 0.4899        |
+| 2.1744  | 10000 | 0.4602        |
+| 2.1853  | 10050 | 0.4976        |
+| 2.1961  | 10100 | 0.4161        |
+| 2.2070  | 10150 | 0.4652        |
+| 2.2179  | 10200 | 0.444         |
+| 2.2287  | 10250 | 0.472         |
+| 2.2396  | 10300 | 0.4657        |
+| 2.2505  | 10350 | 0.4483        |
+| 2.2614  | 10400 | 0.5059        |
+| 2.2722  | 10450 | 0.4887        |
+| 2.2831  | 10500 | 0.4583        |
+| 2.2940  | 10550 | 0.4551        |
+| 2.3048  | 10600 | 0.4353        |
+| 2.3157  | 10650 | 0.4883        |
+| 2.3266  | 10700 | 0.4683        |
+| 2.3375  | 10750 | 0.4461        |
+| 2.3483  | 10800 | 0.4323        |
+| 2.3592  | 10850 | 0.4779        |
+| 2.3701  | 10900 | 0.3794        |
+| 2.3810  | 10950 | 0.4247        |
+| 2.3918  | 11000 | 0.4223        |
+| 2.4027  | 11050 | 0.4325        |
+| 2.4136  | 11100 | 0.3852        |
+| 2.4244  | 11150 | 0.4424        |
+| 2.4353  | 11200 | 0.4614        |
+| 2.4462  | 11250 | 0.5371        |
+| 2.4571  | 11300 | 0.4411        |
+| 2.4679  | 11350 | 0.4248        |
+| 2.4788  | 11400 | 0.4675        |
+| 2.4897  | 11450 | 0.4442        |
+| 2.5005  | 11500 | 0.4382        |
+| 2.5114  | 11550 | 0.45          |
+| 2.5223  | 11600 | 0.3965        |
+| 2.5332  | 11650 | 0.4243        |
+| 2.5440  | 11700 | 0.5324        |
+| 2.5549  | 11750 | 0.4558        |
+| 2.5658  | 11800 | 0.4677        |
+| 2.5766  | 11850 | 0.4307        |
+| 2.5875  | 11900 | 0.4344        |
+| 2.5984  | 11950 | 0.4066        |
+| 2.6093  | 12000 | 0.4063        |
+| 2.6201  | 12050 | 0.4823        |
+| 2.6310  | 12100 | 0.4009        |
+| 2.6419  | 12150 | 0.3996        |
+| 2.6528  | 12200 | 0.4401        |
+| 2.6636  | 12250 | 0.4244        |
+| 2.6745  | 12300 | 0.4074        |
+| 2.6854  | 12350 | 0.4391        |
+| 2.6962  | 12400 | 0.4452        |
+| 2.7071  | 12450 | 0.4893        |
+| 2.7180  | 12500 | 0.4644        |
+| 2.7289  | 12550 | 0.4626        |
+| 2.7397  | 12600 | 0.4329        |
+| 2.7506  | 12650 | 0.4706        |
+| 2.7615  | 12700 | 0.4076        |
+| 2.7723  | 12750 | 0.4258        |
+| 2.7832  | 12800 | 0.4746        |
+| 2.7941  | 12850 | 0.4445        |
+| 2.8050  | 12900 | 0.3991        |
+| 2.8158  | 12950 | 0.4463        |
+| 2.8267  | 13000 | 0.5408        |
+| 2.8376  | 13050 | 0.4755        |
+| 2.8484  | 13100 | 0.4352        |
+| 2.8593  | 13150 | 0.4397        |
+| 2.8702  | 13200 | 0.4313        |
+| 2.8811  | 13250 | 0.4292        |
+| 2.8919  | 13300 | 0.4706        |
+| 2.9028  | 13350 | 0.44          |
+| 2.9137  | 13400 | 0.4608        |
+| 2.9245  | 13450 | 0.4115        |
+| 2.9354  | 13500 | 0.4301        |
+| 2.9463  | 13550 | 0.3949        |
+| 2.9572  | 13600 | 0.5413        |
+| 2.9680  | 13650 | 0.4923        |
+| 2.9789  | 13700 | 0.4789        |
+| 2.9898  | 13750 | 0.4517        |
+| 3.0007  | 13800 | 0.4442        |
+| 3.0115  | 13850 | 0.4024        |
+| 3.0224  | 13900 | 0.4693        |
+| 3.0333  | 13950 | 0.3928        |
+| 3.0441  | 14000 | 0.4171        |
+| 3.0550  | 14050 | 0.4563        |
+| 3.0659  | 14100 | 0.4822        |
+| 3.0768  | 14150 | 0.3919        |
+| 3.0876  | 14200 | 0.4311        |
+| 3.0985  | 14250 | 0.4678        |
+| 3.1094  | 14300 | 0.4385        |
+| 3.1202  | 14350 | 0.4603        |
+| 3.1311  | 14400 | 0.3592        |
+| 3.1420  | 14450 | 0.4371        |
+| 3.1529  | 14500 | 0.4543        |
+| 3.1637  | 14550 | 0.4129        |
+| 3.1746  | 14600 | 0.482         |
+| 3.1855  | 14650 | 0.4003        |
+| 3.1963  | 14700 | 0.4369        |
+| 3.2072  | 14750 | 0.4284        |
+| 3.2181  | 14800 | 0.4054        |
+| 3.2290  | 14850 | 0.4646        |
+| 3.2398  | 14900 | 0.4694        |
+| 3.2507  | 14950 | 0.4373        |
+| 3.2616  | 15000 | 0.4242        |
+| 3.2725  | 15050 | 0.3831        |
+| 3.2833  | 15100 | 0.4368        |
+| 3.2942  | 15150 | 0.3969        |
+| 3.3051  | 15200 | 0.4054        |
+| 3.3159  | 15250 | 0.4599        |
+| 3.3268  | 15300 | 0.4339        |
+| 3.3377  | 15350 | 0.4139        |
+| 3.3486  | 15400 | 0.3776        |
+| 3.3594  | 15450 | 0.382         |
+| 3.3703  | 15500 | 0.3721        |
+| 3.3812  | 15550 | 0.4027        |
+| 3.3920  | 15600 | 0.4055        |
+| 3.4029  | 15650 | 0.4425        |
+| 3.4138  | 15700 | 0.4547        |
+| 3.4247  | 15750 | 0.4262        |
+| 3.4355  | 15800 | 0.4254        |
+| 3.4464  | 15850 | 0.4351        |
+| 3.4573  | 15900 | 0.4512        |
+| 3.4681  | 15950 | 0.4176        |
+| 3.4790  | 16000 | 0.4309        |
+| 3.4899  | 16050 | 0.4769        |
+| 3.5008  | 16100 | 0.4066        |
+| 3.5116  | 16150 | 0.4299        |
+| 3.5225  | 16200 | 0.4656        |
+| 3.5334  | 16250 | 0.3952        |
+| 3.5442  | 16300 | 0.4916        |
+| 3.5551  | 16350 | 0.4299        |
+| 3.5660  | 16400 | 0.4113        |
+| 3.5769  | 16450 | 0.3327        |
+| 3.5877  | 16500 | 0.3846        |
+| 3.5986  | 16550 | 0.4026        |
+| 3.6095  | 16600 | 0.4467        |
+| 3.6204  | 16650 | 0.4034        |
+| 3.6312  | 16700 | 0.4372        |
+| 3.6421  | 16750 | 0.3998        |
+| 3.6530  | 16800 | 0.4125        |
+| 3.6638  | 16850 | 0.4402        |
+| 3.6747  | 16900 | 0.4505        |
+| 3.6856  | 16950 | 0.4204        |
+| 3.6965  | 17000 | 0.4321        |
+| 3.7073  | 17050 | 0.4538        |
+| 3.7182  | 17100 | 0.4095        |
+| 3.7291  | 17150 | 0.4361        |
+| 3.7399  | 17200 | 0.3658        |
+| 3.7508  | 17250 | 0.4158        |
+| 3.7617  | 17300 | 0.4394        |
+| 3.7726  | 17350 | 0.4329        |
+| 3.7834  | 17400 | 0.4599        |
+| 3.7943  | 17450 | 0.4091        |
+| 3.8052  | 17500 | 0.404         |
+| 3.8160  | 17550 | 0.4532        |
+| 3.8269  | 17600 | 0.4591        |
+| 3.8378  | 17650 | 0.4178        |
+| 3.8487  | 17700 | 0.4236        |
+| 3.8595  | 17750 | 0.4122        |
+| 3.8704  | 17800 | 0.404         |
+| 3.8813  | 17850 | 0.4057        |
+| 3.8922  | 17900 | 0.4169        |
+| 3.9030  | 17950 | 0.4668        |
+| 3.9139  | 18000 | 0.4186        |
+| 3.9248  | 18050 | 0.3874        |
+| 3.9356  | 18100 | 0.4644        |
+| 3.9465  | 18150 | 0.3788        |
+| 3.9574  | 18200 | 0.4308        |
+| 3.9683  | 18250 | 0.4466        |
+| 3.9791  | 18300 | 0.434         |
+| 3.9900  | 18350 | 0.4317        |
+| 4.0009  | 18400 | 0.3846        |
+| 4.0117  | 18450 | 0.4284        |
+| 4.0226  | 18500 | 0.3853        |
+| 4.0335  | 18550 | 0.4083        |
+| 4.0444  | 18600 | 0.3601        |
+| 4.0552  | 18650 | 0.4309        |
+| 4.0661  | 18700 | 0.4503        |
+| 4.0770  | 18750 | 0.3978        |
+| 4.0878  | 18800 | 0.4455        |
+| 4.0987  | 18850 | 0.4662        |
+| 4.1096  | 18900 | 0.3975        |
+| 4.1205  | 18950 | 0.388         |
+| 4.1313  | 19000 | 0.4246        |
+| 4.1422  | 19050 | 0.3963        |
+| 4.1531  | 19100 | 0.38          |
+| 4.1639  | 19150 | 0.3699        |
+| 4.1748  | 19200 | 0.4176        |
+| 4.1857  | 19250 | 0.4139        |
+| 4.1966  | 19300 | 0.439         |
+| 4.2074  | 19350 | 0.4259        |
+| 4.2183  | 19400 | 0.4135        |
+| 4.2292  | 19450 | 0.4516        |
+| 4.2401  | 19500 | 0.3861        |
+| 4.2509  | 19550 | 0.3929        |
+| 4.2618  | 19600 | 0.3653        |
+| 4.2727  | 19650 | 0.4113        |
+| 4.2835  | 19700 | 0.422         |
+| 4.2944  | 19750 | 0.3864        |
+| 4.3053  | 19800 | 0.4171        |
+| 4.3162  | 19850 | 0.4439        |
+| 4.3270  | 19900 | 0.369         |
+| 4.3379  | 19950 | 0.3967        |
+| 4.3488  | 20000 | 0.423         |
+| 4.3596  | 20050 | 0.402         |
+| 4.3705  | 20100 | 0.4588        |
+| 4.3814  | 20150 | 0.4101        |
+| 4.3923  | 20200 | 0.4198        |
+| 4.4031  | 20250 | 0.3895        |
+| 4.4140  | 20300 | 0.4411        |
+| 4.4249  | 20350 | 0.3582        |
+| 4.4357  | 20400 | 0.4318        |
+| 4.4466  | 20450 | 0.4115        |
+| 4.4575  | 20500 | 0.4088        |
+| 4.4684  | 20550 | 0.4462        |
+| 4.4792  | 20600 | 0.4421        |
+| 4.4901  | 20650 | 0.4228        |
+| 4.5010  | 20700 | 0.4397        |
+| 4.5119  | 20750 | 0.395         |
+| 4.5227  | 20800 | 0.4417        |
+| 4.5336  | 20850 | 0.4457        |
+| 4.5445  | 20900 | 0.4006        |
+| 4.5553  | 20950 | 0.4017        |
+| 4.5662  | 21000 | 0.4101        |
+| 4.5771  | 21050 | 0.4464        |
+| 4.5880  | 21100 | 0.3936        |
+| 4.5988  | 21150 | 0.414         |
+| 4.6097  | 21200 | 0.4519        |
+| 4.6206  | 21250 | 0.3599        |
+| 4.6314  | 21300 | 0.4264        |
+| 4.6423  | 21350 | 0.4284        |
+| 4.6532  | 21400 | 0.3824        |
+| 4.6641  | 21450 | 0.4375        |
+| 4.6749  | 21500 | 0.4304        |
+| 4.6858  | 21550 | 0.3955        |
+| 4.6967  | 21600 | 0.4071        |
+| 4.7075  | 21650 | 0.4033        |
+| 4.7184  | 21700 | 0.401         |
+| 4.7293  | 21750 | 0.4326        |
+| 4.7402  | 21800 | 0.3946        |
+| 4.7510  | 21850 | 0.4203        |
+| 4.7619  | 21900 | 0.4118        |
+| 4.7728  | 21950 | 0.4601        |
+| 4.7836  | 22000 | 0.4075        |
+| 4.7945  | 22050 | 0.387         |
+| 4.8054  | 22100 | 0.4452        |
+| 4.8163  | 22150 | 0.4315        |
+| 4.8271  | 22200 | 0.4326        |
+| 4.8380  | 22250 | 0.3973        |
+| 4.8489  | 22300 | 0.3921        |
+| 4.8598  | 22350 | 0.4193        |
+| 4.8706  | 22400 | 0.4387        |
+| 4.8815  | 22450 | 0.3427        |
+| 4.8924  | 22500 | 0.3798        |
+| 4.9032  | 22550 | 0.4283        |
+| 4.9141  | 22600 | 0.3316        |
+| 4.9250  | 22650 | 0.4236        |
+| 4.9359  | 22700 | 0.3889        |
+| 4.9467  | 22750 | 0.4361        |
+| 4.9576  | 22800 | 0.4042        |
+| 4.9685  | 22850 | 0.4242        |
+| 4.9793  | 22900 | 0.4236        |
+| 4.9902  | 22950 | 0.4107        |
+| 5.0011  | 23000 | 0.4085        |
+| 5.0120  | 23050 | 0.3792        |
+| 5.0228  | 23100 | 0.3613        |
+| 5.0337  | 23150 | 0.3732        |
+| 5.0446  | 23200 | 0.4417        |
+| 5.0554  | 23250 | 0.3981        |
+| 5.0663  | 23300 | 0.3633        |
+| 5.0772  | 23350 | 0.4462        |
+| 5.0881  | 23400 | 0.379         |
+| 5.0989  | 23450 | 0.4407        |
+| 5.1098  | 23500 | 0.4276        |
+| 5.1207  | 23550 | 0.4015        |
+| 5.1316  | 23600 | 0.4077        |
+| 5.1424  | 23650 | 0.3684        |
+| 5.1533  | 23700 | 0.4177        |
+| 5.1642  | 23750 | 0.3799        |
+| 5.1750  | 23800 | 0.368         |
+| 5.1859  | 23850 | 0.3895        |
+| 5.1968  | 23900 | 0.3701        |
+| 5.2077  | 23950 | 0.4023        |
+| 5.2185  | 24000 | 0.4366        |
+| 5.2294  | 24050 | 0.3531        |
+| 5.2403  | 24100 | 0.4698        |
+| 5.2511  | 24150 | 0.4044        |
+| 5.2620  | 24200 | 0.4206        |
+| 5.2729  | 24250 | 0.3634        |
+| 5.2838  | 24300 | 0.4641        |
+| 5.2946  | 24350 | 0.3938        |
+| 5.3055  | 24400 | 0.4068        |
+| 5.3164  | 24450 | 0.3967        |
+| 5.3272  | 24500 | 0.4201        |
+| 5.3381  | 24550 | 0.3853        |
+| 5.3490  | 24600 | 0.4395        |
+| 5.3599  | 24650 | 0.3889        |
+| 5.3707  | 24700 | 0.4131        |
+| 5.3816  | 24750 | 0.394         |
+| 5.3925  | 24800 | 0.3739        |
+| 5.4033  | 24850 | 0.4131        |
+| 5.4142  | 24900 | 0.411         |
+| 5.4251  | 24950 | 0.3676        |
+| 5.4360  | 25000 | 0.4032        |
+| 5.4468  | 25050 | 0.4161        |
+| 5.4577  | 25100 | 0.3662        |
+| 5.4686  | 25150 | 0.4215        |
+| 5.4795  | 25200 | 0.4032        |
+| 5.4903  | 25250 | 0.4417        |
+| 5.5012  | 25300 | 0.4006        |
+| 5.5121  | 25350 | 0.3782        |
+| 5.5229  | 25400 | 0.3671        |
+| 5.5338  | 25450 | 0.4444        |
+| 5.5447  | 25500 | 0.4082        |
+| 5.5556  | 25550 | 0.4489        |
+| 5.5664  | 25600 | 0.4141        |
+| 5.5773  | 25650 | 0.412         |
+| 5.5882  | 25700 | 0.3835        |
+| 5.5990  | 25750 | 0.3717        |
+| 5.6099  | 25800 | 0.4346        |
+| 5.6208  | 25850 | 0.425         |
+| 5.6317  | 25900 | 0.3928        |
+| 5.6425  | 25950 | 0.397         |
+| 5.6534  | 26000 | 0.4092        |
+| 5.6643  | 26050 | 0.4099        |
+| 5.6751  | 26100 | 0.4541        |
+| 5.6860  | 26150 | 0.3871        |
+| 5.6969  | 26200 | 0.4233        |
+| 5.7078  | 26250 | 0.3828        |
+| 5.7186  | 26300 | 0.3768        |
+| 5.7295  | 26350 | 0.3651        |
+| 5.7404  | 26400 | 0.4069        |
+| 5.7513  | 26450 | 0.345         |
+| 5.7621  | 26500 | 0.4104        |
+| 5.7730  | 26550 | 0.4277        |
+| 5.7839  | 26600 | 0.376         |
+| 5.7947  | 26650 | 0.3807        |
+| 5.8056  | 26700 | 0.4101        |
+| 5.8165  | 26750 | 0.4363        |
+| 5.8274  | 26800 | 0.4297        |
+| 5.8382  | 26850 | 0.4474        |
+| 5.8491  | 26900 | 0.4342        |
+| 5.8600  | 26950 | 0.4105        |
+| 5.8708  | 27000 | 0.3463        |
+| 5.8817  | 27050 | 0.4649        |
+| 5.8926  | 27100 | 0.4394        |
+| 5.9035  | 27150 | 0.4414        |
+| 5.9143  | 27200 | 0.3887        |
+| 5.9252  | 27250 | 0.3368        |
+| 5.9361  | 27300 | 0.4028        |
+| 5.9469  | 27350 | 0.4122        |
+| 5.9578  | 27400 | 0.4111        |
+| 5.9687  | 27450 | 0.4525        |
+| 5.9796  | 27500 | 0.3339        |
+| 5.9904  | 27550 | 0.3963        |
+| 6.0013  | 27600 | 0.4151        |
+| 6.0122  | 27650 | 0.3519        |
+| 6.0230  | 27700 | 0.3927        |
+| 6.0339  | 27750 | 0.407         |
+| 6.0448  | 27800 | 0.3792        |
+| 6.0557  | 27850 | 0.4349        |
+| 6.0665  | 27900 | 0.3903        |
+| 6.0774  | 27950 | 0.4265        |
+| 6.0883  | 28000 | 0.4152        |
+| 6.0992  | 28050 | 0.4057        |
+| 6.1100  | 28100 | 0.4566        |
+| 6.1209  | 28150 | 0.3662        |
+| 6.1318  | 28200 | 0.405         |
+| 6.1426  | 28250 | 0.3539        |
+| 6.1535  | 28300 | 0.4315        |
+| 6.1644  | 28350 | 0.3625        |
+| 6.1753  | 28400 | 0.4443        |
+| 6.1861  | 28450 | 0.4326        |
+| 6.1970  | 28500 | 0.3912        |
+| 6.2079  | 28550 | 0.3864        |
+| 6.2187  | 28600 | 0.3573        |
+| 6.2296  | 28650 | 0.324         |
+| 6.2405  | 28700 | 0.3862        |
+| 6.2514  | 28750 | 0.4149        |
+| 6.2622  | 28800 | 0.3508        |
+| 6.2731  | 28850 | 0.4066        |
+| 6.2840  | 28900 | 0.433         |
+| 6.2948  | 28950 | 0.3811        |
+| 6.3057  | 29000 | 0.3503        |
+| 6.3166  | 29050 | 0.468         |
+| 6.3275  | 29100 | 0.3464        |
+| 6.3383  | 29150 | 0.4167        |
+| 6.3492  | 29200 | 0.4133        |
+| 6.3601  | 29250 | 0.431         |
+| 6.3710  | 29300 | 0.4057        |
+| 6.3818  | 29350 | 0.4215        |
+| 6.3927  | 29400 | 0.3867        |
+| 6.4036  | 29450 | 0.4081        |
+| 6.4144  | 29500 | 0.4377        |
+| 6.4253  | 29550 | 0.4169        |
+| 6.4362  | 29600 | 0.3796        |
+| 6.4471  | 29650 | 0.3792        |
+| 6.4579  | 29700 | 0.4182        |
+| 6.4688  | 29750 | 0.403         |
+| 6.4797  | 29800 | 0.4034        |
+| 6.4905  | 29850 | 0.3737        |
+| 6.5014  | 29900 | 0.3986        |
+| 6.5123  | 29950 | 0.3897        |
+| 6.5232  | 30000 | 0.3927        |
+| 6.5340  | 30050 | 0.3798        |
+| 6.5449  | 30100 | 0.4052        |
+| 6.5558  | 30150 | 0.398         |
+| 6.5666  | 30200 | 0.4089        |
+| 6.5775  | 30250 | 0.4213        |
+| 6.5884  | 30300 | 0.3861        |
+| 6.5993  | 30350 | 0.3724        |
+| 6.6101  | 30400 | 0.364         |
+| 6.6210  | 30450 | 0.4101        |
+| 6.6319  | 30500 | 0.3813        |
+| 6.6427  | 30550 | 0.3925        |
+| 6.6536  | 30600 | 0.414         |
+| 6.6645  | 30650 | 0.3712        |
+| 6.6754  | 30700 | 0.3608        |
+| 6.6862  | 30750 | 0.415         |
+| 6.6971  | 30800 | 0.3416        |
+| 6.7080  | 30850 | 0.4023        |
+| 6.7189  | 30900 | 0.3632        |
+| 6.7297  | 30950 | 0.4068        |
+| 6.7406  | 31000 | 0.4181        |
+| 6.7515  | 31050 | 0.3951        |
+| 6.7623  | 31100 | 0.4186        |
+| 6.7732  | 31150 | 0.4233        |
+| 6.7841  | 31200 | 0.4182        |
+| 6.7950  | 31250 | 0.3858        |
+| 6.8058  | 31300 | 0.4656        |
+| 6.8167  | 31350 | 0.3596        |
+| 6.8276  | 31400 | 0.3377        |
+| 6.8384  | 31450 | 0.3345        |
+| 6.8493  | 31500 | 0.3761        |
+| 6.8602  | 31550 | 0.3514        |
+| 6.8711  | 31600 | 0.3907        |
+| 6.8819  | 31650 | 0.3561        |
+| 6.8928  | 31700 | 0.3971        |
+| 6.9037  | 31750 | 0.3883        |
+| 6.9145  | 31800 | 0.441         |
+| 6.9254  | 31850 | 0.4718        |
+| 6.9363  | 31900 | 0.4288        |
+| 6.9472  | 31950 | 0.4641        |
+| 6.9580  | 32000 | 0.4066        |
+| 6.9689  | 32050 | 0.3905        |
+| 6.9798  | 32100 | 0.3728        |
+| 6.9907  | 32150 | 0.3954        |
+| 7.0015  | 32200 | 0.3357        |
+| 7.0124  | 32250 | 0.366         |
+| 7.0233  | 32300 | 0.3696        |
+| 7.0341  | 32350 | 0.4179        |
+| 7.0450  | 32400 | 0.3931        |
+| 7.0559  | 32450 | 0.4164        |
+| 7.0668  | 32500 | 0.4097        |
+| 7.0776  | 32550 | 0.4674        |
+| 7.0885  | 32600 | 0.3893        |
+| 7.0994  | 32650 | 0.3671        |
+| 7.1102  | 32700 | 0.4092        |
+| 7.1211  | 32750 | 0.3574        |
+| 7.1320  | 32800 | 0.3815        |
+| 7.1429  | 32850 | 0.3164        |
+| 7.1537  | 32900 | 0.3772        |
+| 7.1646  | 32950 | 0.4368        |
+| 7.1755  | 33000 | 0.3925        |
+| 7.1863  | 33050 | 0.3766        |
+| 7.1972  | 33100 | 0.4089        |
+| 7.2081  | 33150 | 0.3856        |
+| 7.2190  | 33200 | 0.4306        |
+| 7.2298  | 33250 | 0.4093        |
+| 7.2407  | 33300 | 0.3828        |
+| 7.2516  | 33350 | 0.4299        |
+| 7.2624  | 33400 | 0.4001        |
+| 7.2733  | 33450 | 0.4021        |
+| 7.2842  | 33500 | 0.3392        |
+| 7.2951  | 33550 | 0.4213        |
+| 7.3059  | 33600 | 0.3594        |
+| 7.3168  | 33650 | 0.4092        |
+| 7.3277  | 33700 | 0.3857        |
+| 7.3386  | 33750 | 0.3717        |
+| 7.3494  | 33800 | 0.4132        |
+| 7.3603  | 33850 | 0.3815        |
+| 7.3712  | 33900 | 0.4002        |
+| 7.3820  | 33950 | 0.3883        |
+| 7.3929  | 34000 | 0.4153        |
+| 7.4038  | 34050 | 0.3918        |
+| 7.4147  | 34100 | 0.3487        |
+| 7.4255  | 34150 | 0.3604        |
+| 7.4364  | 34200 | 0.4205        |
+| 7.4473  | 34250 | 0.4095        |
+| 7.4581  | 34300 | 0.3463        |
+| 7.4690  | 34350 | 0.4192        |
+| 7.4799  | 34400 | 0.4076        |
+| 7.4908  | 34450 | 0.3847        |
+| 7.5016  | 34500 | 0.3631        |
+| 7.5125  | 34550 | 0.4154        |
+| 7.5234  | 34600 | 0.4206        |
+| 7.5342  | 34650 | 0.431         |
+| 7.5451  | 34700 | 0.3779        |
+| 7.5560  | 34750 | 0.3597        |
+| 7.5669  | 34800 | 0.363         |
+| 7.5777  | 34850 | 0.3835        |
+| 7.5886  | 34900 | 0.4041        |
+| 7.5995  | 34950 | 0.3574        |
+| 7.6104  | 35000 | 0.3667        |
+| 7.6212  | 35050 | 0.4191        |
+| 7.6321  | 35100 | 0.3391        |
+| 7.6430  | 35150 | 0.3872        |
+| 7.6538  | 35200 | 0.3894        |
+| 7.6647  | 35250 | 0.377         |
+| 7.6756  | 35300 | 0.3397        |
+| 7.6865  | 35350 | 0.3886        |
+| 7.6973  | 35400 | 0.3914        |
+| 7.7082  | 35450 | 0.3828        |
+| 7.7191  | 35500 | 0.3908        |
+| 7.7299  | 35550 | 0.3751        |
+| 7.7408  | 35600 | 0.3977        |
+| 7.7517  | 35650 | 0.3908        |
+| 7.7626  | 35700 | 0.41          |
+| 7.7734  | 35750 | 0.4429        |
+| 7.7843  | 35800 | 0.396         |
+| 7.7952  | 35850 | 0.43          |
+| 7.8060  | 35900 | 0.3857        |
+| 7.8169  | 35950 | 0.4043        |
+| 7.8278  | 36000 | 0.4116        |
+| 7.8387  | 36050 | 0.3566        |
+| 7.8495  | 36100 | 0.3787        |
+| 7.8604  | 36150 | 0.3401        |
+| 7.8713  | 36200 | 0.3484        |
+| 7.8821  | 36250 | 0.3831        |
+| 7.8930  | 36300 | 0.4087        |
+| 7.9039  | 36350 | 0.422         |
+| 7.9148  | 36400 | 0.3823        |
+| 7.9256  | 36450 | 0.4076        |
+| 7.9365  | 36500 | 0.4103        |
+| 7.9474  | 36550 | 0.3523        |
+| 7.9583  | 36600 | 0.3968        |
+| 7.9691  | 36650 | 0.4052        |
+| 7.9800  | 36700 | 0.3673        |
+| 7.9909  | 36750 | 0.4189        |
+| 8.0017  | 36800 | 0.3705        |
+| 8.0126  | 36850 | 0.3544        |
+| 8.0235  | 36900 | 0.3695        |
+| 8.0344  | 36950 | 0.3525        |
+| 8.0452  | 37000 | 0.4156        |
+| 8.0561  | 37050 | 0.3759        |
+| 8.0670  | 37100 | 0.3964        |
+| 8.0778  | 37150 | 0.4138        |
+| 8.0887  | 37200 | 0.36          |
+| 8.0996  | 37250 | 0.362         |
+| 8.1105  | 37300 | 0.3548        |
+| 8.1213  | 37350 | 0.4081        |
+| 8.1322  | 37400 | 0.3777        |
+| 8.1431  | 37450 | 0.4061        |
+| 8.1539  | 37500 | 0.4284        |
+| 8.1648  | 37550 | 0.4064        |
+| 8.1757  | 37600 | 0.343         |
+| 8.1866  | 37650 | 0.4108        |
+| 8.1974  | 37700 | 0.336         |
+| 8.2083  | 37750 | 0.3538        |
+| 8.2192  | 37800 | 0.3657        |
+| 8.2301  | 37850 | 0.3972        |
+| 8.2409  | 37900 | 0.4172        |
+| 8.2518  | 37950 | 0.3773        |
+| 8.2627  | 38000 | 0.3395        |
+| 8.2735  | 38050 | 0.3776        |
+| 8.2844  | 38100 | 0.3982        |
+| 8.2953  | 38150 | 0.3889        |
+| 8.3062  | 38200 | 0.3353        |
+| 8.3170  | 38250 | 0.3916        |
+| 8.3279  | 38300 | 0.3427        |
+| 8.3388  | 38350 | 0.3135        |
+| 8.3496  | 38400 | 0.3703        |
+| 8.3605  | 38450 | 0.4161        |
+| 8.3714  | 38500 | 0.3549        |
+| 8.3823  | 38550 | 0.3947        |
+| 8.3931  | 38600 | 0.4015        |
+| 8.4040  | 38650 | 0.333         |
+| 8.4149  | 38700 | 0.3313        |
+| 8.4257  | 38750 | 0.3608        |
+| 8.4366  | 38800 | 0.3947        |
+| 8.4475  | 38850 | 0.4155        |
+| 8.4584  | 38900 | 0.3938        |
+| 8.4692  | 38950 | 0.3758        |
+| 8.4801  | 39000 | 0.4196        |
+| 8.4910  | 39050 | 0.3235        |
+| 8.5018  | 39100 | 0.3854        |
+| 8.5127  | 39150 | 0.4412        |
+| 8.5236  | 39200 | 0.3849        |
+| 8.5345  | 39250 | 0.3653        |
+| 8.5453  | 39300 | 0.3626        |
+| 8.5562  | 39350 | 0.4181        |
+| 8.5671  | 39400 | 0.3956        |
+| 8.5780  | 39450 | 0.3829        |
+| 8.5888  | 39500 | 0.383         |
+| 8.5997  | 39550 | 0.3333        |
+| 8.6106  | 39600 | 0.3862        |
+| 8.6214  | 39650 | 0.3389        |
+| 8.6323  | 39700 | 0.3775        |
+| 8.6432  | 39750 | 0.4498        |
+| 8.6541  | 39800 | 0.3995        |
+| 8.6649  | 39850 | 0.4221        |
+| 8.6758  | 39900 | 0.34          |
+| 8.6867  | 39950 | 0.3624        |
+| 8.6975  | 40000 | 0.4044        |
+| 8.7084  | 40050 | 0.3707        |
+| 8.7193  | 40100 | 0.3893        |
+| 8.7302  | 40150 | 0.3641        |
+| 8.7410  | 40200 | 0.3615        |
+| 8.7519  | 40250 | 0.3771        |
+| 8.7628  | 40300 | 0.3741        |
+| 8.7736  | 40350 | 0.3714        |
+| 8.7845  | 40400 | 0.3949        |
+| 8.7954  | 40450 | 0.3863        |
+| 8.8063  | 40500 | 0.4038        |
+| 8.8171  | 40550 | 0.3981        |
+| 8.8280  | 40600 | 0.4127        |
+| 8.8389  | 40650 | 0.3975        |
+| 8.8497  | 40700 | 0.4221        |
+| 8.8606  | 40750 | 0.4015        |
+| 8.8715  | 40800 | 0.3867        |
+| 8.8824  | 40850 | 0.4103        |
+| 8.8932  | 40900 | 0.3706        |
+| 8.9041  | 40950 | 0.4163        |
+| 8.9150  | 41000 | 0.4587        |
+| 8.9259  | 41050 | 0.3577        |
+| 8.9367  | 41100 | 0.3935        |
+| 8.9476  | 41150 | 0.3692        |
+| 8.9585  | 41200 | 0.3671        |
+| 8.9693  | 41250 | 0.383         |
+| 8.9802  | 41300 | 0.3916        |
+| 8.9911  | 41350 | 0.3449        |
+| 9.0020  | 41400 | 0.4054        |
+| 9.0128  | 41450 | 0.3806        |
+| 9.0237  | 41500 | 0.4055        |
+| 9.0346  | 41550 | 0.4158        |
+| 9.0454  | 41600 | 0.3617        |
+| 9.0563  | 41650 | 0.3988        |
+| 9.0672  | 41700 | 0.3772        |
+| 9.0781  | 41750 | 0.3613        |
+| 9.0889  | 41800 | 0.3518        |
+| 9.0998  | 41850 | 0.418         |
+| 9.1107  | 41900 | 0.3602        |
+| 9.1215  | 41950 | 0.3609        |
+| 9.1324  | 42000 | 0.3637        |
+| 9.1433  | 42050 | 0.4353        |
+| 9.1542  | 42100 | 0.3632        |
+| 9.1650  | 42150 | 0.3768        |
+| 9.1759  | 42200 | 0.3581        |
+| 9.1868  | 42250 | 0.3985        |
+| 9.1977  | 42300 | 0.4324        |
+| 9.2085  | 42350 | 0.3882        |
+| 9.2194  | 42400 | 0.3738        |
+| 9.2303  | 42450 | 0.3604        |
+| 9.2411  | 42500 | 0.3963        |
+| 9.2520  | 42550 | 0.4378        |
+| 9.2629  | 42600 | 0.3642        |
+| 9.2738  | 42650 | 0.3827        |
+| 9.2846  | 42700 | 0.3595        |
+| 9.2955  | 42750 | 0.3893        |
+| 9.3064  | 42800 | 0.3628        |
+| 9.3172  | 42850 | 0.3544        |
+| 9.3281  | 42900 | 0.357         |
+| 9.3390  | 42950 | 0.3834        |
+| 9.3499  | 43000 | 0.4025        |
+| 9.3607  | 43050 | 0.3697        |
+| 9.3716  | 43100 | 0.3772        |
+| 9.3825  | 43150 | 0.3813        |
+| 9.3933  | 43200 | 0.4251        |
+| 9.4042  | 43250 | 0.3971        |
+| 9.4151  | 43300 | 0.3567        |
+| 9.4260  | 43350 | 0.3724        |
+| 9.4368  | 43400 | 0.3579        |
+| 9.4477  | 43450 | 0.3655        |
+| 9.4586  | 43500 | 0.3995        |
+| 9.4694  | 43550 | 0.3906        |
+| 9.4803  | 43600 | 0.3124        |
+| 9.4912  | 43650 | 0.372         |
+| 9.5021  | 43700 | 0.3589        |
+| 9.5129  | 43750 | 0.3725        |
+| 9.5238  | 43800 | 0.362         |
+| 9.5347  | 43850 | 0.4196        |
+| 9.5456  | 43900 | 0.333         |
+| 9.5564  | 43950 | 0.3905        |
+| 9.5673  | 44000 | 0.4155        |
+| 9.5782  | 44050 | 0.3746        |
+| 9.5890  | 44100 | 0.3773        |
+| 9.5999  | 44150 | 0.3839        |
+| 9.6108  | 44200 | 0.3307        |
+| 9.6217  | 44250 | 0.3672        |
+| 9.6325  | 44300 | 0.3956        |
+| 9.6434  | 44350 | 0.3714        |
+| 9.6543  | 44400 | 0.4217        |
+| 9.6651  | 44450 | 0.4147        |
+| 9.6760  | 44500 | 0.3997        |
+| 9.6869  | 44550 | 0.3748        |
+| 9.6978  | 44600 | 0.3879        |
+| 9.7086  | 44650 | 0.3631        |
+| 9.7195  | 44700 | 0.3216        |
+| 9.7304  | 44750 | 0.4008        |
+| 9.7412  | 44800 | 0.3659        |
+| 9.7521  | 44850 | 0.4471        |
+| 9.7630  | 44900 | 0.3854        |
+| 9.7739  | 44950 | 0.3807        |
+| 9.7847  | 45000 | 0.3801        |
+| 9.7956  | 45050 | 0.3513        |
+| 9.8065  | 45100 | 0.3657        |
+| 9.8174  | 45150 | 0.3398        |
+| 9.8282  | 45200 | 0.3679        |
+| 9.8391  | 45250 | 0.351         |
+| 9.8500  | 45300 | 0.368         |
+| 9.8608  | 45350 | 0.3891        |
+| 9.8717  | 45400 | 0.4348        |
+| 9.8826  | 45450 | 0.4001        |
+| 9.8935  | 45500 | 0.3772        |
+| 9.9043  | 45550 | 0.3866        |
+| 9.9152  | 45600 | 0.3451        |
+| 9.9261  | 45650 | 0.3417        |
+| 9.9369  | 45700 | 0.3835        |
+| 9.9478  | 45750 | 0.361         |
+| 9.9587  | 45800 | 0.3486        |
+| 9.9696  | 45850 | 0.4059        |
+| 9.9804  | 45900 | 0.3914        |
+| 9.9913  | 45950 | 0.3923        |
+| 10.0022 | 46000 | 0.4277        |
+| 10.0130 | 46050 | 0.3718        |
+| 10.0239 | 46100 | 0.3377        |
+| 10.0348 | 46150 | 0.3998        |
+| 10.0457 | 46200 | 0.3424        |
+| 10.0565 | 46250 | 0.3297        |
+| 10.0674 | 46300 | 0.3731        |
+| 10.0783 | 46350 | 0.4425        |
+| 10.0891 | 46400 | 0.3615        |
+| 10.1000 | 46450 | 0.4088        |
+| 10.1109 | 46500 | 0.3561        |
+| 10.1218 | 46550 | 0.3331        |
+| 10.1326 | 46600 | 0.3726        |
+| 10.1435 | 46650 | 0.3645        |
+| 10.1544 | 46700 | 0.3279        |
+| 10.1653 | 46750 | 0.4132        |
+| 10.1761 | 46800 | 0.3835        |
+| 10.1870 | 46850 | 0.3603        |
+| 10.1979 | 46900 | 0.3594        |
+| 10.2087 | 46950 | 0.3732        |
+| 10.2196 | 47000 | 0.37          |
+| 10.2305 | 47050 | 0.3831        |
+| 10.2414 | 47100 | 0.3719        |
+| 10.2522 | 47150 | 0.3735        |
+| 10.2631 | 47200 | 0.3898        |
+| 10.2740 | 47250 | 0.3952        |
+| 10.2848 | 47300 | 0.3408        |
+| 10.2957 | 47350 | 0.3923        |
+| 10.3066 | 47400 | 0.3764        |
+| 10.3175 | 47450 | 0.3919        |
+| 10.3283 | 47500 | 0.3992        |
+| 10.3392 | 47550 | 0.383         |
+| 10.3501 | 47600 | 0.3935        |
+| 10.3609 | 47650 | 0.3732        |
+| 10.3718 | 47700 | 0.366         |
+| 10.3827 | 47750 | 0.3686        |
+| 10.3936 | 47800 | 0.4177        |
+| 10.4044 | 47850 | 0.3399        |
+| 10.4153 | 47900 | 0.411         |
+| 10.4262 | 47950 | 0.3624        |
+| 10.4371 | 48000 | 0.3869        |
+| 10.4479 | 48050 | 0.3715        |
+| 10.4588 | 48100 | 0.3813        |
+| 10.4697 | 48150 | 0.358         |
+| 10.4805 | 48200 | 0.4047        |
+| 10.4914 | 48250 | 0.3632        |
+| 10.5023 | 48300 | 0.372         |
+| 10.5132 | 48350 | 0.362         |
+| 10.5240 | 48400 | 0.3293        |
+| 10.5349 | 48450 | 0.3344        |
+| 10.5458 | 48500 | 0.3246        |
+| 10.5566 | 48550 | 0.3946        |
+| 10.5675 | 48600 | 0.377         |
+| 10.5784 | 48650 | 0.4152        |
+| 10.5893 | 48700 | 0.4255        |
+| 10.6001 | 48750 | 0.379         |
+| 10.6110 | 48800 | 0.3757        |
+| 10.6219 | 48850 | 0.3783        |
+| 10.6327 | 48900 | 0.4167        |
+| 10.6436 | 48950 | 0.382         |
+| 10.6545 | 49000 | 0.3883        |
+| 10.6654 | 49050 | 0.38          |
+| 10.6762 | 49100 | 0.3599        |
+| 10.6871 | 49150 | 0.3579        |
+| 10.6980 | 49200 | 0.4133        |
+| 10.7088 | 49250 | 0.4178        |
+| 10.7197 | 49300 | 0.4253        |
+| 10.7306 | 49350 | 0.3959        |
+| 10.7415 | 49400 | 0.3869        |
+| 10.7523 | 49450 | 0.3852        |
+| 10.7632 | 49500 | 0.3965        |
+| 10.7741 | 49550 | 0.3784        |
+| 10.7850 | 49600 | 0.3926        |
+| 10.7958 | 49650 | 0.3469        |
+| 10.8067 | 49700 | 0.3381        |
+| 10.8176 | 49750 | 0.3771        |
+| 10.8284 | 49800 | 0.4124        |
+| 10.8393 | 49850 | 0.3118        |
+| 10.8502 | 49900 | 0.3945        |
+| 10.8611 | 49950 | 0.4245        |
+| 10.8719 | 50000 | 0.3823        |
+| 10.8828 | 50050 | 0.3773        |
+| 10.8937 | 50100 | 0.4237        |
+| 10.9045 | 50150 | 0.4174        |
+| 10.9154 | 50200 | 0.3625        |
+| 10.9263 | 50250 | 0.4409        |
+| 10.9372 | 50300 | 0.3334        |
+| 10.9480 | 50350 | 0.3782        |
+| 10.9589 | 50400 | 0.3437        |
+| 10.9698 | 50450 | 0.415         |
+| 10.9806 | 50500 | 0.3625        |
+| 10.9915 | 50550 | 0.3544        |
+| 11.0024 | 50600 | 0.384         |
+| 11.0133 | 50650 | 0.3672        |
+| 11.0241 | 50700 | 0.3501        |
+| 11.0350 | 50750 | 0.3299        |
+| 11.0459 | 50800 | 0.3612        |
+| 11.0568 | 50850 | 0.4047        |
+| 11.0676 | 50900 | 0.3628        |
+| 11.0785 | 50950 | 0.3941        |
+| 11.0894 | 51000 | 0.3789        |
+| 11.1002 | 51050 | 0.4089        |
+| 11.1111 | 51100 | 0.3826        |
+| 11.1220 | 51150 | 0.4027        |
+| 11.1329 | 51200 | 0.3819        |
+| 11.1437 | 51250 | 0.3902        |
+| 11.1546 | 51300 | 0.3555        |
+| 11.1655 | 51350 | 0.4149        |
+| 11.1763 | 51400 | 0.3353        |
+| 11.1872 | 51450 | 0.4295        |
+| 11.1981 | 51500 | 0.3294        |
+| 11.2090 | 51550 | 0.3729        |
+| 11.2198 | 51600 | 0.3338        |
+| 11.2307 | 51650 | 0.3769        |
+| 11.2416 | 51700 | 0.3704        |
+| 11.2524 | 51750 | 0.3652        |
+| 11.2633 | 51800 | 0.3768        |
+| 11.2742 | 51850 | 0.4031        |
+| 11.2851 | 51900 | 0.3477        |
+| 11.2959 | 51950 | 0.3586        |
+| 11.3068 | 52000 | 0.3924        |
+| 11.3177 | 52050 | 0.3385        |
+| 11.3285 | 52100 | 0.3939        |
+| 11.3394 | 52150 | 0.3694        |
+| 11.3503 | 52200 | 0.398         |
+| 11.3612 | 52250 | 0.3461        |
+| 11.3720 | 52300 | 0.3812        |
+| 11.3829 | 52350 | 0.3953        |
+| 11.3938 | 52400 | 0.409         |
+| 11.4047 | 52450 | 0.3474        |
+| 11.4155 | 52500 | 0.4009        |
+| 11.4264 | 52550 | 0.3971        |
+| 11.4373 | 52600 | 0.4131        |
+| 11.4481 | 52650 | 0.3711        |
+| 11.4590 | 52700 | 0.3455        |
+| 11.4699 | 52750 | 0.4088        |
+| 11.4808 | 52800 | 0.3561        |
+| 11.4916 | 52850 | 0.3516        |
+| 11.5025 | 52900 | 0.3997        |
+| 11.5134 | 52950 | 0.351         |
+| 11.5242 | 53000 | 0.3563        |
+| 11.5351 | 53050 | 0.3862        |
+| 11.5460 | 53100 | 0.3628        |
+| 11.5569 | 53150 | 0.3957        |
+| 11.5677 | 53200 | 0.3637        |
+| 11.5786 | 53250 | 0.3493        |
+| 11.5895 | 53300 | 0.3208        |
+| 11.6003 | 53350 | 0.3879        |
+| 11.6112 | 53400 | 0.3627        |
+| 11.6221 | 53450 | 0.3721        |
+| 11.6330 | 53500 | 0.3547        |
+| 11.6438 | 53550 | 0.3582        |
+| 11.6547 | 53600 | 0.4228        |
+| 11.6656 | 53650 | 0.4185        |
+| 11.6765 | 53700 | 0.3619        |
+| 11.6873 | 53750 | 0.3966        |
+| 11.6982 | 53800 | 0.3777        |
+| 11.7091 | 53850 | 0.3544        |
+| 11.7199 | 53900 | 0.4293        |
+| 11.7308 | 53950 | 0.3577        |
+| 11.7417 | 54000 | 0.3639        |
+| 11.7526 | 54050 | 0.3358        |
+| 11.7634 | 54100 | 0.3465        |
+| 11.7743 | 54150 | 0.367         |
+| 11.7852 | 54200 | 0.3649        |
+| 11.7960 | 54250 | 0.3897        |
+| 11.8069 | 54300 | 0.4063        |
+| 11.8178 | 54350 | 0.3931        |
+| 11.8287 | 54400 | 0.3686        |
+| 11.8395 | 54450 | 0.3705        |
+| 11.8504 | 54500 | 0.4032        |
+| 11.8613 | 54550 | 0.4081        |
+| 11.8721 | 54600 | 0.3637        |
+| 11.8830 | 54650 | 0.3459        |
+| 11.8939 | 54700 | 0.3964        |
+| 11.9048 | 54750 | 0.3826        |
+| 11.9156 | 54800 | 0.3526        |
+| 11.9265 | 54850 | 0.3946        |
+| 11.9374 | 54900 | 0.432         |
+| 11.9482 | 54950 | 0.3254        |
+| 11.9591 | 55000 | 0.3246        |
+| 11.9700 | 55050 | 0.3744        |
+| 11.9809 | 55100 | 0.3696        |
+| 11.9917 | 55150 | 0.4402        |
+| 12.0026 | 55200 | 0.3494        |
+| 12.0135 | 55250 | 0.4012        |
+| 12.0244 | 55300 | 0.3754        |
+| 12.0352 | 55350 | 0.3325        |
+| 12.0461 | 55400 | 0.3879        |
+| 12.0570 | 55450 | 0.3728        |
+| 12.0678 | 55500 | 0.349         |
+| 12.0787 | 55550 | 0.3733        |
+| 12.0896 | 55600 | 0.3678        |
+| 12.1005 | 55650 | 0.3597        |
+| 12.1113 | 55700 | 0.3777        |
+| 12.1222 | 55750 | 0.3371        |
+| 12.1331 | 55800 | 0.3419        |
+| 12.1439 | 55850 | 0.371         |
+| 12.1548 | 55900 | 0.3638        |
+| 12.1657 | 55950 | 0.3617        |
+| 12.1766 | 56000 | 0.3771        |
+| 12.1874 | 56050 | 0.3327        |
+| 12.1983 | 56100 | 0.3877        |
+| 12.2092 | 56150 | 0.4187        |
+| 12.2200 | 56200 | 0.387         |
+| 12.2309 | 56250 | 0.3868        |
+| 12.2418 | 56300 | 0.4428        |
+| 12.2527 | 56350 | 0.4025        |
+| 12.2635 | 56400 | 0.3249        |
+| 12.2744 | 56450 | 0.3251        |
+| 12.2853 | 56500 | 0.4257        |
+| 12.2962 | 56550 | 0.3933        |
+| 12.3070 | 56600 | 0.3835        |
+| 12.3179 | 56650 | 0.3841        |
+| 12.3288 | 56700 | 0.3717        |
+| 12.3396 | 56750 | 0.4337        |
+| 12.3505 | 56800 | 0.3609        |
+| 12.3614 | 56850 | 0.3734        |
+| 12.3723 | 56900 | 0.3477        |
+| 12.3831 | 56950 | 0.4179        |
+| 12.3940 | 57000 | 0.3765        |
+| 12.4049 | 57050 | 0.3744        |
+| 12.4157 | 57100 | 0.3668        |
+| 12.4266 | 57150 | 0.3708        |
+| 12.4375 | 57200 | 0.3653        |
+| 12.4484 | 57250 | 0.3609        |
+| 12.4592 | 57300 | 0.392         |
+| 12.4701 | 57350 | 0.3679        |
+| 12.4810 | 57400 | 0.3737        |
+| 12.4918 | 57450 | 0.2987        |
+| 12.5027 | 57500 | 0.3862        |
+| 12.5136 | 57550 | 0.3554        |
+| 12.5245 | 57600 | 0.292         |
+| 12.5353 | 57650 | 0.3533        |
+| 12.5462 | 57700 | 0.3358        |
+| 12.5571 | 57750 | 0.4058        |
+| 12.5679 | 57800 | 0.393         |
+| 12.5788 | 57850 | 0.3404        |
+| 12.5897 | 57900 | 0.3831        |
+| 12.6006 | 57950 | 0.3478        |
+| 12.6114 | 58000 | 0.3687        |
+| 12.6223 | 58050 | 0.3986        |
+| 12.6332 | 58100 | 0.3533        |
+| 12.6441 | 58150 | 0.3476        |
+| 12.6549 | 58200 | 0.3532        |
+| 12.6658 | 58250 | 0.4081        |
+| 12.6767 | 58300 | 0.3337        |
+| 12.6875 | 58350 | 0.3084        |
+| 12.6984 | 58400 | 0.4038        |
+| 12.7093 | 58450 | 0.3829        |
+| 12.7202 | 58500 | 0.3839        |
+| 12.7310 | 58550 | 0.3781        |
+| 12.7419 | 58600 | 0.3373        |
+| 12.7528 | 58650 | 0.3853        |
+| 12.7636 | 58700 | 0.416         |
+| 12.7745 | 58750 | 0.3356        |
+| 12.7854 | 58800 | 0.3827        |
+| 12.7963 | 58850 | 0.3714        |
+| 12.8071 | 58900 | 0.3838        |
+| 12.8180 | 58950 | 0.3779        |
+| 12.8289 | 59000 | 0.3802        |
+| 12.8397 | 59050 | 0.3548        |
+| 12.8506 | 59100 | 0.4167        |
+| 12.8615 | 59150 | 0.3471        |
+| 12.8724 | 59200 | 0.3736        |
+| 12.8832 | 59250 | 0.4097        |
+| 12.8941 | 59300 | 0.3666        |
+| 12.9050 | 59350 | 0.3612        |
+| 12.9159 | 59400 | 0.3438        |
+| 12.9267 | 59450 | 0.3263        |
+| 12.9376 | 59500 | 0.3219        |
+| 12.9485 | 59550 | 0.3483        |
+| 12.9593 | 59600 | 0.3787        |
+| 12.9702 | 59650 | 0.337         |
+| 12.9811 | 59700 | 0.3694        |
+| 12.9920 | 59750 | 0.4032        |
+| 13.0028 | 59800 | 0.3801        |
+| 13.0137 | 59850 | 0.3674        |
+| 13.0246 | 59900 | 0.3761        |
+| 13.0354 | 59950 | 0.3629        |
+| 13.0463 | 60000 | 0.3847        |
+| 13.0572 | 60050 | 0.339         |
+| 13.0681 | 60100 | 0.3238        |
+| 13.0789 | 60150 | 0.3351        |
+| 13.0898 | 60200 | 0.3858        |
+| 13.1007 | 60250 | 0.3588        |
+| 13.1115 | 60300 | 0.3727        |
+| 13.1224 | 60350 | 0.3324        |
+| 13.1333 | 60400 | 0.4151        |
+| 13.1442 | 60450 | 0.3637        |
+| 13.1550 | 60500 | 0.3528        |
+| 13.1659 | 60550 | 0.3801        |
+| 13.1768 | 60600 | 0.4035        |
+| 13.1876 | 60650 | 0.3841        |
+| 13.1985 | 60700 | 0.3266        |
+| 13.2094 | 60750 | 0.3713        |
+| 13.2203 | 60800 | 0.4011        |
+| 13.2311 | 60850 | 0.3664        |
+| 13.2420 | 60900 | 0.3387        |
+| 13.2529 | 60950 | 0.3623        |
+| 13.2638 | 61000 | 0.3553        |
+| 13.2746 | 61050 | 0.411         |
+| 13.2855 | 61100 | 0.3595        |
+| 13.2964 | 61150 | 0.3667        |
+| 13.3072 | 61200 | 0.3656        |
+| 13.3181 | 61250 | 0.3535        |
+| 13.3290 | 61300 | 0.3299        |
+| 13.3399 | 61350 | 0.4442        |
+| 13.3507 | 61400 | 0.4031        |
+| 13.3616 | 61450 | 0.3803        |
+| 13.3725 | 61500 | 0.3437        |
+| 13.3833 | 61550 | 0.4238        |
+| 13.3942 | 61600 | 0.3868        |
+| 13.4051 | 61650 | 0.4159        |
+| 13.4160 | 61700 | 0.346         |
+| 13.4268 | 61750 | 0.391         |
+| 13.4377 | 61800 | 0.3447        |
+| 13.4486 | 61850 | 0.3073        |
+| 13.4594 | 61900 | 0.3579        |
+| 13.4703 | 61950 | 0.4034        |
+| 13.4812 | 62000 | 0.3487        |
+| 13.4921 | 62050 | 0.3756        |
+| 13.5029 | 62100 | 0.2851        |
+| 13.5138 | 62150 | 0.3661        |
+| 13.5247 | 62200 | 0.3342        |
+| 13.5356 | 62250 | 0.4146        |
+| 13.5464 | 62300 | 0.3672        |
+| 13.5573 | 62350 | 0.4095        |
+| 13.5682 | 62400 | 0.334         |
+| 13.5790 | 62450 | 0.3679        |
+| 13.5899 | 62500 | 0.322         |
+| 13.6008 | 62550 | 0.3681        |
+| 13.6117 | 62600 | 0.3405        |
+| 13.6225 | 62650 | 0.331         |
+| 13.6334 | 62700 | 0.3709        |
+| 13.6443 | 62750 | 0.3548        |
+| 13.6551 | 62800 | 0.3824        |
+| 13.6660 | 62850 | 0.3772        |
+| 13.6769 | 62900 | 0.4261        |
+| 13.6878 | 62950 | 0.327         |
+| 13.6986 | 63000 | 0.3145        |
+| 13.7095 | 63050 | 0.3729        |
+| 13.7204 | 63100 | 0.3639        |
+| 13.7312 | 63150 | 0.4084        |
+| 13.7421 | 63200 | 0.3707        |
+| 13.7530 | 63250 | 0.3824        |
+| 13.7639 | 63300 | 0.3388        |
+| 13.7747 | 63350 | 0.3527        |
+| 13.7856 | 63400 | 0.3491        |
+| 13.7965 | 63450 | 0.365         |
+| 13.8073 | 63500 | 0.3353        |
+| 13.8182 | 63550 | 0.4076        |
+| 13.8291 | 63600 | 0.3472        |
+| 13.8400 | 63650 | 0.3779        |
+| 13.8508 | 63700 | 0.4188        |
+| 13.8617 | 63750 | 0.4104        |
+| 13.8726 | 63800 | 0.3719        |
+| 13.8835 | 63850 | 0.3705        |
+| 13.8943 | 63900 | 0.3203        |
+| 13.9052 | 63950 | 0.3246        |
+| 13.9161 | 64000 | 0.3578        |
+| 13.9269 | 64050 | 0.3895        |
+| 13.9378 | 64100 | 0.3588        |
+| 13.9487 | 64150 | 0.3739        |
+| 13.9596 | 64200 | 0.382         |
+| 13.9704 | 64250 | 0.3474        |
+| 13.9813 | 64300 | 0.3871        |
+| 13.9922 | 64350 | 0.391         |
+| 14.0030 | 64400 | 0.356         |
+| 14.0139 | 64450 | 0.3723        |
+| 14.0248 | 64500 | 0.3945        |
+| 14.0357 | 64550 | 0.3582        |
+| 14.0465 | 64600 | 0.3855        |
+| 14.0574 | 64650 | 0.4075        |
+| 14.0683 | 64700 | 0.3688        |
+| 14.0791 | 64750 | 0.3688        |
+| 14.0900 | 64800 | 0.3567        |
+| 14.1009 | 64850 | 0.3405        |
+| 14.1118 | 64900 | 0.4093        |
+| 14.1226 | 64950 | 0.2962        |
+| 14.1335 | 65000 | 0.3668        |
+| 14.1444 | 65050 | 0.3383        |
+| 14.1553 | 65100 | 0.3721        |
+| 14.1661 | 65150 | 0.4097        |
+| 14.1770 | 65200 | 0.3547        |
+| 14.1879 | 65250 | 0.3874        |
+| 14.1987 | 65300 | 0.3449        |
+| 14.2096 | 65350 | 0.3188        |
+| 14.2205 | 65400 | 0.3589        |
+| 14.2314 | 65450 | 0.3395        |
+| 14.2422 | 65500 | 0.3993        |
+| 14.2531 | 65550 | 0.3663        |
+| 14.2640 | 65600 | 0.3587        |
+| 14.2748 | 65650 | 0.3087        |
+| 14.2857 | 65700 | 0.3864        |
+| 14.2966 | 65750 | 0.3347        |
+| 14.3075 | 65800 | 0.3429        |
+| 14.3183 | 65850 | 0.3444        |
+| 14.3292 | 65900 | 0.305         |
+| 14.3401 | 65950 | 0.3262        |
+| 14.3509 | 66000 | 0.357         |
+| 14.3618 | 66050 | 0.4113        |
+| 14.3727 | 66100 | 0.3654        |
+| 14.3836 | 66150 | 0.4121        |
+| 14.3944 | 66200 | 0.3675        |
+| 14.4053 | 66250 | 0.3535        |
+| 14.4162 | 66300 | 0.388         |
+| 14.4270 | 66350 | 0.3759        |
+| 14.4379 | 66400 | 0.3682        |
+| 14.4488 | 66450 | 0.3214        |
+| 14.4597 | 66500 | 0.3846        |
+| 14.4705 | 66550 | 0.3921        |
+| 14.4814 | 66600 | 0.3948        |
+| 14.4923 | 66650 | 0.3609        |
+| 14.5032 | 66700 | 0.3709        |
+| 14.5140 | 66750 | 0.4154        |
+| 14.5249 | 66800 | 0.3576        |
+| 14.5358 | 66850 | 0.3397        |
+| 14.5466 | 66900 | 0.3459        |
+| 14.5575 | 66950 | 0.3908        |
+| 14.5684 | 67000 | 0.4457        |
+| 14.5793 | 67050 | 0.3727        |
+| 14.5901 | 67100 | 0.312         |
+| 14.6010 | 67150 | 0.3946        |
+| 14.6119 | 67200 | 0.3626        |
+| 14.6227 | 67250 | 0.3639        |
+| 14.6336 | 67300 | 0.3261        |
+| 14.6445 | 67350 | 0.3779        |
+| 14.6554 | 67400 | 0.393         |
+| 14.6662 | 67450 | 0.3687        |
+| 14.6771 | 67500 | 0.3772        |
+| 14.6880 | 67550 | 0.3954        |
+| 14.6988 | 67600 | 0.3565        |
+| 14.7097 | 67650 | 0.375         |
+| 14.7206 | 67700 | 0.3815        |
+| 14.7315 | 67750 | 0.3516        |
+| 14.7423 | 67800 | 0.4417        |
+| 14.7532 | 67850 | 0.4151        |
+| 14.7641 | 67900 | 0.4171        |
+| 14.7750 | 67950 | 0.3401        |
+| 14.7858 | 68000 | 0.351         |
+| 14.7967 | 68050 | 0.4041        |
+| 14.8076 | 68100 | 0.3528        |
+| 14.8184 | 68150 | 0.3397        |
+| 14.8293 | 68200 | 0.3897        |
+| 14.8402 | 68250 | 0.3917        |
+| 14.8511 | 68300 | 0.3718        |
+| 14.8619 | 68350 | 0.3762        |
+| 14.8728 | 68400 | 0.3391        |
+| 14.8837 | 68450 | 0.3603        |
+| 14.8945 | 68500 | 0.3861        |
+| 14.9054 | 68550 | 0.3658        |
+| 14.9163 | 68600 | 0.3146        |
+| 14.9272 | 68650 | 0.4093        |
+| 14.9380 | 68700 | 0.363         |
+| 14.9489 | 68750 | 0.3835        |
+| 14.9598 | 68800 | 0.3347        |
+| 14.9706 | 68850 | 0.3562        |
+| 14.9815 | 68900 | 0.3594        |
+| 14.9924 | 68950 | 0.3612        |
+
+</details>
+
+### Framework Versions
+- Python: 3.12.12
+- Sentence Transformers: 5.0.0
+- Transformers: 4.57.6
+- PyTorch: 2.10.0+cu128
+- Accelerate: 1.13.0
+- Datasets: 4.0.0
+- Tokenizers: 0.22.2
+
+## 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",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## 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,
+  "dtype": "float32",
+  "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",
+  "transformers_version": "4.57.6",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.0.0",
+    "transformers": "4.57.6",
+    "pytorch": "2.10.0+cu128"
+  },
+  "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 sha256:92512d8b395003673f4ea7ece356fbcca566fdb604aca12c3bb70fe0d73578e4
+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]"
+}