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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:10001819
+- loss:CoSENTLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: Versatile cardigan
+  sentences:
+  - silver bowl
+  - honey
+  - traditional shape glass
+- source_sentence: Adjustments dress
+  sentences:
+  - paraben free Lipstick
+  - heat resistance Bag
+  - Practical candy container
+- source_sentence: Ankle strap for Muay Thai and kickboxing
+  sentences:
+  - cooledged Mug
+  - Kamena
+  - Sleeveless top
+- source_sentence: square toecap ballerina
+  sentences:
+  - FashionNova ripped pullover
+  - Hair Saviors Perfect Match
+  - plastic Measuring Spoons
+- source_sentence: Even applicationLiquid Primer
+  sentences:
+  - Bowl
+  - Satin head rest for car and office
+  - Portable beach stand
+datasets:
+- KhaledReda/pairs_three_scores_v13_description
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# all-MiniLM-L6-v14-pair_score
+
+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) on the [pairs_three_scores_v13_description](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v13_description) 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:** [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:**
+    - [pairs_three_scores_v13_description](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v13_description)
+- **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 = [
+    'Even applicationLiquid Primer',
+    'Portable beach stand',
+    'Bowl',
+]
+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, 0.7720, 0.7641],
+#         [0.7720, 1.0000, 0.9039],
+#         [0.7641, 0.9039, 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_v13_description
+
+* Dataset: [pairs_three_scores_v13_description](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v13_description) at [6fd8086](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v13_description/tree/6fd80866b176f3eb46aa43f8a19130a6f0377619)
+* Size: 10,001,819 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: 5.97 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.17 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 0.13</li><li>mean: 0.25</li><li>max: 0.8</li></ul> |
+* Samples:
+  | sentence1                     | sentence2                             | score             |
+  |:------------------------------|:--------------------------------------|:------------------|
+  | <code>Adult Cat Treats</code> | <code>sweet chili vegan nugget</code> | <code>0.28</code> |
+  | <code>Brick Sweater</code>    | <code>Chestnut Brown hair dye</code>  | <code>0.18</code> |
+  | <code>Sweetal</code>          | <code>PVC tote bag</code>             | <code>0.22</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_v13_description
+
+* Dataset: [pairs_three_scores_v13_description](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v13_description) at [6fd8086](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v13_description/tree/6fd80866b176f3eb46aa43f8a19130a6f0377619)
+* Size: 50,261 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: 5.82 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.47 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 0.14</li><li>mean: 0.25</li><li>max: 0.83</li></ul> |
+* Samples:
+  | sentence1                        | sentence2                         | score             |
+  |:---------------------------------|:----------------------------------|:------------------|
+  | <code>Buff Foundation</code>     | <code>nursing product</code>      | <code>0.27</code> |
+  | <code>Elastic waist Pants</code> | <code>Crisp rim bowl</code>       | <code>0.28</code> |
+  | <code>Appetizers</code>          | <code>comfortable Jumpsuit</code> | <code>0.2</code>  |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0013 | 100   | 11.9475       |
+| 0.0026 | 200   | 11.5542       |
+| 0.0038 | 300   | 11.4709       |
+| 0.0051 | 400   | 11.061        |
+| 0.0064 | 500   | 10.8765       |
+| 0.0077 | 600   | 10.7174       |
+| 0.0090 | 700   | 10.4134       |
+| 0.0102 | 800   | 10.2001       |
+| 0.0115 | 900   | 10.0598       |
+| 0.0128 | 1000  | 9.8019        |
+| 0.0141 | 1100  | 9.6144        |
+| 0.0154 | 1200  | 9.3509        |
+| 0.0166 | 1300  | 9.1212        |
+| 0.0179 | 1400  | 8.9316        |
+| 0.0192 | 1500  | 8.8345        |
+| 0.0205 | 1600  | 8.791         |
+| 0.0218 | 1700  | 8.7675        |
+| 0.0230 | 1800  | 8.7487        |
+| 0.0243 | 1900  | 8.7465        |
+| 0.0256 | 2000  | 8.7353        |
+| 0.0269 | 2100  | 8.7231        |
+| 0.0282 | 2200  | 8.7079        |
+| 0.0294 | 2300  | 8.6999        |
+| 0.0307 | 2400  | 8.7062        |
+| 0.0320 | 2500  | 8.7044        |
+| 0.0333 | 2600  | 8.6868        |
+| 0.0346 | 2700  | 8.6834        |
+| 0.0358 | 2800  | 8.6796        |
+| 0.0371 | 2900  | 8.6736        |
+| 0.0384 | 3000  | 8.6677        |
+| 0.0397 | 3100  | 8.653         |
+| 0.0410 | 3200  | 8.6472        |
+| 0.0422 | 3300  | 8.6597        |
+| 0.0435 | 3400  | 8.646         |
+| 0.0448 | 3500  | 8.6523        |
+| 0.0461 | 3600  | 8.6513        |
+| 0.0474 | 3700  | 8.639         |
+| 0.0486 | 3800  | 8.6269        |
+| 0.0499 | 3900  | 8.6201        |
+| 0.0512 | 4000  | 8.634         |
+| 0.0525 | 4100  | 8.6203        |
+| 0.0537 | 4200  | 8.6243        |
+| 0.0550 | 4300  | 8.6289        |
+| 0.0563 | 4400  | 8.6065        |
+| 0.0576 | 4500  | 8.6068        |
+| 0.0589 | 4600  | 8.6026        |
+| 0.0601 | 4700  | 8.6067        |
+| 0.0614 | 4800  | 8.6048        |
+| 0.0627 | 4900  | 8.6078        |
+| 0.0640 | 5000  | 8.6006        |
+| 0.0653 | 5100  | 8.6056        |
+| 0.0665 | 5200  | 8.5972        |
+| 0.0678 | 5300  | 8.5999        |
+| 0.0691 | 5400  | 8.5856        |
+| 0.0704 | 5500  | 8.59          |
+| 0.0717 | 5600  | 8.5799        |
+| 0.0729 | 5700  | 8.5922        |
+| 0.0742 | 5800  | 8.573         |
+| 0.0755 | 5900  | 8.5764        |
+| 0.0768 | 6000  | 8.5729        |
+| 0.0781 | 6100  | 8.5816        |
+| 0.0793 | 6200  | 8.5763        |
+| 0.0806 | 6300  | 8.5784        |
+| 0.0819 | 6400  | 8.5798        |
+| 0.0832 | 6500  | 8.5775        |
+| 0.0845 | 6600  | 8.5698        |
+| 0.0857 | 6700  | 8.5695        |
+| 0.0870 | 6800  | 8.5661        |
+| 0.0883 | 6900  | 8.5594        |
+| 0.0896 | 7000  | 8.5523        |
+| 0.0909 | 7100  | 8.5615        |
+| 0.0921 | 7200  | 8.5565        |
+| 0.0934 | 7300  | 8.5522        |
+| 0.0947 | 7400  | 8.5463        |
+| 0.0960 | 7500  | 8.5433        |
+| 0.0973 | 7600  | 8.5307        |
+| 0.0985 | 7700  | 8.5448        |
+| 0.0998 | 7800  | 8.5462        |
+| 0.1011 | 7900  | 8.529         |
+| 0.1024 | 8000  | 8.5377        |
+| 0.1037 | 8100  | 8.5306        |
+| 0.1049 | 8200  | 8.5407        |
+| 0.1062 | 8300  | 8.5382        |
+| 0.1075 | 8400  | 8.5281        |
+| 0.1088 | 8500  | 8.5358        |
+| 0.1101 | 8600  | 8.528         |
+| 0.1113 | 8700  | 8.5216        |
+| 0.1126 | 8800  | 8.5264        |
+| 0.1139 | 8900  | 8.5178        |
+| 0.1152 | 9000  | 8.525         |
+| 0.1165 | 9100  | 8.5221        |
+| 0.1177 | 9200  | 8.5134        |
+| 0.1190 | 9300  | 8.5212        |
+| 0.1203 | 9400  | 8.5197        |
+| 0.1216 | 9500  | 8.5189        |
+| 0.1229 | 9600  | 8.5091        |
+| 0.1241 | 9700  | 8.5085        |
+| 0.1254 | 9800  | 8.5176        |
+| 0.1267 | 9900  | 8.5143        |
+| 0.1280 | 10000 | 8.5011        |
+| 0.1293 | 10100 | 8.4946        |
+| 0.1305 | 10200 | 8.504         |
+| 0.1318 | 10300 | 8.5046        |
+| 0.1331 | 10400 | 8.5074        |
+| 0.1344 | 10500 | 8.504         |
+| 0.1357 | 10600 | 8.5057        |
+| 0.1369 | 10700 | 8.5027        |
+| 0.1382 | 10800 | 8.5046        |
+| 0.1395 | 10900 | 8.4947        |
+| 0.1408 | 11000 | 8.4928        |
+| 0.1421 | 11100 | 8.5046        |
+| 0.1433 | 11200 | 8.4979        |
+| 0.1446 | 11300 | 8.4974        |
+| 0.1459 | 11400 | 8.49          |
+| 0.1472 | 11500 | 8.4924        |
+| 0.1485 | 11600 | 8.4981        |
+| 0.1497 | 11700 | 8.4821        |
+| 0.1510 | 11800 | 8.4827        |
+| 0.1523 | 11900 | 8.4849        |
+| 0.1536 | 12000 | 8.4816        |
+| 0.1549 | 12100 | 8.4959        |
+| 0.1561 | 12200 | 8.4887        |
+| 0.1574 | 12300 | 8.4904        |
+| 0.1587 | 12400 | 8.4805        |
+| 0.1600 | 12500 | 8.4821        |
+| 0.1612 | 12600 | 8.4896        |
+| 0.1625 | 12700 | 8.4888        |
+| 0.1638 | 12800 | 8.4816        |
+| 0.1651 | 12900 | 8.4784        |
+| 0.1664 | 13000 | 8.4832        |
+| 0.1676 | 13100 | 8.4832        |
+| 0.1689 | 13200 | 8.4731        |
+| 0.1702 | 13300 | 8.4835        |
+| 0.1715 | 13400 | 8.4808        |
+| 0.1728 | 13500 | 8.4773        |
+| 0.1740 | 13600 | 8.4734        |
+| 0.1753 | 13700 | 8.4732        |
+| 0.1766 | 13800 | 8.4758        |
+| 0.1779 | 13900 | 8.4675        |
+| 0.1792 | 14000 | 8.466         |
+| 0.1804 | 14100 | 8.4649        |
+| 0.1817 | 14200 | 8.467         |
+| 0.1830 | 14300 | 8.4811        |
+| 0.1843 | 14400 | 8.4761        |
+| 0.1856 | 14500 | 8.4584        |
+| 0.1868 | 14600 | 8.4674        |
+| 0.1881 | 14700 | 8.477         |
+| 0.1894 | 14800 | 8.4639        |
+| 0.1907 | 14900 | 8.4527        |
+| 0.1920 | 15000 | 8.4657        |
+| 0.1932 | 15100 | 8.4592        |
+| 0.1945 | 15200 | 8.4663        |
+| 0.1958 | 15300 | 8.4699        |
+| 0.1971 | 15400 | 8.4646        |
+| 0.1984 | 15500 | 8.4676        |
+| 0.1996 | 15600 | 8.4546        |
+| 0.2009 | 15700 | 8.4575        |
+| 0.2022 | 15800 | 8.4541        |
+| 0.2035 | 15900 | 8.4627        |
+| 0.2048 | 16000 | 8.4648        |
+| 0.2060 | 16100 | 8.4605        |
+| 0.2073 | 16200 | 8.4563        |
+| 0.2086 | 16300 | 8.456         |
+| 0.2099 | 16400 | 8.4513        |
+| 0.2112 | 16500 | 8.4614        |
+| 0.2124 | 16600 | 8.4591        |
+| 0.2137 | 16700 | 8.4533        |
+| 0.2150 | 16800 | 8.4507        |
+| 0.2163 | 16900 | 8.4543        |
+| 0.2176 | 17000 | 8.4539        |
+| 0.2188 | 17100 | 8.4433        |
+| 0.2201 | 17200 | 8.4406        |
+| 0.2214 | 17300 | 8.4449        |
+| 0.2227 | 17400 | 8.4532        |
+| 0.2240 | 17500 | 8.4473        |
+| 0.2252 | 17600 | 8.4399        |
+| 0.2265 | 17700 | 8.4442        |
+| 0.2278 | 17800 | 8.4449        |
+| 0.2291 | 17900 | 8.4461        |
+| 0.2304 | 18000 | 8.4434        |
+| 0.2316 | 18100 | 8.4497        |
+| 0.2329 | 18200 | 8.4506        |
+| 0.2342 | 18300 | 8.4465        |
+| 0.2355 | 18400 | 8.4278        |
+| 0.2368 | 18500 | 8.4296        |
+| 0.2380 | 18600 | 8.4554        |
+| 0.2393 | 18700 | 8.4302        |
+| 0.2406 | 18800 | 8.4376        |
+| 0.2419 | 18900 | 8.4393        |
+| 0.2432 | 19000 | 8.4395        |
+| 0.2444 | 19100 | 8.4318        |
+| 0.2457 | 19200 | 8.4434        |
+| 0.2470 | 19300 | 8.4383        |
+| 0.2483 | 19400 | 8.4345        |
+| 0.2496 | 19500 | 8.4236        |
+| 0.2508 | 19600 | 8.4413        |
+| 0.2521 | 19700 | 8.4338        |
+| 0.2534 | 19800 | 8.4194        |
+| 0.2547 | 19900 | 8.434         |
+| 0.2560 | 20000 | 8.4358        |
+| 0.2572 | 20100 | 8.4433        |
+| 0.2585 | 20200 | 8.4302        |
+| 0.2598 | 20300 | 8.4224        |
+| 0.2611 | 20400 | 8.4419        |
+| 0.2623 | 20500 | 8.4315        |
+| 0.2636 | 20600 | 8.4218        |
+| 0.2649 | 20700 | 8.4276        |
+| 0.2662 | 20800 | 8.4278        |
+| 0.2675 | 20900 | 8.4339        |
+| 0.2687 | 21000 | 8.4391        |
+| 0.2700 | 21100 | 8.4306        |
+| 0.2713 | 21200 | 8.4192        |
+| 0.2726 | 21300 | 8.4265        |
+| 0.2739 | 21400 | 8.435         |
+| 0.2751 | 21500 | 8.4226        |
+| 0.2764 | 21600 | 8.4146        |
+| 0.2777 | 21700 | 8.428         |
+| 0.2790 | 21800 | 8.4157        |
+| 0.2803 | 21900 | 8.412         |
+| 0.2815 | 22000 | 8.408         |
+| 0.2828 | 22100 | 8.4233        |
+| 0.2841 | 22200 | 8.433         |
+| 0.2854 | 22300 | 8.4141        |
+| 0.2867 | 22400 | 8.4068        |
+| 0.2879 | 22500 | 8.4272        |
+| 0.2892 | 22600 | 8.4193        |
+| 0.2905 | 22700 | 8.4171        |
+| 0.2918 | 22800 | 8.4209        |
+| 0.2931 | 22900 | 8.4049        |
+| 0.2943 | 23000 | 8.4204        |
+| 0.2956 | 23100 | 8.4178        |
+| 0.2969 | 23200 | 8.4095        |
+| 0.2982 | 23300 | 8.4213        |
+| 0.2995 | 23400 | 8.4162        |
+| 0.3007 | 23500 | 8.4164        |
+| 0.3020 | 23600 | 8.4157        |
+| 0.3033 | 23700 | 8.4194        |
+| 0.3046 | 23800 | 8.4173        |
+| 0.3059 | 23900 | 8.4237        |
+| 0.3071 | 24000 | 8.4244        |
+| 0.3084 | 24100 | 8.4147        |
+| 0.3097 | 24200 | 8.4045        |
+| 0.3110 | 24300 | 8.4109        |
+| 0.3123 | 24400 | 8.4162        |
+| 0.3135 | 24500 | 8.4225        |
+| 0.3148 | 24600 | 8.4152        |
+| 0.3161 | 24700 | 8.3963        |
+| 0.3174 | 24800 | 8.4144        |
+| 0.3187 | 24900 | 8.4172        |
+| 0.3199 | 25000 | 8.4095        |
+| 0.3212 | 25100 | 8.4031        |
+| 0.3225 | 25200 | 8.408         |
+| 0.3238 | 25300 | 8.4049        |
+| 0.3251 | 25400 | 8.405         |
+| 0.3263 | 25500 | 8.3955        |
+| 0.3276 | 25600 | 8.3845        |
+| 0.3289 | 25700 | 8.4132        |
+| 0.3302 | 25800 | 8.4106        |
+| 0.3315 | 25900 | 8.4189        |
+| 0.3327 | 26000 | 8.3942        |
+| 0.3340 | 26100 | 8.4062        |
+| 0.3353 | 26200 | 8.4118        |
+| 0.3366 | 26300 | 8.4032        |
+| 0.3379 | 26400 | 8.4077        |
+| 0.3391 | 26500 | 8.4188        |
+| 0.3404 | 26600 | 8.3865        |
+| 0.3417 | 26700 | 8.4043        |
+| 0.3430 | 26800 | 8.4053        |
+| 0.3443 | 26900 | 8.3966        |
+| 0.3455 | 27000 | 8.3957        |
+| 0.3468 | 27100 | 8.4032        |
+| 0.3481 | 27200 | 8.3814        |
+| 0.3494 | 27300 | 8.3974        |
+| 0.3507 | 27400 | 8.4064        |
+| 0.3519 | 27500 | 8.4001        |
+| 0.3532 | 27600 | 8.402         |
+| 0.3545 | 27700 | 8.41          |
+| 0.3558 | 27800 | 8.4052        |
+| 0.3571 | 27900 | 8.4021        |
+| 0.3583 | 28000 | 8.3969        |
+| 0.3596 | 28100 | 8.4142        |
+| 0.3609 | 28200 | 8.3894        |
+| 0.3622 | 28300 | 8.3988        |
+| 0.3635 | 28400 | 8.3861        |
+| 0.3647 | 28500 | 8.379         |
+| 0.3660 | 28600 | 8.3919        |
+| 0.3673 | 28700 | 8.3976        |
+| 0.3686 | 28800 | 8.4002        |
+| 0.3698 | 28900 | 8.3957        |
+| 0.3711 | 29000 | 8.401         |
+| 0.3724 | 29100 | 8.3846        |
+| 0.3737 | 29200 | 8.3951        |
+| 0.3750 | 29300 | 8.3855        |
+| 0.3762 | 29400 | 8.3968        |
+| 0.3775 | 29500 | 8.3826        |
+| 0.3788 | 29600 | 8.397         |
+| 0.3801 | 29700 | 8.4039        |
+| 0.3814 | 29800 | 8.3793        |
+| 0.3826 | 29900 | 8.3853        |
+| 0.3839 | 30000 | 8.3851        |
+| 0.3852 | 30100 | 8.3874        |
+| 0.3865 | 30200 | 8.3851        |
+| 0.3878 | 30300 | 8.3776        |
+| 0.3890 | 30400 | 8.3846        |
+| 0.3903 | 30500 | 8.3822        |
+| 0.3916 | 30600 | 8.392         |
+| 0.3929 | 30700 | 8.4014        |
+| 0.3942 | 30800 | 8.3892        |
+| 0.3954 | 30900 | 8.3892        |
+| 0.3967 | 31000 | 8.3866        |
+| 0.3980 | 31100 | 8.3837        |
+| 0.3993 | 31200 | 8.3856        |
+| 0.4006 | 31300 | 8.3851        |
+| 0.4018 | 31400 | 8.3755        |
+| 0.4031 | 31500 | 8.398         |
+| 0.4044 | 31600 | 8.3769        |
+| 0.4057 | 31700 | 8.3926        |
+| 0.4070 | 31800 | 8.3806        |
+| 0.4082 | 31900 | 8.3855        |
+| 0.4095 | 32000 | 8.3667        |
+| 0.4108 | 32100 | 8.3754        |
+| 0.4121 | 32200 | 8.3874        |
+| 0.4134 | 32300 | 8.3905        |
+| 0.4146 | 32400 | 8.3952        |
+| 0.4159 | 32500 | 8.3759        |
+| 0.4172 | 32600 | 8.3883        |
+| 0.4185 | 32700 | 8.3896        |
+| 0.4198 | 32800 | 8.3859        |
+| 0.4210 | 32900 | 8.3765        |
+| 0.4223 | 33000 | 8.3805        |
+| 0.4236 | 33100 | 8.3729        |
+| 0.4249 | 33200 | 8.3609        |
+| 0.4262 | 33300 | 8.3731        |
+| 0.4274 | 33400 | 8.3693        |
+| 0.4287 | 33500 | 8.3731        |
+| 0.4300 | 33600 | 8.3693        |
+| 0.4313 | 33700 | 8.3735        |
+| 0.4326 | 33800 | 8.377         |
+| 0.4338 | 33900 | 8.3792        |
+| 0.4351 | 34000 | 8.3764        |
+| 0.4364 | 34100 | 8.3774        |
+| 0.4377 | 34200 | 8.3728        |
+| 0.4390 | 34300 | 8.371         |
+| 0.4402 | 34400 | 8.3791        |
+| 0.4415 | 34500 | 8.365         |
+| 0.4428 | 34600 | 8.3781        |
+| 0.4441 | 34700 | 8.3574        |
+| 0.4454 | 34800 | 8.3798        |
+| 0.4466 | 34900 | 8.3865        |
+| 0.4479 | 35000 | 8.3734        |
+| 0.4492 | 35100 | 8.3859        |
+| 0.4505 | 35200 | 8.3743        |
+| 0.4518 | 35300 | 8.3741        |
+| 0.4530 | 35400 | 8.3654        |
+| 0.4543 | 35500 | 8.3836        |
+| 0.4556 | 35600 | 8.3703        |
+| 0.4569 | 35700 | 8.3699        |
+| 0.4582 | 35800 | 8.3658        |
+| 0.4594 | 35900 | 8.3768        |
+| 0.4607 | 36000 | 8.3637        |
+| 0.4620 | 36100 | 8.39          |
+| 0.4633 | 36200 | 8.3744        |
+| 0.4646 | 36300 | 8.3674        |
+| 0.4658 | 36400 | 8.3797        |
+| 0.4671 | 36500 | 8.3827        |
+| 0.4684 | 36600 | 8.372         |
+| 0.4697 | 36700 | 8.3645        |
+| 0.4709 | 36800 | 8.3655        |
+| 0.4722 | 36900 | 8.3846        |
+| 0.4735 | 37000 | 8.3646        |
+| 0.4748 | 37100 | 8.3624        |
+| 0.4761 | 37200 | 8.3639        |
+| 0.4773 | 37300 | 8.3636        |
+| 0.4786 | 37400 | 8.3491        |
+| 0.4799 | 37500 | 8.3738        |
+| 0.4812 | 37600 | 8.3637        |
+| 0.4825 | 37700 | 8.3645        |
+| 0.4837 | 37800 | 8.37          |
+| 0.4850 | 37900 | 8.3699        |
+| 0.4863 | 38000 | 8.3609        |
+| 0.4876 | 38100 | 8.3783        |
+| 0.4889 | 38200 | 8.3613        |
+| 0.4901 | 38300 | 8.3745        |
+| 0.4914 | 38400 | 8.3503        |
+| 0.4927 | 38500 | 8.3747        |
+| 0.4940 | 38600 | 8.3635        |
+| 0.4953 | 38700 | 8.3608        |
+| 0.4965 | 38800 | 8.3675        |
+| 0.4978 | 38900 | 8.368         |
+| 0.4991 | 39000 | 8.3706        |
+| 0.5004 | 39100 | 8.3716        |
+| 0.5017 | 39200 | 8.3744        |
+| 0.5029 | 39300 | 8.3659        |
+| 0.5042 | 39400 | 8.3687        |
+| 0.5055 | 39500 | 8.3637        |
+| 0.5068 | 39600 | 8.3479        |
+| 0.5081 | 39700 | 8.3429        |
+| 0.5093 | 39800 | 8.3607        |
+| 0.5106 | 39900 | 8.3534        |
+| 0.5119 | 40000 | 8.3465        |
+| 0.5132 | 40100 | 8.372         |
+| 0.5145 | 40200 | 8.3547        |
+| 0.5157 | 40300 | 8.3565        |
+| 0.5170 | 40400 | 8.369         |
+| 0.5183 | 40500 | 8.374         |
+| 0.5196 | 40600 | 8.3595        |
+| 0.5209 | 40700 | 8.357         |
+| 0.5221 | 40800 | 8.3545        |
+| 0.5234 | 40900 | 8.3541        |
+| 0.5247 | 41000 | 8.3595        |
+| 0.5260 | 41100 | 8.3504        |
+| 0.5273 | 41200 | 8.3667        |
+| 0.5285 | 41300 | 8.3542        |
+| 0.5298 | 41400 | 8.3665        |
+| 0.5311 | 41500 | 8.36          |
+| 0.5324 | 41600 | 8.3554        |
+| 0.5337 | 41700 | 8.3564        |
+| 0.5349 | 41800 | 8.368         |
+| 0.5362 | 41900 | 8.3634        |
+| 0.5375 | 42000 | 8.3513        |
+| 0.5388 | 42100 | 8.3544        |
+| 0.5401 | 42200 | 8.3532        |
+| 0.5413 | 42300 | 8.3576        |
+| 0.5426 | 42400 | 8.3578        |
+| 0.5439 | 42500 | 8.3596        |
+| 0.5452 | 42600 | 8.3542        |
+| 0.5465 | 42700 | 8.354         |
+| 0.5477 | 42800 | 8.3606        |
+| 0.5490 | 42900 | 8.3611        |
+| 0.5503 | 43000 | 8.3708        |
+| 0.5516 | 43100 | 8.3627        |
+| 0.5529 | 43200 | 8.3451        |
+| 0.5541 | 43300 | 8.361         |
+| 0.5554 | 43400 | 8.3499        |
+| 0.5567 | 43500 | 8.3559        |
+| 0.5580 | 43600 | 8.3356        |
+| 0.5593 | 43700 | 8.3467        |
+| 0.5605 | 43800 | 8.3648        |
+| 0.5618 | 43900 | 8.3523        |
+| 0.5631 | 44000 | 8.3599        |
+| 0.5644 | 44100 | 8.3687        |
+| 0.5657 | 44200 | 8.347         |
+| 0.5669 | 44300 | 8.3365        |
+| 0.5682 | 44400 | 8.348         |
+| 0.5695 | 44500 | 8.3631        |
+| 0.5708 | 44600 | 8.3609        |
+| 0.5721 | 44700 | 8.368         |
+| 0.5733 | 44800 | 8.374         |
+| 0.5746 | 44900 | 8.349         |
+| 0.5759 | 45000 | 8.3493        |
+| 0.5772 | 45100 | 8.3568        |
+| 0.5784 | 45200 | 8.3294        |
+| 0.5797 | 45300 | 8.337         |
+| 0.5810 | 45400 | 8.3545        |
+| 0.5823 | 45500 | 8.3512        |
+| 0.5836 | 45600 | 8.3419        |
+| 0.5848 | 45700 | 8.3411        |
+| 0.5861 | 45800 | 8.3509        |
+| 0.5874 | 45900 | 8.3465        |
+| 0.5887 | 46000 | 8.3489        |
+| 0.5900 | 46100 | 8.3555        |
+| 0.5912 | 46200 | 8.3506        |
+| 0.5925 | 46300 | 8.3492        |
+| 0.5938 | 46400 | 8.3493        |
+| 0.5951 | 46500 | 8.3494        |
+| 0.5964 | 46600 | 8.3591        |
+| 0.5976 | 46700 | 8.3357        |
+| 0.5989 | 46800 | 8.3337        |
+| 0.6002 | 46900 | 8.3414        |
+| 0.6015 | 47000 | 8.3598        |
+| 0.6028 | 47100 | 8.3433        |
+| 0.6040 | 47200 | 8.3296        |
+| 0.6053 | 47300 | 8.3354        |
+| 0.6066 | 47400 | 8.3515        |
+| 0.6079 | 47500 | 8.3472        |
+| 0.6092 | 47600 | 8.3374        |
+| 0.6104 | 47700 | 8.3516        |
+| 0.6117 | 47800 | 8.3549        |
+| 0.6130 | 47900 | 8.3436        |
+| 0.6143 | 48000 | 8.3295        |
+| 0.6156 | 48100 | 8.3592        |
+| 0.6168 | 48200 | 8.3374        |
+| 0.6181 | 48300 | 8.3328        |
+| 0.6194 | 48400 | 8.33          |
+| 0.6207 | 48500 | 8.3433        |
+| 0.6220 | 48600 | 8.347         |
+| 0.6232 | 48700 | 8.3492        |
+| 0.6245 | 48800 | 8.3485        |
+| 0.6258 | 48900 | 8.344         |
+| 0.6271 | 49000 | 8.357         |
+| 0.6284 | 49100 | 8.3444        |
+| 0.6296 | 49200 | 8.3464        |
+| 0.6309 | 49300 | 8.345         |
+| 0.6322 | 49400 | 8.3462        |
+| 0.6335 | 49500 | 8.3451        |
+| 0.6348 | 49600 | 8.3402        |
+| 0.6360 | 49700 | 8.3375        |
+| 0.6373 | 49800 | 8.343         |
+| 0.6386 | 49900 | 8.3463        |
+| 0.6399 | 50000 | 8.3352        |
+| 0.6412 | 50100 | 8.3317        |
+| 0.6424 | 50200 | 8.3414        |
+| 0.6437 | 50300 | 8.326         |
+| 0.6450 | 50400 | 8.3281        |
+| 0.6463 | 50500 | 8.3354        |
+| 0.6476 | 50600 | 8.3411        |
+| 0.6488 | 50700 | 8.3384        |
+| 0.6501 | 50800 | 8.3415        |
+| 0.6514 | 50900 | 8.3672        |
+| 0.6527 | 51000 | 8.3371        |
+| 0.6540 | 51100 | 8.3431        |
+| 0.6552 | 51200 | 8.3471        |
+| 0.6565 | 51300 | 8.3292        |
+| 0.6578 | 51400 | 8.3398        |
+| 0.6591 | 51500 | 8.3333        |
+| 0.6604 | 51600 | 8.3412        |
+| 0.6616 | 51700 | 8.3256        |
+| 0.6629 | 51800 | 8.3417        |
+| 0.6642 | 51900 | 8.335         |
+| 0.6655 | 52000 | 8.3431        |
+| 0.6668 | 52100 | 8.3214        |
+| 0.6680 | 52200 | 8.3327        |
+| 0.6693 | 52300 | 8.3311        |
+| 0.6706 | 52400 | 8.3515        |
+| 0.6719 | 52500 | 8.3409        |
+| 0.6732 | 52600 | 8.3295        |
+| 0.6744 | 52700 | 8.3242        |
+| 0.6757 | 52800 | 8.3459        |
+| 0.6770 | 52900 | 8.3088        |
+| 0.6783 | 53000 | 8.3454        |
+| 0.6795 | 53100 | 8.3336        |
+| 0.6808 | 53200 | 8.3534        |
+| 0.6821 | 53300 | 8.3277        |
+| 0.6834 | 53400 | 8.3534        |
+| 0.6847 | 53500 | 8.3399        |
+| 0.6859 | 53600 | 8.3332        |
+| 0.6872 | 53700 | 8.3269        |
+| 0.6885 | 53800 | 8.3339        |
+| 0.6898 | 53900 | 8.339         |
+| 0.6911 | 54000 | 8.3452        |
+| 0.6923 | 54100 | 8.324         |
+| 0.6936 | 54200 | 8.3305        |
+| 0.6949 | 54300 | 8.3359        |
+| 0.6962 | 54400 | 8.3267        |
+| 0.6975 | 54500 | 8.3221        |
+| 0.6987 | 54600 | 8.3295        |
+| 0.7000 | 54700 | 8.3459        |
+| 0.7013 | 54800 | 8.3446        |
+| 0.7026 | 54900 | 8.3235        |
+| 0.7039 | 55000 | 8.3393        |
+| 0.7051 | 55100 | 8.3359        |
+| 0.7064 | 55200 | 8.3209        |
+| 0.7077 | 55300 | 8.3377        |
+| 0.7090 | 55400 | 8.3277        |
+| 0.7103 | 55500 | 8.3298        |
+| 0.7115 | 55600 | 8.3279        |
+| 0.7128 | 55700 | 8.3207        |
+| 0.7141 | 55800 | 8.3202        |
+| 0.7154 | 55900 | 8.3339        |
+| 0.7167 | 56000 | 8.329         |
+| 0.7179 | 56100 | 8.3409        |
+| 0.7192 | 56200 | 8.3398        |
+| 0.7205 | 56300 | 8.3331        |
+| 0.7218 | 56400 | 8.3327        |
+| 0.7231 | 56500 | 8.3228        |
+| 0.7243 | 56600 | 8.3246        |
+| 0.7256 | 56700 | 8.3395        |
+| 0.7269 | 56800 | 8.3438        |
+| 0.7282 | 56900 | 8.3258        |
+| 0.7295 | 57000 | 8.3256        |
+| 0.7307 | 57100 | 8.3336        |
+| 0.7320 | 57200 | 8.341         |
+| 0.7333 | 57300 | 8.3229        |
+| 0.7346 | 57400 | 8.3364        |
+| 0.7359 | 57500 | 8.3219        |
+| 0.7371 | 57600 | 8.3247        |
+| 0.7384 | 57700 | 8.3254        |
+| 0.7397 | 57800 | 8.3319        |
+| 0.7410 | 57900 | 8.3202        |
+| 0.7423 | 58000 | 8.327         |
+| 0.7435 | 58100 | 8.3228        |
+| 0.7448 | 58200 | 8.3472        |
+| 0.7461 | 58300 | 8.3413        |
+| 0.7474 | 58400 | 8.3173        |
+| 0.7487 | 58500 | 8.3264        |
+| 0.7499 | 58600 | 8.3166        |
+| 0.7512 | 58700 | 8.3209        |
+| 0.7525 | 58800 | 8.3184        |
+| 0.7538 | 58900 | 8.3357        |
+| 0.7551 | 59000 | 8.3249        |
+| 0.7563 | 59100 | 8.3251        |
+| 0.7576 | 59200 | 8.3215        |
+| 0.7589 | 59300 | 8.3323        |
+| 0.7602 | 59400 | 8.3552        |
+| 0.7615 | 59500 | 8.3237        |
+| 0.7627 | 59600 | 8.3355        |
+| 0.7640 | 59700 | 8.328         |
+| 0.7653 | 59800 | 8.324         |
+| 0.7666 | 59900 | 8.3117        |
+| 0.7679 | 60000 | 8.3367        |
+| 0.7691 | 60100 | 8.3214        |
+| 0.7704 | 60200 | 8.3084        |
+| 0.7717 | 60300 | 8.3249        |
+| 0.7730 | 60400 | 8.3238        |
+| 0.7743 | 60500 | 8.3251        |
+| 0.7755 | 60600 | 8.3328        |
+| 0.7768 | 60700 | 8.3344        |
+| 0.7781 | 60800 | 8.3186        |
+| 0.7794 | 60900 | 8.3177        |
+| 0.7807 | 61000 | 8.3032        |
+| 0.7819 | 61100 | 8.3274        |
+| 0.7832 | 61200 | 8.3101        |
+| 0.7845 | 61300 | 8.3196        |
+| 0.7858 | 61400 | 8.3467        |
+| 0.7870 | 61500 | 8.3203        |
+| 0.7883 | 61600 | 8.3033        |
+| 0.7896 | 61700 | 8.3259        |
+| 0.7909 | 61800 | 8.3348        |
+| 0.7922 | 61900 | 8.3174        |
+| 0.7934 | 62000 | 8.343         |
+| 0.7947 | 62100 | 8.3223        |
+| 0.7960 | 62200 | 8.3161        |
+| 0.7973 | 62300 | 8.3138        |
+| 0.7986 | 62400 | 8.3144        |
+| 0.7998 | 62500 | 8.3111        |
+| 0.8011 | 62600 | 8.3178        |
+| 0.8024 | 62700 | 8.3169        |
+| 0.8037 | 62800 | 8.328         |
+| 0.8050 | 62900 | 8.314         |
+| 0.8062 | 63000 | 8.3264        |
+| 0.8075 | 63100 | 8.3135        |
+| 0.8088 | 63200 | 8.3158        |
+| 0.8101 | 63300 | 8.3081        |
+| 0.8114 | 63400 | 8.3235        |
+| 0.8126 | 63500 | 8.321         |
+| 0.8139 | 63600 | 8.3307        |
+| 0.8152 | 63700 | 8.3335        |
+| 0.8165 | 63800 | 8.3139        |
+| 0.8178 | 63900 | 8.315         |
+| 0.8190 | 64000 | 8.3172        |
+| 0.8203 | 64100 | 8.3265        |
+| 0.8216 | 64200 | 8.322         |
+| 0.8229 | 64300 | 8.3278        |
+| 0.8242 | 64400 | 8.3116        |
+| 0.8254 | 64500 | 8.3248        |
+| 0.8267 | 64600 | 8.3241        |
+| 0.8280 | 64700 | 8.3269        |
+| 0.8293 | 64800 | 8.3154        |
+| 0.8306 | 64900 | 8.3174        |
+| 0.8318 | 65000 | 8.3154        |
+| 0.8331 | 65100 | 8.3184        |
+| 0.8344 | 65200 | 8.323         |
+| 0.8357 | 65300 | 8.3243        |
+| 0.8370 | 65400 | 8.3127        |
+| 0.8382 | 65500 | 8.3186        |
+| 0.8395 | 65600 | 8.3142        |
+| 0.8408 | 65700 | 8.3161        |
+| 0.8421 | 65800 | 8.3199        |
+| 0.8434 | 65900 | 8.3289        |
+| 0.8446 | 66000 | 8.3174        |
+| 0.8459 | 66100 | 8.3215        |
+| 0.8472 | 66200 | 8.3187        |
+| 0.8485 | 66300 | 8.3367        |
+| 0.8498 | 66400 | 8.3151        |
+| 0.8510 | 66500 | 8.32          |
+| 0.8523 | 66600 | 8.3233        |
+| 0.8536 | 66700 | 8.3116        |
+| 0.8549 | 66800 | 8.3262        |
+| 0.8562 | 66900 | 8.3162        |
+| 0.8574 | 67000 | 8.3153        |
+| 0.8587 | 67100 | 8.2974        |
+| 0.8600 | 67200 | 8.3354        |
+| 0.8613 | 67300 | 8.3185        |
+| 0.8626 | 67400 | 8.3173        |
+| 0.8638 | 67500 | 8.3274        |
+| 0.8651 | 67600 | 8.3203        |
+| 0.8664 | 67700 | 8.3123        |
+| 0.8677 | 67800 | 8.3221        |
+| 0.8690 | 67900 | 8.3101        |
+| 0.8702 | 68000 | 8.3304        |
+| 0.8715 | 68100 | 8.3146        |
+| 0.8728 | 68200 | 8.3216        |
+| 0.8741 | 68300 | 8.3168        |
+| 0.8754 | 68400 | 8.2954        |
+| 0.8766 | 68500 | 8.311         |
+| 0.8779 | 68600 | 8.3275        |
+| 0.8792 | 68700 | 8.3215        |
+| 0.8805 | 68800 | 8.3222        |
+| 0.8818 | 68900 | 8.3125        |
+| 0.8830 | 69000 | 8.3228        |
+| 0.8843 | 69100 | 8.3251        |
+| 0.8856 | 69200 | 8.317         |
+| 0.8869 | 69300 | 8.3041        |
+| 0.8881 | 69400 | 8.3273        |
+| 0.8894 | 69500 | 8.3254        |
+| 0.8907 | 69600 | 8.3222        |
+| 0.8920 | 69700 | 8.311         |
+| 0.8933 | 69800 | 8.2815        |
+| 0.8945 | 69900 | 8.3134        |
+| 0.8958 | 70000 | 8.3259        |
+| 0.8971 | 70100 | 8.3067        |
+| 0.8984 | 70200 | 8.3008        |
+| 0.8997 | 70300 | 8.3187        |
+| 0.9009 | 70400 | 8.3242        |
+| 0.9022 | 70500 | 8.3078        |
+| 0.9035 | 70600 | 8.3089        |
+| 0.9048 | 70700 | 8.3238        |
+| 0.9061 | 70800 | 8.3225        |
+| 0.9073 | 70900 | 8.305         |
+| 0.9086 | 71000 | 8.3014        |
+| 0.9099 | 71100 | 8.3057        |
+| 0.9112 | 71200 | 8.3147        |
+| 0.9125 | 71300 | 8.3201        |
+| 0.9137 | 71400 | 8.3095        |
+| 0.9150 | 71500 | 8.3133        |
+| 0.9163 | 71600 | 8.3021        |
+| 0.9176 | 71700 | 8.3053        |
+| 0.9189 | 71800 | 8.3112        |
+| 0.9201 | 71900 | 8.3074        |
+| 0.9214 | 72000 | 8.3105        |
+| 0.9227 | 72100 | 8.3145        |
+| 0.9240 | 72200 | 8.3248        |
+| 0.9253 | 72300 | 8.3199        |
+| 0.9265 | 72400 | 8.3199        |
+| 0.9278 | 72500 | 8.3221        |
+| 0.9291 | 72600 | 8.3113        |
+| 0.9304 | 72700 | 8.3212        |
+| 0.9317 | 72800 | 8.309         |
+| 0.9329 | 72900 | 8.3186        |
+| 0.9342 | 73000 | 8.3038        |
+| 0.9355 | 73100 | 8.3173        |
+| 0.9368 | 73200 | 8.317         |
+| 0.9381 | 73300 | 8.3313        |
+| 0.9393 | 73400 | 8.3018        |
+| 0.9406 | 73500 | 8.3118        |
+| 0.9419 | 73600 | 8.3089        |
+| 0.9432 | 73700 | 8.3304        |
+| 0.9445 | 73800 | 8.3074        |
+| 0.9457 | 73900 | 8.3007        |
+| 0.9470 | 74000 | 8.3059        |
+| 0.9483 | 74100 | 8.3043        |
+| 0.9496 | 74200 | 8.3115        |
+| 0.9509 | 74300 | 8.3278        |
+| 0.9521 | 74400 | 8.3231        |
+| 0.9534 | 74500 | 8.3109        |
+| 0.9547 | 74600 | 8.3235        |
+| 0.9560 | 74700 | 8.3196        |
+| 0.9573 | 74800 | 8.3113        |
+| 0.9585 | 74900 | 8.3197        |
+| 0.9598 | 75000 | 8.3143        |
+| 0.9611 | 75100 | 8.3121        |
+| 0.9624 | 75200 | 8.2992        |
+| 0.9637 | 75300 | 8.2954        |
+| 0.9649 | 75400 | 8.3133        |
+| 0.9662 | 75500 | 8.3099        |
+| 0.9675 | 75600 | 8.3236        |
+| 0.9688 | 75700 | 8.3101        |
+| 0.9701 | 75800 | 8.3256        |
+| 0.9713 | 75900 | 8.3041        |
+| 0.9726 | 76000 | 8.3035        |
+| 0.9739 | 76100 | 8.312         |
+| 0.9752 | 76200 | 8.3112        |
+| 0.9765 | 76300 | 8.3044        |
+| 0.9777 | 76400 | 8.3135        |
+| 0.9790 | 76500 | 8.3116        |
+| 0.9803 | 76600 | 8.3006        |
+| 0.9816 | 76700 | 8.3068        |
+| 0.9829 | 76800 | 8.3023        |
+| 0.9841 | 76900 | 8.31          |
+| 0.9854 | 77000 | 8.3129        |
+| 0.9867 | 77100 | 8.3197        |
+| 0.9880 | 77200 | 8.3105        |
+| 0.9893 | 77300 | 8.3196        |
+| 0.9905 | 77400 | 8.3169        |
+| 0.9918 | 77500 | 8.3168        |
+| 0.9931 | 77600 | 8.3241        |
+| 0.9944 | 77700 | 8.3144        |
+| 0.9956 | 77800 | 8.2999        |
+| 0.9969 | 77900 | 8.3206        |
+| 0.9982 | 78000 | 8.3046        |
+| 0.9995 | 78100 | 8.306         |
+
+</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"
+  },
+  "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:afadd770db604d6fbbf07bd0794b266c4f78a50374181e4337d21344a907a018
+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]"
+}