Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "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_Dense/config.json

@@ -0,0 +1 @@
+{"in_features": 768, "out_features": 768, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}

2_Dense/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d854334733ce63144c4a6303859ea9e72cc7a67ba57146028aca03d2dde6c2d8
+size 2362528

README.md

@@ -0,0 +1,1227 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1021596
+- loss:MultipleNegativesRankingLoss
+base_model: codersan/FaLabse
+widget:
+- source_sentence: 'بیشتر زنان دلیل این کار را درک نمی‌کنند '
+  sentences:
+  - Most women can't understand why this happens.
+  - feeling with confusion and annoyance that what he could decide easily and clearly
+    by himself, he could not discuss before Princess Tverskaya, who to him stood for
+    the incarnation of that brute force which would inevitably control him in the
+    life he led in the eyes of the world, and hinder him from giving way to his feeling
+    of love and forgiveness.
+  - 'MR TALLBOYS: Happy days, happy days!'
+- source_sentence: به ادارات دولتی و اداره پست و سپس نزد استاندار رفت.
+  sentences:
+  - It strengthens the disease
+  - to government offices, to the post office, and to the Governor's.
+  - but she was utterly beside herself, and moved hanging on her husband's arm as
+    though in a dream.
+- source_sentence: در همین آن صدائی به گوشش رسید که بدون شک صدای بسته شدن ‌پنجره خانه
+    خانم سمپریل بود!
+  sentences:
+  - Even as she did so a sound checked her for an instant ' the unmistakable bang
+    of a window shutting, somewhere in Mrs Semprill's house.
+  - That was over the line.
+  - No one would be better able than she to shape the virtuous man who would restore
+    the prestige of the family
+- source_sentence: معنی آن مهر این است که 3 خدا، امروز به دست من انجام شد.
+  sentences:
+  - 'It signifies God: done this day by my hand.'
+  - They all embraced one another
+  - that's the mark of a Dark wizard.
+- source_sentence: اگر این کار مداومت می‌یافت، سنگر قادر به مقاومت نمی‌بود.
+  sentences:
+  - If this were continued, the barricade was no longer tenable.
+  - They rolled down on the ground.
+  - Well, for this moment she had a protector.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on codersan/FaLabse
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [codersan/FaLabse](https://huggingface.co/codersan/FaLabse). It maps sentences & paragraphs to a 768-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:** [codersan/FaLabse](https://huggingface.co/codersan/FaLabse) <!-- at revision 0fe1341c6962d7fe2ea375d90f9f55f34e395bcd -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 768 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}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
+  (3): 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("codersan/FaLaBSE_Mizan2")
+# Run inference
+sentences = [
+    'اگر این کار مداومت می\u200cیافت، سنگر قادر به مقاومت نمی\u200cبود.',
+    'If this were continued, the barricade was no longer tenable.',
+    'Well, for this moment she had a protector.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### 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: 1,021,596 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 18.63 tokens</li><li>max: 81 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 16.37 tokens</li><li>max: 85 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                    | positive                                                                                               |
+  |:--------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|
+  | <code>دختران برای اطاعت امر پدر از جا برخاستند.</code>                                                                    | <code>They arose to obey.</code>                                                                       |
+  | <code>همه چیز را بم وقع خواهی دانست.</code>                                                                               | <code>You'll know it all in time</code>                                                                |
+  | <code>او هر لحظه گرفتار یک‌ وضع است، زارزار گریه می‌کند. می‌گوید به ما توهین کرده‌اند، حیثیتمان را لکه‌دار نمودند.</code> | <code>She is in hysterics up there, and moans and says that we have been 'shamed and disgraced.</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
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 12
+- `learning_rate`: 5e-06
+- `weight_decay`: 0.01
+- `max_grad_norm`: 5
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `push_to_hub`: True
+- `hub_model_id`: codersan/FaLaBSE_Mizan2
+- `eval_on_start`: True
+- `batch_sampler`: no_duplicates
+
+#### 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`: 12
+- `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`: 5e-06
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 5
+- `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`: False
+- `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`: True
+- `resume_from_checkpoint`: None
+- `hub_model_id`: codersan/FaLaBSE_Mizan2
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `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
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: True
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0      | 0     | -             |
+| 0.0012 | 100   | 0.054         |
+| 0.0023 | 200   | 0.0442        |
+| 0.0035 | 300   | 0.0714        |
+| 0.0047 | 400   | 0.0715        |
+| 0.0059 | 500   | 0.0642        |
+| 0.0070 | 600   | 0.058         |
+| 0.0082 | 700   | 0.062         |
+| 0.0094 | 800   | 0.0626        |
+| 0.0106 | 900   | 0.0466        |
+| 0.0117 | 1000  | 0.0617        |
+| 0.0129 | 1100  | 0.0464        |
+| 0.0141 | 1200  | 0.0532        |
+| 0.0153 | 1300  | 0.0472        |
+| 0.0164 | 1400  | 0.0396        |
+| 0.0176 | 1500  | 0.0587        |
+| 0.0188 | 1600  | 0.0378        |
+| 0.0200 | 1700  | 0.0448        |
+| 0.0211 | 1800  | 0.0475        |
+| 0.0223 | 1900  | 0.0533        |
+| 0.0235 | 2000  | 0.0693        |
+| 0.0247 | 2100  | 0.0451        |
+| 0.0258 | 2200  | 0.0397        |
+| 0.0270 | 2300  | 0.0392        |
+| 0.0282 | 2400  | 0.0437        |
+| 0.0294 | 2500  | 0.0467        |
+| 0.0305 | 2600  | 0.0456        |
+| 0.0317 | 2700  | 0.0274        |
+| 0.0329 | 2800  | 0.0379        |
+| 0.0341 | 2900  | 0.0412        |
+| 0.0352 | 3000  | 0.0445        |
+| 0.0364 | 3100  | 0.0419        |
+| 0.0376 | 3200  | 0.032         |
+| 0.0388 | 3300  | 0.0351        |
+| 0.0399 | 3400  | 0.0442        |
+| 0.0411 | 3500  | 0.0434        |
+| 0.0423 | 3600  | 0.0331        |
+| 0.0435 | 3700  | 0.0398        |
+| 0.0446 | 3800  | 0.0518        |
+| 0.0458 | 3900  | 0.0287        |
+| 0.0470 | 4000  | 0.0322        |
+| 0.0482 | 4100  | 0.0389        |
+| 0.0493 | 4200  | 0.0268        |
+| 0.0505 | 4300  | 0.0352        |
+| 0.0517 | 4400  | 0.021         |
+| 0.0529 | 4500  | 0.0322        |
+| 0.0540 | 4600  | 0.0228        |
+| 0.0552 | 4700  | 0.0396        |
+| 0.0564 | 4800  | 0.033         |
+| 0.0576 | 4900  | 0.0444        |
+| 0.0587 | 5000  | 0.0392        |
+| 0.0599 | 5100  | 0.033         |
+| 0.0611 | 5200  | 0.0401        |
+| 0.0623 | 5300  | 0.0397        |
+| 0.0634 | 5400  | 0.0327        |
+| 0.0646 | 5500  | 0.0346        |
+| 0.0658 | 5600  | 0.0315        |
+| 0.0670 | 5700  | 0.0315        |
+| 0.0681 | 5800  | 0.0234        |
+| 0.0693 | 5900  | 0.0311        |
+| 0.0705 | 6000  | 0.0323        |
+| 0.0717 | 6100  | 0.0248        |
+| 0.0728 | 6200  | 0.0384        |
+| 0.0740 | 6300  | 0.0394        |
+| 0.0752 | 6400  | 0.0299        |
+| 0.0764 | 6500  | 0.0479        |
+| 0.0775 | 6600  | 0.0253        |
+| 0.0787 | 6700  | 0.0424        |
+| 0.0799 | 6800  | 0.0269        |
+| 0.0810 | 6900  | 0.035         |
+| 0.0822 | 7000  | 0.0349        |
+| 0.0834 | 7100  | 0.0302        |
+| 0.0846 | 7200  | 0.0426        |
+| 0.0857 | 7300  | 0.0287        |
+| 0.0869 | 7400  | 0.0254        |
+| 0.0881 | 7500  | 0.0306        |
+| 0.0893 | 7600  | 0.0356        |
+| 0.0904 | 7700  | 0.0393        |
+| 0.0916 | 7800  | 0.035         |
+| 0.0928 | 7900  | 0.0449        |
+| 0.0940 | 8000  | 0.0228        |
+| 0.0951 | 8100  | 0.0342        |
+| 0.0963 | 8200  | 0.0233        |
+| 0.0975 | 8300  | 0.0259        |
+| 0.0987 | 8400  | 0.0402        |
+| 0.0998 | 8500  | 0.0277        |
+| 0.1010 | 8600  | 0.0345        |
+| 0.1022 | 8700  | 0.0361        |
+| 0.1034 | 8800  | 0.0326        |
+| 0.1045 | 8900  | 0.0367        |
+| 0.1057 | 9000  | 0.0408        |
+| 0.1069 | 9100  | 0.0289        |
+| 0.1081 | 9200  | 0.026         |
+| 0.1092 | 9300  | 0.0367        |
+| 0.1104 | 9400  | 0.0327        |
+| 0.1116 | 9500  | 0.0273        |
+| 0.1128 | 9600  | 0.0545        |
+| 0.1139 | 9700  | 0.0395        |
+| 0.1151 | 9800  | 0.0394        |
+| 0.1163 | 9900  | 0.0293        |
+| 0.1175 | 10000 | 0.0411        |
+| 0.1186 | 10100 | 0.0353        |
+| 0.1198 | 10200 | 0.0369        |
+| 0.1210 | 10300 | 0.0222        |
+| 0.1222 | 10400 | 0.0418        |
+| 0.1233 | 10500 | 0.039         |
+| 0.1245 | 10600 | 0.041         |
+| 0.1257 | 10700 | 0.0316        |
+| 0.1269 | 10800 | 0.0351        |
+| 0.1280 | 10900 | 0.0258        |
+| 0.1292 | 11000 | 0.0481        |
+| 0.1304 | 11100 | 0.027         |
+| 0.1316 | 11200 | 0.0357        |
+| 0.1327 | 11300 | 0.0366        |
+| 0.1339 | 11400 | 0.0345        |
+| 0.1351 | 11500 | 0.0311        |
+| 0.1363 | 11600 | 0.0335        |
+| 0.1374 | 11700 | 0.0268        |
+| 0.1386 | 11800 | 0.0272        |
+| 0.1398 | 11900 | 0.0317        |
+| 0.1410 | 12000 | 0.052         |
+| 0.1421 | 12100 | 0.027         |
+| 0.1433 | 12200 | 0.028         |
+| 0.1445 | 12300 | 0.0435        |
+| 0.1457 | 12400 | 0.0335        |
+| 0.1468 | 12500 | 0.0506        |
+| 0.1480 | 12600 | 0.033         |
+| 0.1492 | 12700 | 0.0278        |
+| 0.1504 | 12800 | 0.0298        |
+| 0.1515 | 12900 | 0.0317        |
+| 0.1527 | 13000 | 0.0157        |
+| 0.1539 | 13100 | 0.0252        |
+| 0.1551 | 13200 | 0.0214        |
+| 0.1562 | 13300 | 0.0269        |
+| 0.1574 | 13400 | 0.0287        |
+| 0.1586 | 13500 | 0.0261        |
+| 0.1598 | 13600 | 0.0195        |
+| 0.1609 | 13700 | 0.0262        |
+| 0.1621 | 13800 | 0.0446        |
+| 0.1633 | 13900 | 0.0402        |
+| 0.1644 | 14000 | 0.0318        |
+| 0.1656 | 14100 | 0.039         |
+| 0.1668 | 14200 | 0.0227        |
+| 0.1680 | 14300 | 0.0247        |
+| 0.1691 | 14400 | 0.0236        |
+| 0.1703 | 14500 | 0.0213        |
+| 0.1715 | 14600 | 0.0434        |
+| 0.1727 | 14700 | 0.0486        |
+| 0.1738 | 14800 | 0.0537        |
+| 0.1750 | 14900 | 0.033         |
+| 0.1762 | 15000 | 0.0289        |
+| 0.1774 | 15100 | 0.0389        |
+| 0.1785 | 15200 | 0.0267        |
+| 0.1797 | 15300 | 0.031         |
+| 0.1809 | 15400 | 0.029         |
+| 0.1821 | 15500 | 0.0357        |
+| 0.1832 | 15600 | 0.0231        |
+| 0.1844 | 15700 | 0.035         |
+| 0.1856 | 15800 | 0.0201        |
+| 0.1868 | 15900 | 0.0361        |
+| 0.1879 | 16000 | 0.0297        |
+| 0.1891 | 16100 | 0.0216        |
+| 0.1903 | 16200 | 0.0283        |
+| 0.1915 | 16300 | 0.0205        |
+| 0.1926 | 16400 | 0.0318        |
+| 0.1938 | 16500 | 0.0385        |
+| 0.1950 | 16600 | 0.0363        |
+| 0.1962 | 16700 | 0.0462        |
+| 0.1973 | 16800 | 0.0342        |
+| 0.1985 | 16900 | 0.0213        |
+| 0.1997 | 17000 | 0.0492        |
+| 0.2009 | 17100 | 0.0354        |
+| 0.2020 | 17200 | 0.0219        |
+| 0.2032 | 17300 | 0.0338        |
+| 0.2044 | 17400 | 0.0322        |
+| 0.2056 | 17500 | 0.0283        |
+| 0.2067 | 17600 | 0.024         |
+| 0.2079 | 17700 | 0.0206        |
+| 0.2091 | 17800 | 0.0416        |
+| 0.2103 | 17900 | 0.0284        |
+| 0.2114 | 18000 | 0.0305        |
+| 0.2126 | 18100 | 0.0261        |
+| 0.2138 | 18200 | 0.0228        |
+| 0.2150 | 18300 | 0.048         |
+| 0.2161 | 18400 | 0.0241        |
+| 0.2173 | 18500 | 0.0484        |
+| 0.2185 | 18600 | 0.0362        |
+| 0.2197 | 18700 | 0.0296        |
+| 0.2208 | 18800 | 0.0335        |
+| 0.2220 | 18900 | 0.0383        |
+| 0.2232 | 19000 | 0.0378        |
+| 0.2244 | 19100 | 0.042         |
+| 0.2255 | 19200 | 0.0405        |
+| 0.2267 | 19300 | 0.0369        |
+| 0.2279 | 19400 | 0.0238        |
+| 0.2291 | 19500 | 0.0226        |
+| 0.2302 | 19600 | 0.0338        |
+| 0.2314 | 19700 | 0.0299        |
+| 0.2326 | 19800 | 0.0436        |
+| 0.2338 | 19900 | 0.0302        |
+| 0.2349 | 20000 | 0.0253        |
+| 0.2361 | 20100 | 0.0233        |
+| 0.2373 | 20200 | 0.0427        |
+| 0.2385 | 20300 | 0.0328        |
+| 0.2396 | 20400 | 0.0366        |
+| 0.2408 | 20500 | 0.0231        |
+| 0.2420 | 20600 | 0.0467        |
+| 0.2431 | 20700 | 0.0287        |
+| 0.2443 | 20800 | 0.0393        |
+| 0.2455 | 20900 | 0.0276        |
+| 0.2467 | 21000 | 0.0355        |
+| 0.2478 | 21100 | 0.0189        |
+| 0.2490 | 21200 | 0.0152        |
+| 0.2502 | 21300 | 0.0272        |
+| 0.2514 | 21400 | 0.0267        |
+| 0.2525 | 21500 | 0.044         |
+| 0.2537 | 21600 | 0.024         |
+| 0.2549 | 21700 | 0.0142        |
+| 0.2561 | 21800 | 0.0263        |
+| 0.2572 | 21900 | 0.0273        |
+| 0.2584 | 22000 | 0.0238        |
+| 0.2596 | 22100 | 0.0185        |
+| 0.2608 | 22200 | 0.0459        |
+| 0.2619 | 22300 | 0.0351        |
+| 0.2631 | 22400 | 0.0498        |
+| 0.2643 | 22500 | 0.0478        |
+| 0.2655 | 22600 | 0.0331        |
+| 0.2666 | 22700 | 0.0276        |
+| 0.2678 | 22800 | 0.025         |
+| 0.2690 | 22900 | 0.0424        |
+| 0.2702 | 23000 | 0.0335        |
+| 0.2713 | 23100 | 0.0401        |
+| 0.2725 | 23200 | 0.038         |
+| 0.2737 | 23300 | 0.0184        |
+| 0.2749 | 23400 | 0.0235        |
+| 0.2760 | 23500 | 0.0361        |
+| 0.2772 | 23600 | 0.0359        |
+| 0.2784 | 23700 | 0.0279        |
+| 0.2796 | 23800 | 0.038         |
+| 0.2807 | 23900 | 0.0198        |
+| 0.2819 | 24000 | 0.0466        |
+| 0.2831 | 24100 | 0.0297        |
+| 0.2843 | 24200 | 0.0189        |
+| 0.2854 | 24300 | 0.0418        |
+| 0.2866 | 24400 | 0.0247        |
+| 0.2878 | 24500 | 0.054         |
+| 0.2890 | 24600 | 0.0449        |
+| 0.2901 | 24700 | 0.0532        |
+| 0.2913 | 24800 | 0.0317        |
+| 0.2925 | 24900 | 0.0427        |
+| 0.2937 | 25000 | 0.0282        |
+| 0.2948 | 25100 | 0.029         |
+| 0.2960 | 25200 | 0.0298        |
+| 0.2972 | 25300 | 0.0297        |
+| 0.2984 | 25400 | 0.0414        |
+| 0.2995 | 25500 | 0.0297        |
+| 0.3007 | 25600 | 0.0525        |
+| 0.3019 | 25700 | 0.0478        |
+| 0.3031 | 25800 | 0.0287        |
+| 0.3042 | 25900 | 0.0235        |
+| 0.3054 | 26000 | 0.0344        |
+| 0.3066 | 26100 | 0.041         |
+| 0.3078 | 26200 | 0.0325        |
+| 0.3089 | 26300 | 0.0334        |
+| 0.3101 | 26400 | 0.0211        |
+| 0.3113 | 26500 | 0.0461        |
+| 0.3125 | 26600 | 0.025         |
+| 0.3136 | 26700 | 0.0276        |
+| 0.3148 | 26800 | 0.0322        |
+| 0.3160 | 26900 | 0.0261        |
+| 0.3172 | 27000 | 0.0268        |
+| 0.3183 | 27100 | 0.0349        |
+| 0.3195 | 27200 | 0.0303        |
+| 0.3207 | 27300 | 0.026         |
+| 0.3218 | 27400 | 0.0328        |
+| 0.3230 | 27500 | 0.0294        |
+| 0.3242 | 27600 | 0.0275        |
+| 0.3254 | 27700 | 0.0343        |
+| 0.3265 | 27800 | 0.0294        |
+| 0.3277 | 27900 | 0.032         |
+| 0.3289 | 28000 | 0.0221        |
+| 0.3301 | 28100 | 0.0249        |
+| 0.3312 | 28200 | 0.0311        |
+| 0.3324 | 28300 | 0.0257        |
+| 0.3336 | 28400 | 0.0424        |
+| 0.3348 | 28500 | 0.0394        |
+| 0.3359 | 28600 | 0.044         |
+| 0.3371 | 28700 | 0.0271        |
+| 0.3383 | 28800 | 0.0363        |
+| 0.3395 | 28900 | 0.0329        |
+| 0.3406 | 29000 | 0.0383        |
+| 0.3418 | 29100 | 0.0414        |
+| 0.3430 | 29200 | 0.0219        |
+| 0.3442 | 29300 | 0.0137        |
+| 0.3453 | 29400 | 0.0389        |
+| 0.3465 | 29500 | 0.0355        |
+| 0.3477 | 29600 | 0.0105        |
+| 0.3489 | 29700 | 0.0347        |
+| 0.3500 | 29800 | 0.037         |
+| 0.3512 | 29900 | 0.0333        |
+| 0.3524 | 30000 | 0.0164        |
+| 0.3536 | 30100 | 0.0336        |
+| 0.3547 | 30200 | 0.0345        |
+| 0.3559 | 30300 | 0.0359        |
+| 0.3571 | 30400 | 0.0343        |
+| 0.3583 | 30500 | 0.0528        |
+| 0.3594 | 30600 | 0.0332        |
+| 0.3606 | 30700 | 0.0487        |
+| 0.3618 | 30800 | 0.0302        |
+| 0.3630 | 30900 | 0.037         |
+| 0.3641 | 31000 | 0.0339        |
+| 0.3653 | 31100 | 0.0359        |
+| 0.3665 | 31200 | 0.0403        |
+| 0.3677 | 31300 | 0.0376        |
+| 0.3688 | 31400 | 0.0367        |
+| 0.3700 | 31500 | 0.0452        |
+| 0.3712 | 31600 | 0.023         |
+| 0.3724 | 31700 | 0.0281        |
+| 0.3735 | 31800 | 0.0297        |
+| 0.3747 | 31900 | 0.0353        |
+| 0.3759 | 32000 | 0.0215        |
+| 0.3771 | 32100 | 0.0234        |
+| 0.3782 | 32200 | 0.0245        |
+| 0.3794 | 32300 | 0.0485        |
+| 0.3806 | 32400 | 0.0249        |
+| 0.3818 | 32500 | 0.021         |
+| 0.3829 | 32600 | 0.0381        |
+| 0.3841 | 32700 | 0.0332        |
+| 0.3853 | 32800 | 0.0263        |
+| 0.3865 | 32900 | 0.0346        |
+| 0.3876 | 33000 | 0.0401        |
+| 0.3888 | 33100 | 0.0318        |
+| 0.3900 | 33200 | 0.0224        |
+| 0.3912 | 33300 | 0.0225        |
+| 0.3923 | 33400 | 0.0265        |
+| 0.3935 | 33500 | 0.0204        |
+| 0.3947 | 33600 | 0.0321        |
+| 0.3959 | 33700 | 0.0188        |
+| 0.3970 | 33800 | 0.0338        |
+| 0.3982 | 33900 | 0.0309        |
+| 0.3994 | 34000 | 0.0233        |
+| 0.4005 | 34100 | 0.0303        |
+| 0.4017 | 34200 | 0.0387        |
+| 0.4029 | 34300 | 0.0255        |
+| 0.4041 | 34400 | 0.0212        |
+| 0.4052 | 34500 | 0.0324        |
+| 0.4064 | 34600 | 0.0412        |
+| 0.4076 | 34700 | 0.0203        |
+| 0.4088 | 34800 | 0.0211        |
+| 0.4099 | 34900 | 0.031         |
+| 0.4111 | 35000 | 0.0178        |
+| 0.4123 | 35100 | 0.0222        |
+| 0.4135 | 35200 | 0.018         |
+| 0.4146 | 35300 | 0.0274        |
+| 0.4158 | 35400 | 0.0364        |
+| 0.4170 | 35500 | 0.0254        |
+| 0.4182 | 35600 | 0.0219        |
+| 0.4193 | 35700 | 0.0352        |
+| 0.4205 | 35800 | 0.0324        |
+| 0.4217 | 35900 | 0.026         |
+| 0.4229 | 36000 | 0.0212        |
+| 0.4240 | 36100 | 0.0326        |
+| 0.4252 | 36200 | 0.0332        |
+| 0.4264 | 36300 | 0.0358        |
+| 0.4276 | 36400 | 0.0301        |
+| 0.4287 | 36500 | 0.0328        |
+| 0.4299 | 36600 | 0.0289        |
+| 0.4311 | 36700 | 0.0351        |
+| 0.4323 | 36800 | 0.0331        |
+| 0.4334 | 36900 | 0.0209        |
+| 0.4346 | 37000 | 0.0392        |
+| 0.4358 | 37100 | 0.0171        |
+| 0.4370 | 37200 | 0.035         |
+| 0.4381 | 37300 | 0.0395        |
+| 0.4393 | 37400 | 0.0437        |
+| 0.4405 | 37500 | 0.0355        |
+| 0.4417 | 37600 | 0.0383        |
+| 0.4428 | 37700 | 0.0227        |
+| 0.4440 | 37800 | 0.0286        |
+| 0.4452 | 37900 | 0.0337        |
+| 0.4464 | 38000 | 0.0514        |
+| 0.4475 | 38100 | 0.0299        |
+| 0.4487 | 38200 | 0.0343        |
+| 0.4499 | 38300 | 0.025         |
+| 0.4511 | 38400 | 0.0193        |
+| 0.4522 | 38500 | 0.0293        |
+| 0.4534 | 38600 | 0.0159        |
+| 0.4546 | 38700 | 0.0183        |
+| 0.4558 | 38800 | 0.0226        |
+| 0.4569 | 38900 | 0.0437        |
+| 0.4581 | 39000 | 0.0242        |
+| 0.4593 | 39100 | 0.0396        |
+| 0.4605 | 39200 | 0.0414        |
+| 0.4616 | 39300 | 0.0337        |
+| 0.4628 | 39400 | 0.035         |
+| 0.4640 | 39500 | 0.0175        |
+| 0.4652 | 39600 | 0.0228        |
+| 0.4663 | 39700 | 0.019         |
+| 0.4675 | 39800 | 0.0402        |
+| 0.4687 | 39900 | 0.0177        |
+| 0.4699 | 40000 | 0.0287        |
+| 0.4710 | 40100 | 0.0262        |
+| 0.4722 | 40200 | 0.0347        |
+| 0.4734 | 40300 | 0.0249        |
+| 0.4746 | 40400 | 0.0217        |
+| 0.4757 | 40500 | 0.0258        |
+| 0.4769 | 40600 | 0.0336        |
+| 0.4781 | 40700 | 0.0391        |
+| 0.4793 | 40800 | 0.042         |
+| 0.4804 | 40900 | 0.03          |
+| 0.4816 | 41000 | 0.0205        |
+| 0.4828 | 41100 | 0.0273        |
+| 0.4839 | 41200 | 0.0564        |
+| 0.4851 | 41300 | 0.0311        |
+| 0.4863 | 41400 | 0.0333        |
+| 0.4875 | 41500 | 0.0162        |
+| 0.4886 | 41600 | 0.0414        |
+| 0.4898 | 41700 | 0.044         |
+| 0.4910 | 41800 | 0.0411        |
+| 0.4922 | 41900 | 0.0384        |
+| 0.4933 | 42000 | 0.0269        |
+| 0.4945 | 42100 | 0.0414        |
+| 0.4957 | 42200 | 0.0175        |
+| 0.4969 | 42300 | 0.0223        |
+| 0.4980 | 42400 | 0.0354        |
+| 0.4992 | 42500 | 0.0338        |
+| 0.5004 | 42600 | 0.0182        |
+| 0.5016 | 42700 | 0.0217        |
+| 0.5027 | 42800 | 0.0227        |
+| 0.5039 | 42900 | 0.0247        |
+| 0.5051 | 43000 | 0.0238        |
+| 0.5063 | 43100 | 0.0357        |
+| 0.5074 | 43200 | 0.0237        |
+| 0.5086 | 43300 | 0.0308        |
+| 0.5098 | 43400 | 0.0294        |
+| 0.5110 | 43500 | 0.0258        |
+| 0.5121 | 43600 | 0.0234        |
+| 0.5133 | 43700 | 0.0324        |
+| 0.5145 | 43800 | 0.0334        |
+| 0.5157 | 43900 | 0.0256        |
+| 0.5168 | 44000 | 0.0243        |
+| 0.5180 | 44100 | 0.0231        |
+| 0.5192 | 44200 | 0.0312        |
+| 0.5204 | 44300 | 0.0278        |
+| 0.5215 | 44400 | 0.0432        |
+| 0.5227 | 44500 | 0.0413        |
+| 0.5239 | 44600 | 0.047         |
+| 0.5251 | 44700 | 0.0384        |
+| 0.5262 | 44800 | 0.0181        |
+| 0.5274 | 44900 | 0.0303        |
+| 0.5286 | 45000 | 0.0297        |
+| 0.5298 | 45100 | 0.0292        |
+| 0.5309 | 45200 | 0.033         |
+| 0.5321 | 45300 | 0.0299        |
+| 0.5333 | 45400 | 0.0269        |
+| 0.5345 | 45500 | 0.0255        |
+| 0.5356 | 45600 | 0.0395        |
+| 0.5368 | 45700 | 0.0302        |
+| 0.5380 | 45800 | 0.0237        |
+| 0.5392 | 45900 | 0.0228        |
+| 0.5403 | 46000 | 0.0329        |
+| 0.5415 | 46100 | 0.0265        |
+| 0.5427 | 46200 | 0.0187        |
+| 0.5439 | 46300 | 0.0358        |
+| 0.5450 | 46400 | 0.0319        |
+| 0.5462 | 46500 | 0.0292        |
+| 0.5474 | 46600 | 0.0366        |
+| 0.5486 | 46700 | 0.0369        |
+| 0.5497 | 46800 | 0.0219        |
+| 0.5509 | 46900 | 0.0339        |
+| 0.5521 | 47000 | 0.0383        |
+| 0.5533 | 47100 | 0.0316        |
+| 0.5544 | 47200 | 0.0374        |
+| 0.5556 | 47300 | 0.0199        |
+| 0.5568 | 47400 | 0.0279        |
+| 0.5580 | 47500 | 0.04          |
+| 0.5591 | 47600 | 0.0276        |
+| 0.5603 | 47700 | 0.0281        |
+| 0.5615 | 47800 | 0.0288        |
+| 0.5626 | 47900 | 0.0256        |
+| 0.5638 | 48000 | 0.0262        |
+| 0.5650 | 48100 | 0.0264        |
+| 0.5662 | 48200 | 0.0222        |
+| 0.5673 | 48300 | 0.0202        |
+| 0.5685 | 48400 | 0.0233        |
+| 0.5697 | 48500 | 0.034         |
+| 0.5709 | 48600 | 0.0354        |
+| 0.5720 | 48700 | 0.0455        |
+| 0.5732 | 48800 | 0.0384        |
+| 0.5744 | 48900 | 0.0362        |
+| 0.5756 | 49000 | 0.0249        |
+| 0.5767 | 49100 | 0.0392        |
+| 0.5779 | 49200 | 0.0279        |
+| 0.5791 | 49300 | 0.0255        |
+| 0.5803 | 49400 | 0.0254        |
+| 0.5814 | 49500 | 0.0187        |
+| 0.5826 | 49600 | 0.0215        |
+| 0.5838 | 49700 | 0.0407        |
+| 0.5850 | 49800 | 0.0158        |
+| 0.5861 | 49900 | 0.0404        |
+| 0.5873 | 50000 | 0.0303        |
+| 0.5885 | 50100 | 0.0296        |
+| 0.5897 | 50200 | 0.0307        |
+| 0.5908 | 50300 | 0.0217        |
+| 0.5920 | 50400 | 0.0436        |
+| 0.5932 | 50500 | 0.0253        |
+| 0.5944 | 50600 | 0.0191        |
+| 0.5955 | 50700 | 0.032         |
+| 0.5967 | 50800 | 0.0399        |
+| 0.5979 | 50900 | 0.0346        |
+| 0.5991 | 51000 | 0.031         |
+| 0.6002 | 51100 | 0.0214        |
+| 0.6014 | 51200 | 0.0134        |
+| 0.6026 | 51300 | 0.0337        |
+| 0.6038 | 51400 | 0.0394        |
+| 0.6049 | 51500 | 0.0359        |
+| 0.6061 | 51600 | 0.019         |
+| 0.6073 | 51700 | 0.0145        |
+| 0.6085 | 51800 | 0.0157        |
+| 0.6096 | 51900 | 0.0496        |
+| 0.6108 | 52000 | 0.0113        |
+| 0.6120 | 52100 | 0.0293        |
+| 0.6132 | 52200 | 0.0165        |
+| 0.6143 | 52300 | 0.03          |
+| 0.6155 | 52400 | 0.0266        |
+| 0.6167 | 52500 | 0.0244        |
+| 0.6179 | 52600 | 0.0234        |
+| 0.6190 | 52700 | 0.0354        |
+| 0.6202 | 52800 | 0.0176        |
+| 0.6214 | 52900 | 0.0377        |
+| 0.6226 | 53000 | 0.0374        |
+| 0.6237 | 53100 | 0.0147        |
+| 0.6249 | 53200 | 0.0408        |
+| 0.6261 | 53300 | 0.0281        |
+| 0.6273 | 53400 | 0.0474        |
+| 0.6284 | 53500 | 0.0389        |
+| 0.6296 | 53600 | 0.0294        |
+| 0.6308 | 53700 | 0.0316        |
+| 0.6320 | 53800 | 0.0314        |
+| 0.6331 | 53900 | 0.0246        |
+| 0.6343 | 54000 | 0.0243        |
+| 0.6355 | 54100 | 0.0138        |
+| 0.6367 | 54200 | 0.0242        |
+| 0.6378 | 54300 | 0.0164        |
+| 0.6390 | 54400 | 0.0279        |
+| 0.6402 | 54500 | 0.0195        |
+| 0.6413 | 54600 | 0.0202        |
+| 0.6425 | 54700 | 0.0239        |
+| 0.6437 | 54800 | 0.0287        |
+| 0.6449 | 54900 | 0.0186        |
+| 0.6460 | 55000 | 0.0141        |
+| 0.6472 | 55100 | 0.0182        |
+| 0.6484 | 55200 | 0.0275        |
+| 0.6496 | 55300 | 0.0227        |
+| 0.6507 | 55400 | 0.027         |
+| 0.6519 | 55500 | 0.0242        |
+| 0.6531 | 55600 | 0.0179        |
+| 0.6543 | 55700 | 0.0245        |
+| 0.6554 | 55800 | 0.0288        |
+| 0.6566 | 55900 | 0.0189        |
+| 0.6578 | 56000 | 0.0336        |
+| 0.6590 | 56100 | 0.0328        |
+| 0.6601 | 56200 | 0.0295        |
+| 0.6613 | 56300 | 0.032         |
+| 0.6625 | 56400 | 0.0256        |
+| 0.6637 | 56500 | 0.0387        |
+| 0.6648 | 56600 | 0.031         |
+| 0.6660 | 56700 | 0.0188        |
+| 0.6672 | 56800 | 0.028         |
+| 0.6684 | 56900 | 0.0397        |
+| 0.6695 | 57000 | 0.0285        |
+| 0.6707 | 57100 | 0.033         |
+| 0.6719 | 57200 | 0.0281        |
+| 0.6731 | 57300 | 0.0196        |
+| 0.6742 | 57400 | 0.025         |
+| 0.6754 | 57500 | 0.0397        |
+| 0.6766 | 57600 | 0.0403        |
+| 0.6778 | 57700 | 0.022         |
+| 0.6789 | 57800 | 0.0392        |
+| 0.6801 | 57900 | 0.0254        |
+| 0.6813 | 58000 | 0.0316        |
+| 0.6825 | 58100 | 0.0186        |
+| 0.6836 | 58200 | 0.0271        |
+| 0.6848 | 58300 | 0.035         |
+| 0.6860 | 58400 | 0.0322        |
+| 0.6872 | 58500 | 0.0147        |
+| 0.6883 | 58600 | 0.0314        |
+| 0.6895 | 58700 | 0.0214        |
+| 0.6907 | 58800 | 0.0177        |
+| 0.6919 | 58900 | 0.0307        |
+| 0.6930 | 59000 | 0.0246        |
+| 0.6942 | 59100 | 0.0125        |
+| 0.6954 | 59200 | 0.0232        |
+| 0.6966 | 59300 | 0.0325        |
+| 0.6977 | 59400 | 0.0253        |
+| 0.6989 | 59500 | 0.0151        |
+| 0.7001 | 59600 | 0.0261        |
+| 0.7013 | 59700 | 0.0253        |
+| 0.7024 | 59800 | 0.0124        |
+| 0.7036 | 59900 | 0.0298        |
+| 0.7048 | 60000 | 0.0254        |
+| 0.7060 | 60100 | 0.0262        |
+| 0.7071 | 60200 | 0.0274        |
+| 0.7083 | 60300 | 0.0344        |
+| 0.7095 | 60400 | 0.03          |
+| 0.7107 | 60500 | 0.0312        |
+| 0.7118 | 60600 | 0.0354        |
+| 0.7130 | 60700 | 0.0334        |
+| 0.7142 | 60800 | 0.0325        |
+| 0.7154 | 60900 | 0.0236        |
+| 0.7165 | 61000 | 0.0266        |
+| 0.7177 | 61100 | 0.0183        |
+| 0.7189 | 61200 | 0.045         |
+| 0.7200 | 61300 | 0.0174        |
+| 0.7212 | 61400 | 0.0518        |
+| 0.7224 | 61500 | 0.0247        |
+| 0.7236 | 61600 | 0.0255        |
+| 0.7247 | 61700 | 0.0209        |
+| 0.7259 | 61800 | 0.0206        |
+| 0.7271 | 61900 | 0.0306        |
+| 0.7283 | 62000 | 0.0215        |
+| 0.7294 | 62100 | 0.0241        |
+| 0.7306 | 62200 | 0.0324        |
+| 0.7318 | 62300 | 0.0433        |
+| 0.7330 | 62400 | 0.0238        |
+| 0.7341 | 62500 | 0.0302        |
+| 0.7353 | 62600 | 0.0282        |
+| 0.7365 | 62700 | 0.0371        |
+| 0.7377 | 62800 | 0.0397        |
+| 0.7388 | 62900 | 0.0488        |
+| 0.7400 | 63000 | 0.032         |
+| 0.7412 | 63100 | 0.0161        |
+| 0.7424 | 63200 | 0.0351        |
+| 0.7435 | 63300 | 0.0282        |
+| 0.7447 | 63400 | 0.0221        |
+| 0.7459 | 63500 | 0.0275        |
+| 0.7471 | 63600 | 0.0198        |
+| 0.7482 | 63700 | 0.0339        |
+| 0.7494 | 63800 | 0.0285        |
+| 0.7506 | 63900 | 0.0314        |
+| 0.7518 | 64000 | 0.0216        |
+| 0.7529 | 64100 | 0.0383        |
+| 0.7541 | 64200 | 0.0386        |
+| 0.7553 | 64300 | 0.0305        |
+| 0.7565 | 64400 | 0.0265        |
+| 0.7576 | 64500 | 0.0288        |
+| 0.7588 | 64600 | 0.0125        |
+| 0.7600 | 64700 | 0.0212        |
+| 0.7612 | 64800 | 0.0242        |
+| 0.7623 | 64900 | 0.0384        |
+| 0.7635 | 65000 | 0.0163        |
+| 0.7647 | 65100 | 0.0132        |
+| 0.7659 | 65200 | 0.0209        |
+| 0.7670 | 65300 | 0.0408        |
+| 0.7682 | 65400 | 0.0312        |
+| 0.7694 | 65500 | 0.0382        |
+| 0.7706 | 65600 | 0.0217        |
+| 0.7717 | 65700 | 0.0384        |
+| 0.7729 | 65800 | 0.0267        |
+| 0.7741 | 65900 | 0.047         |
+| 0.7753 | 66000 | 0.021         |
+| 0.7764 | 66100 | 0.0138        |
+| 0.7776 | 66200 | 0.0308        |
+| 0.7788 | 66300 | 0.0193        |
+| 0.7800 | 66400 | 0.0285        |
+| 0.7811 | 66500 | 0.0235        |
+| 0.7823 | 66600 | 0.0281        |
+| 0.7835 | 66700 | 0.0407        |
+| 0.7847 | 66800 | 0.0269        |
+| 0.7858 | 66900 | 0.0346        |
+| 0.7870 | 67000 | 0.0223        |
+| 0.7882 | 67100 | 0.0278        |
+| 0.7894 | 67200 | 0.0255        |
+| 0.7905 | 67300 | 0.014         |
+| 0.7917 | 67400 | 0.0248        |
+| 0.7929 | 67500 | 0.022         |
+| 0.7941 | 67600 | 0.0292        |
+| 0.7952 | 67700 | 0.038         |
+| 0.7964 | 67800 | 0.0158        |
+| 0.7976 | 67900 | 0.0212        |
+| 0.7988 | 68000 | 0.0405        |
+| 0.7999 | 68100 | 0.029         |
+| 0.8011 | 68200 | 0.0379        |
+| 0.8023 | 68300 | 0.0256        |
+| 0.8034 | 68400 | 0.0263        |
+| 0.8046 | 68500 | 0.0214        |
+| 0.8058 | 68600 | 0.0224        |
+| 0.8070 | 68700 | 0.0159        |
+| 0.8081 | 68800 | 0.0302        |
+| 0.8093 | 68900 | 0.0313        |
+| 0.8105 | 69000 | 0.0395        |
+| 0.8117 | 69100 | 0.0296        |
+| 0.8128 | 69200 | 0.0353        |
+| 0.8140 | 69300 | 0.025         |
+| 0.8152 | 69400 | 0.0246        |
+| 0.8164 | 69500 | 0.0312        |
+| 0.8175 | 69600 | 0.0199        |
+| 0.8187 | 69700 | 0.0225        |
+| 0.8199 | 69800 | 0.0254        |
+| 0.8211 | 69900 | 0.0095        |
+| 0.8222 | 70000 | 0.0326        |
+| 0.8234 | 70100 | 0.0355        |
+| 0.8246 | 70200 | 0.0368        |
+| 0.8258 | 70300 | 0.0339        |
+| 0.8269 | 70400 | 0.0278        |
+| 0.8281 | 70500 | 0.0249        |
+| 0.8293 | 70600 | 0.0379        |
+| 0.8305 | 70700 | 0.0368        |
+| 0.8316 | 70800 | 0.0146        |
+| 0.8328 | 70900 | 0.0153        |
+| 0.8340 | 71000 | 0.0352        |
+| 0.8352 | 71100 | 0.0248        |
+| 0.8363 | 71200 | 0.0255        |
+| 0.8375 | 71300 | 0.0306        |
+| 0.8387 | 71400 | 0.0293        |
+| 0.8399 | 71500 | 0.0303        |
+| 0.8410 | 71600 | 0.0244        |
+| 0.8422 | 71700 | 0.0174        |
+| 0.8434 | 71800 | 0.0241        |
+| 0.8446 | 71900 | 0.0276        |
+| 0.8457 | 72000 | 0.0359        |
+| 0.8469 | 72100 | 0.0257        |
+| 0.8481 | 72200 | 0.0344        |
+| 0.8493 | 72300 | 0.0275        |
+| 0.8504 | 72400 | 0.022         |
+| 0.8516 | 72500 | 0.0275        |
+| 0.8528 | 72600 | 0.0317        |
+| 0.8540 | 72700 | 0.0386        |
+| 0.8551 | 72800 | 0.0421        |
+| 0.8563 | 72900 | 0.0259        |
+| 0.8575 | 73000 | 0.0244        |
+| 0.8587 | 73100 | 0.0231        |
+| 0.8598 | 73200 | 0.0373        |
+| 0.8610 | 73300 | 0.0296        |
+| 0.8622 | 73400 | 0.024         |
+| 0.8634 | 73500 | 0.0382        |
+| 0.8645 | 73600 | 0.0223        |
+| 0.8657 | 73700 | 0.0254        |
+| 0.8669 | 73800 | 0.0259        |
+| 0.8681 | 73900 | 0.0171        |
+| 0.8692 | 74000 | 0.0268        |
+| 0.8704 | 74100 | 0.0196        |
+| 0.8716 | 74200 | 0.0206        |
+| 0.8728 | 74300 | 0.0411        |
+| 0.8739 | 74400 | 0.039         |
+| 0.8751 | 74500 | 0.0197        |
+| 0.8763 | 74600 | 0.0144        |
+| 0.8775 | 74700 | 0.0231        |
+| 0.8786 | 74800 | 0.0217        |
+| 0.8798 | 74900 | 0.0244        |
+| 0.8810 | 75000 | 0.0291        |
+| 0.8821 | 75100 | 0.0243        |
+| 0.8833 | 75200 | 0.0294        |
+| 0.8845 | 75300 | 0.0129        |
+| 0.8857 | 75400 | 0.0291        |
+| 0.8868 | 75500 | 0.0273        |
+| 0.8880 | 75600 | 0.0297        |
+| 0.8892 | 75700 | 0.0266        |
+| 0.8904 | 75800 | 0.0374        |
+| 0.8915 | 75900 | 0.0225        |
+| 0.8927 | 76000 | 0.0223        |
+| 0.8939 | 76100 | 0.0229        |
+| 0.8951 | 76200 | 0.0306        |
+| 0.8962 | 76300 | 0.0238        |
+| 0.8974 | 76400 | 0.0197        |
+| 0.8986 | 76500 | 0.0265        |
+| 0.8998 | 76600 | 0.0411        |
+| 0.9009 | 76700 | 0.022         |
+| 0.9021 | 76800 | 0.0151        |
+| 0.9033 | 76900 | 0.0251        |
+| 0.9045 | 77000 | 0.0211        |
+| 0.9056 | 77100 | 0.0302        |
+| 0.9068 | 77200 | 0.0229        |
+| 0.9080 | 77300 | 0.0398        |
+| 0.9092 | 77400 | 0.0174        |
+| 0.9103 | 77500 | 0.0327        |
+| 0.9115 | 77600 | 0.0258        |
+| 0.9127 | 77700 | 0.026         |
+| 0.9139 | 77800 | 0.0251        |
+| 0.9150 | 77900 | 0.0351        |
+| 0.9162 | 78000 | 0.0315        |
+| 0.9174 | 78100 | 0.0342        |
+| 0.9186 | 78200 | 0.0244        |
+| 0.9197 | 78300 | 0.0171        |
+| 0.9209 | 78400 | 0.043         |
+| 0.9221 | 78500 | 0.0189        |
+| 0.9233 | 78600 | 0.0241        |
+| 0.9244 | 78700 | 0.0266        |
+| 0.9256 | 78800 | 0.0173        |
+| 0.9268 | 78900 | 0.0238        |
+| 0.9280 | 79000 | 0.0222        |
+| 0.9291 | 79100 | 0.0416        |
+| 0.9303 | 79200 | 0.0377        |
+| 0.9315 | 79300 | 0.0311        |
+| 0.9327 | 79400 | 0.0251        |
+| 0.9338 | 79500 | 0.0208        |
+| 0.9350 | 79600 | 0.0274        |
+| 0.9362 | 79700 | 0.0327        |
+| 0.9374 | 79800 | 0.0258        |
+| 0.9385 | 79900 | 0.0175        |
+| 0.9397 | 80000 | 0.0297        |
+| 0.9409 | 80100 | 0.0182        |
+| 0.9421 | 80200 | 0.0279        |
+| 0.9432 | 80300 | 0.0197        |
+| 0.9444 | 80400 | 0.0122        |
+| 0.9456 | 80500 | 0.0293        |
+| 0.9468 | 80600 | 0.0126        |
+| 0.9479 | 80700 | 0.0317        |
+| 0.9491 | 80800 | 0.0276        |
+| 0.9503 | 80900 | 0.025         |
+| 0.9515 | 81000 | 0.0264        |
+| 0.9526 | 81100 | 0.0236        |
+| 0.9538 | 81200 | 0.0273        |
+| 0.9550 | 81300 | 0.0276        |
+| 0.9562 | 81400 | 0.0262        |
+| 0.9573 | 81500 | 0.0167        |
+| 0.9585 | 81600 | 0.0313        |
+| 0.9597 | 81700 | 0.0253        |
+| 0.9608 | 81800 | 0.0207        |
+| 0.9620 | 81900 | 0.0199        |
+| 0.9632 | 82000 | 0.0411        |
+| 0.9644 | 82100 | 0.0302        |
+| 0.9655 | 82200 | 0.0244        |
+| 0.9667 | 82300 | 0.0264        |
+| 0.9679 | 82400 | 0.0213        |
+| 0.9691 | 82500 | 0.0137        |
+| 0.9702 | 82600 | 0.019         |
+| 0.9714 | 82700 | 0.0318        |
+| 0.9726 | 82800 | 0.037         |
+| 0.9738 | 82900 | 0.0249        |
+| 0.9749 | 83000 | 0.0193        |
+| 0.9761 | 83100 | 0.0243        |
+| 0.9773 | 83200 | 0.0222        |
+| 0.9785 | 83300 | 0.0271        |
+| 0.9796 | 83400 | 0.0147        |
+| 0.9808 | 83500 | 0.0211        |
+| 0.9820 | 83600 | 0.0248        |
+| 0.9832 | 83700 | 0.0216        |
+| 0.9843 | 83800 | 0.0238        |
+| 0.9855 | 83900 | 0.0231        |
+| 0.9867 | 84000 | 0.0308        |
+| 0.9879 | 84100 | 0.0282        |
+| 0.9890 | 84200 | 0.0217        |
+| 0.9902 | 84300 | 0.021         |
+| 0.9914 | 84400 | 0.0162        |
+| 0.9926 | 84500 | 0.0288        |
+| 0.9937 | 84600 | 0.0343        |
+| 0.9949 | 84700 | 0.0192        |
+| 0.9961 | 84800 | 0.0256        |
+| 0.9973 | 84900 | 0.0181        |
+| 0.9984 | 85000 | 0.0186        |
+| 0.9996 | 85100 | 0.0206        |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.3.1
+- Transformers: 4.47.0
+- PyTorch: 2.5.1+cu121
+- Accelerate: 1.2.1
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## 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_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.47.0",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

modules.json

@@ -0,0 +1,26 @@
+[
+  {
+    "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_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 3,
+    "name": "3",
+    "path": "3_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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