Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:172826
+- loss:CosineSimilarityLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: How do you make Yahoo your homepage?
+  sentences:
+  - چگونه ویکی پدیا بدون تبلیغ در وب سایت خود درآمد کسب می کند؟
+  - چگونه می توانم برای امتحان INS 21 آماده شوم؟
+  - How can I make Yahoo my homepage on my browser?
+- source_sentence: کدام VPN رایگان در چین کار می کند؟
+  sentences:
+  - VPN های رایگان که در چین کار می کنند چیست؟
+  - How can I stop masturbations?
+  - آیا مدرسه خلاقیت را می کشد؟
+- source_sentence: چند روش خوب برای کاهش وزن چیست؟
+  sentences:
+  - چگونه می توانم یک کتاب خوب بنویسم؟
+  - من اضافه وزن دارمچگونه می توانم وزن کم کنم؟
+  - آیا می توانید ببینید چه کسی داستانهای اینستاگرام شما را مشاهده می کند؟
+- source_sentence: چگونه می توان یک Dell Inspiron 1525 را به تنظیمات کارخانه بازگرداند؟
+  sentences:
+  - چگونه می توان یک Dell Inspiron B130 را به تنظیمات کارخانه بازگرداند؟
+  - مبدل چیست؟
+  - چگونه زندگی شما بعد از تشخیص HIV مثبت تغییر کرد؟
+- source_sentence: داشتن هزاران دنبال کننده در Quora چگونه است؟
+  sentences:
+  - چگونه Airprint HP OfficeJet 4620 با HP LaserJet Enterprise M606X مقایسه می شود؟
+  - چه چیزی است که ده ها هزار دنبال کننده در Quora داشته باشید؟
+  - اگر هند واردات همه محصولات چینی را ممنوع کند ، چه می شود؟
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: 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("codersan/validadted_allMiniLM_withCosSimb")
+# Run inference
+sentences = [
+    'داشتن هزاران دنبال کننده در Quora چگونه است؟',
+    'چه چیزی است که ده ها هزار دنبال کننده در Quora داشته باشید؟',
+    'چگونه Airprint HP OfficeJet 4620 با HP LaserJet Enterprise M606X مقایسه می شود؟',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# 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: 172,826 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: 8 tokens</li><li>mean: 40.84 tokens</li><li>max: 222 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 41.92 tokens</li><li>max: 144 tokens</li></ul> | <ul><li>min: 0.76</li><li>mean: 0.95</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                          | sentence2                                                      | score                           |
+  |:-------------------------------------------------------------------|:---------------------------------------------------------------|:--------------------------------|
+  | <code>تفاوت بین تحلیلگر تحقیقات بازار و تحلیلگر تجارت چیست؟</code> | <code>تفاوت بین تحقیقات بازاریابی و تحلیلگر تجارت چیست؟</code> | <code>0.982593297958374</code>  |
+  | <code>خوردن چه چیزی باعث دل درد میشود؟</code>                      | <code>چه چیزی باعث رفع دل درد میشود؟</code>                    | <code>0.9582258462905884</code> |
+  | <code>بهترین نرم افزار ویرایش ویدیویی کدام است؟</code>             | <code>بهترین نرم افزار برای ویرایش ویدیو چیست؟</code>          | <code>0.9890836477279663</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 12
+- `learning_rate`: 1e-05
+- `weight_decay`: 0.01
+- `num_train_epochs`: 10
+- `warmup_ratio`: 0.1
+- `push_to_hub`: True
+- `hub_model_id`: codersan/validadted_allMiniLM_withCosSimb
+- `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`: 1e-05
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 10
+- `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/validadted_allMiniLM_withCosSimb
+- `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      | -             |
+| 0.0069 | 100    | 0.0218        |
+| 0.0139 | 200    | 0.0225        |
+| 0.0208 | 300    | 0.0136        |
+| 0.0278 | 400    | 0.0097        |
+| 0.0347 | 500    | 0.0083        |
+| 0.0417 | 600    | 0.0056        |
+| 0.0486 | 700    | 0.0065        |
+| 0.0555 | 800    | 0.0047        |
+| 0.0625 | 900    | 0.005         |
+| 0.0694 | 1000   | 0.0051        |
+| 0.0764 | 1100   | 0.0074        |
+| 0.0833 | 1200   | 0.0049        |
+| 0.0903 | 1300   | 0.0051        |
+| 0.0972 | 1400   | 0.0044        |
+| 0.1041 | 1500   | 0.0055        |
+| 0.1111 | 1600   | 0.005         |
+| 0.1180 | 1700   | 0.005         |
+| 0.1250 | 1800   | 0.0038        |
+| 0.1319 | 1900   | 0.0033        |
+| 0.1389 | 2000   | 0.0025        |
+| 0.1458 | 2100   | 0.0034        |
+| 0.1527 | 2200   | 0.0034        |
+| 0.1597 | 2300   | 0.0027        |
+| 0.1666 | 2400   | 0.0022        |
+| 0.1736 | 2500   | 0.0028        |
+| 0.1805 | 2600   | 0.0018        |
+| 0.1875 | 2700   | 0.0018        |
+| 0.1944 | 2800   | 0.0018        |
+| 0.2013 | 2900   | 0.0015        |
+| 0.2083 | 3000   | 0.0015        |
+| 0.2152 | 3100   | 0.0017        |
+| 0.2222 | 3200   | 0.0011        |
+| 0.2291 | 3300   | 0.0012        |
+| 0.2361 | 3400   | 0.0012        |
+| 0.2430 | 3500   | 0.0011        |
+| 0.2499 | 3600   | 0.001         |
+| 0.2569 | 3700   | 0.0008        |
+| 0.2638 | 3800   | 0.0008        |
+| 0.2708 | 3900   | 0.0008        |
+| 0.2777 | 4000   | 0.0009        |
+| 0.2847 | 4100   | 0.0008        |
+| 0.2916 | 4200   | 0.0008        |
+| 0.2985 | 4300   | 0.0007        |
+| 0.3055 | 4400   | 0.0008        |
+| 0.3124 | 4500   | 0.0007        |
+| 0.3194 | 4600   | 0.0007        |
+| 0.3263 | 4700   | 0.0007        |
+| 0.3333 | 4800   | 0.0007        |
+| 0.3402 | 4900   | 0.0006        |
+| 0.3471 | 5000   | 0.0006        |
+| 0.3541 | 5100   | 0.0006        |
+| 0.3610 | 5200   | 0.0006        |
+| 0.3680 | 5300   | 0.0006        |
+| 0.3749 | 5400   | 0.0006        |
+| 0.3819 | 5500   | 0.0006        |
+| 0.3888 | 5600   | 0.0006        |
+| 0.3958 | 5700   | 0.0006        |
+| 0.4027 | 5800   | 0.0006        |
+| 0.4096 | 5900   | 0.0005        |
+| 0.4166 | 6000   | 0.0006        |
+| 0.4235 | 6100   | 0.0006        |
+| 0.4305 | 6200   | 0.0007        |
+| 0.4374 | 6300   | 0.0006        |
+| 0.4444 | 6400   | 0.0006        |
+| 0.4513 | 6500   | 0.0006        |
+| 0.4582 | 6600   | 0.0005        |
+| 0.4652 | 6700   | 0.0006        |
+| 0.4721 | 6800   | 0.0005        |
+| 0.4791 | 6900   | 0.0006        |
+| 0.4860 | 7000   | 0.0006        |
+| 0.4930 | 7100   | 0.0005        |
+| 0.4999 | 7200   | 0.0005        |
+| 0.5068 | 7300   | 0.0005        |
+| 0.5138 | 7400   | 0.0005        |
+| 0.5207 | 7500   | 0.0005        |
+| 0.5277 | 7600   | 0.0006        |
+| 0.5346 | 7700   | 0.0005        |
+| 0.5416 | 7800   | 0.0005        |
+| 0.5485 | 7900   | 0.0005        |
+| 0.5554 | 8000   | 0.0006        |
+| 0.5624 | 8100   | 0.0005        |
+| 0.5693 | 8200   | 0.0005        |
+| 0.5763 | 8300   | 0.0005        |
+| 0.5832 | 8400   | 0.0005        |
+| 0.5902 | 8500   | 0.0005        |
+| 0.5971 | 8600   | 0.0005        |
+| 0.6040 | 8700   | 0.0005        |
+| 0.6110 | 8800   | 0.0005        |
+| 0.6179 | 8900   | 0.0005        |
+| 0.6249 | 9000   | 0.0005        |
+| 0.6318 | 9100   | 0.0005        |
+| 0.6388 | 9200   | 0.0005        |
+| 0.6457 | 9300   | 0.0005        |
+| 0.6526 | 9400   | 0.0005        |
+| 0.6596 | 9500   | 0.0004        |
+| 0.6665 | 9600   | 0.0005        |
+| 0.6735 | 9700   | 0.0005        |
+| 0.6804 | 9800   | 0.0005        |
+| 0.6874 | 9900   | 0.0005        |
+| 0.6943 | 10000  | 0.0005        |
+| 0.7012 | 10100  | 0.0005        |
+| 0.7082 | 10200  | 0.0005        |
+| 0.7151 | 10300  | 0.0005        |
+| 0.7221 | 10400  | 0.0005        |
+| 0.7290 | 10500  | 0.0004        |
+| 0.7360 | 10600  | 0.0004        |
+| 0.7429 | 10700  | 0.0004        |
+| 0.7498 | 10800  | 0.0005        |
+| 0.7568 | 10900  | 0.0005        |
+| 0.7637 | 11000  | 0.0004        |
+| 0.7707 | 11100  | 0.0005        |
+| 0.7776 | 11200  | 0.0005        |
+| 0.7846 | 11300  | 0.0005        |
+| 0.7915 | 11400  | 0.0005        |
+| 0.7984 | 11500  | 0.0005        |
+| 0.8054 | 11600  | 0.0005        |
+| 0.8123 | 11700  | 0.0005        |
+| 0.8193 | 11800  | 0.0004        |
+| 0.8262 | 11900  | 0.0005        |
+| 0.8332 | 12000  | 0.0004        |
+| 0.8401 | 12100  | 0.0004        |
+| 0.8470 | 12200  | 0.0004        |
+| 0.8540 | 12300  | 0.0005        |
+| 0.8609 | 12400  | 0.0004        |
+| 0.8679 | 12500  | 0.0004        |
+| 0.8748 | 12600  | 0.0004        |
+| 0.8818 | 12700  | 0.0004        |
+| 0.8887 | 12800  | 0.0005        |
+| 0.8956 | 12900  | 0.0005        |
+| 0.9026 | 13000  | 0.0005        |
+| 0.9095 | 13100  | 0.0004        |
+| 0.9165 | 13200  | 0.0005        |
+| 0.9234 | 13300  | 0.0004        |
+| 0.9304 | 13400  | 0.0004        |
+| 0.9373 | 13500  | 0.0004        |
+| 0.9442 | 13600  | 0.0004        |
+| 0.9512 | 13700  | 0.0005        |
+| 0.9581 | 13800  | 0.0005        |
+| 0.9651 | 13900  | 0.0004        |
+| 0.9720 | 14000  | 0.0004        |
+| 0.9790 | 14100  | 0.0004        |
+| 0.9859 | 14200  | 0.0004        |
+| 0.9928 | 14300  | 0.0005        |
+| 0.9998 | 14400  | 0.0004        |
+| 1.0067 | 14500  | 0.0004        |
+| 1.0137 | 14600  | 0.0004        |
+| 1.0206 | 14700  | 0.0004        |
+| 1.0276 | 14800  | 0.0004        |
+| 1.0345 | 14900  | 0.0004        |
+| 1.0414 | 15000  | 0.0005        |
+| 1.0484 | 15100  | 0.0004        |
+| 1.0553 | 15200  | 0.0004        |
+| 1.0623 | 15300  | 0.0004        |
+| 1.0692 | 15400  | 0.0004        |
+| 1.0762 | 15500  | 0.0004        |
+| 1.0831 | 15600  | 0.0004        |
+| 1.0901 | 15700  | 0.0004        |
+| 1.0970 | 15800  | 0.0004        |
+| 1.1039 | 15900  | 0.0004        |
+| 1.1109 | 16000  | 0.0004        |
+| 1.1178 | 16100  | 0.0004        |
+| 1.1248 | 16200  | 0.0004        |
+| 1.1317 | 16300  | 0.0004        |
+| 1.1387 | 16400  | 0.0004        |
+| 1.1456 | 16500  | 0.0004        |
+| 1.1525 | 16600  | 0.0004        |
+| 1.1595 | 16700  | 0.0004        |
+| 1.1664 | 16800  | 0.0004        |
+| 1.1734 | 16900  | 0.0004        |
+| 1.1803 | 17000  | 0.0004        |
+| 1.1873 | 17100  | 0.0004        |
+| 1.1942 | 17200  | 0.0004        |
+| 1.2011 | 17300  | 0.0004        |
+| 1.2081 | 17400  | 0.0004        |
+| 1.2150 | 17500  | 0.0004        |
+| 1.2220 | 17600  | 0.0004        |
+| 1.2289 | 17700  | 0.0004        |
+| 1.2359 | 17800  | 0.0004        |
+| 1.2428 | 17900  | 0.0004        |
+| 1.2497 | 18000  | 0.0004        |
+| 1.2567 | 18100  | 0.0004        |
+| 1.2636 | 18200  | 0.0004        |
+| 1.2706 | 18300  | 0.0004        |
+| 1.2775 | 18400  | 0.0004        |
+| 1.2845 | 18500  | 0.0004        |
+| 1.2914 | 18600  | 0.0004        |
+| 1.2983 | 18700  | 0.0003        |
+| 1.3053 | 18800  | 0.0004        |
+| 1.3122 | 18900  | 0.0004        |
+| 1.3192 | 19000  | 0.0004        |
+| 1.3261 | 19100  | 0.0004        |
+| 1.3331 | 19200  | 0.0004        |
+| 1.3400 | 19300  | 0.0004        |
+| 1.3469 | 19400  | 0.0004        |
+| 1.3539 | 19500  | 0.0004        |
+| 1.3608 | 19600  | 0.0004        |
+| 1.3678 | 19700  | 0.0004        |
+| 1.3747 | 19800  | 0.0004        |
+| 1.3817 | 19900  | 0.0004        |
+| 1.3886 | 20000  | 0.0003        |
+| 1.3955 | 20100  | 0.0004        |
+| 1.4025 | 20200  | 0.0004        |
+| 1.4094 | 20300  | 0.0003        |
+| 1.4164 | 20400  | 0.0004        |
+| 1.4233 | 20500  | 0.0004        |
+| 1.4303 | 20600  | 0.0004        |
+| 1.4372 | 20700  | 0.0004        |
+| 1.4441 | 20800  | 0.0004        |
+| 1.4511 | 20900  | 0.0004        |
+| 1.4580 | 21000  | 0.0004        |
+| 1.4650 | 21100  | 0.0004        |
+| 1.4719 | 21200  | 0.0004        |
+| 1.4789 | 21300  | 0.0004        |
+| 1.4858 | 21400  | 0.0004        |
+| 1.4927 | 21500  | 0.0004        |
+| 1.4997 | 21600  | 0.0004        |
+| 1.5066 | 21700  | 0.0003        |
+| 1.5136 | 21800  | 0.0003        |
+| 1.5205 | 21900  | 0.0003        |
+| 1.5275 | 22000  | 0.0004        |
+| 1.5344 | 22100  | 0.0003        |
+| 1.5413 | 22200  | 0.0003        |
+| 1.5483 | 22300  | 0.0004        |
+| 1.5552 | 22400  | 0.0004        |
+| 1.5622 | 22500  | 0.0004        |
+| 1.5691 | 22600  | 0.0004        |
+| 1.5761 | 22700  | 0.0004        |
+| 1.5830 | 22800  | 0.0003        |
+| 1.5899 | 22900  | 0.0003        |
+| 1.5969 | 23000  | 0.0004        |
+| 1.6038 | 23100  | 0.0003        |
+| 1.6108 | 23200  | 0.0003        |
+| 1.6177 | 23300  | 0.0004        |
+| 1.6247 | 23400  | 0.0003        |
+| 1.6316 | 23500  | 0.0004        |
+| 1.6385 | 23600  | 0.0003        |
+| 1.6455 | 23700  | 0.0004        |
+| 1.6524 | 23800  | 0.0003        |
+| 1.6594 | 23900  | 0.0003        |
+| 1.6663 | 24000  | 0.0003        |
+| 1.6733 | 24100  | 0.0003        |
+| 1.6802 | 24200  | 0.0004        |
+| 1.6871 | 24300  | 0.0003        |
+| 1.6941 | 24400  | 0.0003        |
+| 1.7010 | 24500  | 0.0003        |
+| 1.7080 | 24600  | 0.0003        |
+| 1.7149 | 24700  | 0.0003        |
+| 1.7219 | 24800  | 0.0003        |
+| 1.7288 | 24900  | 0.0003        |
+| 1.7357 | 25000  | 0.0003        |
+| 1.7427 | 25100  | 0.0003        |
+| 1.7496 | 25200  | 0.0003        |
+| 1.7566 | 25300  | 0.0003        |
+| 1.7635 | 25400  | 0.0003        |
+| 1.7705 | 25500  | 0.0003        |
+| 1.7774 | 25600  | 0.0003        |
+| 1.7844 | 25700  | 0.0004        |
+| 1.7913 | 25800  | 0.0004        |
+| 1.7982 | 25900  | 0.0004        |
+| 1.8052 | 26000  | 0.0003        |
+| 1.8121 | 26100  | 0.0004        |
+| 1.8191 | 26200  | 0.0003        |
+| 1.8260 | 26300  | 0.0004        |
+| 1.8330 | 26400  | 0.0003        |
+| 1.8399 | 26500  | 0.0003        |
+| 1.8468 | 26600  | 0.0003        |
+| 1.8538 | 26700  | 0.0003        |
+| 1.8607 | 26800  | 0.0003        |
+| 1.8677 | 26900  | 0.0003        |
+| 1.8746 | 27000  | 0.0003        |
+| 1.8816 | 27100  | 0.0003        |
+| 1.8885 | 27200  | 0.0003        |
+| 1.8954 | 27300  | 0.0003        |
+| 1.9024 | 27400  | 0.0004        |
+| 1.9093 | 27500  | 0.0003        |
+| 1.9163 | 27600  | 0.0003        |
+| 1.9232 | 27700  | 0.0003        |
+| 1.9302 | 27800  | 0.0003        |
+| 1.9371 | 27900  | 0.0003        |
+| 1.9440 | 28000  | 0.0003        |
+| 1.9510 | 28100  | 0.0003        |
+| 1.9579 | 28200  | 0.0004        |
+| 1.9649 | 28300  | 0.0003        |
+| 1.9718 | 28400  | 0.0003        |
+| 1.9788 | 28500  | 0.0003        |
+| 1.9857 | 28600  | 0.0003        |
+| 1.9926 | 28700  | 0.0003        |
+| 1.9996 | 28800  | 0.0003        |
+| 2.0065 | 28900  | 0.0003        |
+| 2.0135 | 29000  | 0.0003        |
+| 2.0204 | 29100  | 0.0003        |
+| 2.0274 | 29200  | 0.0003        |
+| 2.0343 | 29300  | 0.0003        |
+| 2.0412 | 29400  | 0.0003        |
+| 2.0482 | 29500  | 0.0003        |
+| 2.0551 | 29600  | 0.0003        |
+| 2.0621 | 29700  | 0.0003        |
+| 2.0690 | 29800  | 0.0003        |
+| 2.0760 | 29900  | 0.0003        |
+| 2.0829 | 30000  | 0.0003        |
+| 2.0898 | 30100  | 0.0003        |
+| 2.0968 | 30200  | 0.0003        |
+| 2.1037 | 30300  | 0.0003        |
+| 2.1107 | 30400  | 0.0003        |
+| 2.1176 | 30500  | 0.0003        |
+| 2.1246 | 30600  | 0.0003        |
+| 2.1315 | 30700  | 0.0003        |
+| 2.1384 | 30800  | 0.0003        |
+| 2.1454 | 30900  | 0.0003        |
+| 2.1523 | 31000  | 0.0003        |
+| 2.1593 | 31100  | 0.0003        |
+| 2.1662 | 31200  | 0.0003        |
+| 2.1732 | 31300  | 0.0003        |
+| 2.1801 | 31400  | 0.0003        |
+| 2.1870 | 31500  | 0.0003        |
+| 2.1940 | 31600  | 0.0003        |
+| 2.2009 | 31700  | 0.0003        |
+| 2.2079 | 31800  | 0.0003        |
+| 2.2148 | 31900  | 0.0003        |
+| 2.2218 | 32000  | 0.0003        |
+| 2.2287 | 32100  | 0.0003        |
+| 2.2356 | 32200  | 0.0003        |
+| 2.2426 | 32300  | 0.0003        |
+| 2.2495 | 32400  | 0.0003        |
+| 2.2565 | 32500  | 0.0003        |
+| 2.2634 | 32600  | 0.0003        |
+| 2.2704 | 32700  | 0.0003        |
+| 2.2773 | 32800  | 0.0003        |
+| 2.2842 | 32900  | 0.0003        |
+| 2.2912 | 33000  | 0.0003        |
+| 2.2981 | 33100  | 0.0003        |
+| 2.3051 | 33200  | 0.0003        |
+| 2.3120 | 33300  | 0.0003        |
+| 2.3190 | 33400  | 0.0003        |
+| 2.3259 | 33500  | 0.0003        |
+| 2.3328 | 33600  | 0.0003        |
+| 2.3398 | 33700  | 0.0003        |
+| 2.3467 | 33800  | 0.0003        |
+| 2.3537 | 33900  | 0.0003        |
+| 2.3606 | 34000  | 0.0003        |
+| 2.3676 | 34100  | 0.0003        |
+| 2.3745 | 34200  | 0.0003        |
+| 2.3814 | 34300  | 0.0003        |
+| 2.3884 | 34400  | 0.0003        |
+| 2.3953 | 34500  | 0.0003        |
+| 2.4023 | 34600  | 0.0003        |
+| 2.4092 | 34700  | 0.0003        |
+| 2.4162 | 34800  | 0.0003        |
+| 2.4231 | 34900  | 0.0003        |
+| 2.4300 | 35000  | 0.0003        |
+| 2.4370 | 35100  | 0.0003        |
+| 2.4439 | 35200  | 0.0003        |
+| 2.4509 | 35300  | 0.0003        |
+| 2.4578 | 35400  | 0.0003        |
+| 2.4648 | 35500  | 0.0003        |
+| 2.4717 | 35600  | 0.0003        |
+| 2.4787 | 35700  | 0.0003        |
+| 2.4856 | 35800  | 0.0003        |
+| 2.4925 | 35900  | 0.0003        |
+| 2.4995 | 36000  | 0.0003        |
+| 2.5064 | 36100  | 0.0003        |
+| 2.5134 | 36200  | 0.0003        |
+| 2.5203 | 36300  | 0.0003        |
+| 2.5273 | 36400  | 0.0003        |
+| 2.5342 | 36500  | 0.0003        |
+| 2.5411 | 36600  | 0.0003        |
+| 2.5481 | 36700  | 0.0003        |
+| 2.5550 | 36800  | 0.0003        |
+| 2.5620 | 36900  | 0.0003        |
+| 2.5689 | 37000  | 0.0003        |
+| 2.5759 | 37100  | 0.0003        |
+| 2.5828 | 37200  | 0.0003        |
+| 2.5897 | 37300  | 0.0003        |
+| 2.5967 | 37400  | 0.0003        |
+| 2.6036 | 37500  | 0.0003        |
+| 2.6106 | 37600  | 0.0003        |
+| 2.6175 | 37700  | 0.0003        |
+| 2.6245 | 37800  | 0.0003        |
+| 2.6314 | 37900  | 0.0003        |
+| 2.6383 | 38000  | 0.0003        |
+| 2.6453 | 38100  | 0.0003        |
+| 2.6522 | 38200  | 0.0003        |
+| 2.6592 | 38300  | 0.0003        |
+| 2.6661 | 38400  | 0.0003        |
+| 2.6731 | 38500  | 0.0003        |
+| 2.6800 | 38600  | 0.0003        |
+| 2.6869 | 38700  | 0.0003        |
+| 2.6939 | 38800  | 0.0003        |
+| 2.7008 | 38900  | 0.0003        |
+| 2.7078 | 39000  | 0.0003        |
+| 2.7147 | 39100  | 0.0003        |
+| 2.7217 | 39200  | 0.0003        |
+| 2.7286 | 39300  | 0.0003        |
+| 2.7355 | 39400  | 0.0003        |
+| 2.7425 | 39500  | 0.0002        |
+| 2.7494 | 39600  | 0.0003        |
+| 2.7564 | 39700  | 0.0003        |
+| 2.7633 | 39800  | 0.0003        |
+| 2.7703 | 39900  | 0.0003        |
+| 2.7772 | 40000  | 0.0003        |
+| 2.7841 | 40100  | 0.0003        |
+| 2.7911 | 40200  | 0.0003        |
+| 2.7980 | 40300  | 0.0003        |
+| 2.8050 | 40400  | 0.0003        |
+| 2.8119 | 40500  | 0.0003        |
+| 2.8189 | 40600  | 0.0003        |
+| 2.8258 | 40700  | 0.0003        |
+| 2.8327 | 40800  | 0.0003        |
+| 2.8397 | 40900  | 0.0003        |
+| 2.8466 | 41000  | 0.0003        |
+| 2.8536 | 41100  | 0.0003        |
+| 2.8605 | 41200  | 0.0003        |
+| 2.8675 | 41300  | 0.0003        |
+| 2.8744 | 41400  | 0.0003        |
+| 2.8813 | 41500  | 0.0003        |
+| 2.8883 | 41600  | 0.0003        |
+| 2.8952 | 41700  | 0.0003        |
+| 2.9022 | 41800  | 0.0003        |
+| 2.9091 | 41900  | 0.0003        |
+| 2.9161 | 42000  | 0.0003        |
+| 2.9230 | 42100  | 0.0003        |
+| 2.9299 | 42200  | 0.0003        |
+| 2.9369 | 42300  | 0.0003        |
+| 2.9438 | 42400  | 0.0003        |
+| 2.9508 | 42500  | 0.0003        |
+| 2.9577 | 42600  | 0.0003        |
+| 2.9647 | 42700  | 0.0003        |
+| 2.9716 | 42800  | 0.0002        |
+| 2.9785 | 42900  | 0.0003        |
+| 2.9855 | 43000  | 0.0003        |
+| 2.9924 | 43100  | 0.0003        |
+| 2.9994 | 43200  | 0.0003        |
+| 3.0063 | 43300  | 0.0002        |
+| 3.0133 | 43400  | 0.0003        |
+| 3.0202 | 43500  | 0.0003        |
+| 3.0271 | 43600  | 0.0003        |
+| 3.0341 | 43700  | 0.0003        |
+| 3.0410 | 43800  | 0.0003        |
+| 3.0480 | 43900  | 0.0003        |
+| 3.0549 | 44000  | 0.0003        |
+| 3.0619 | 44100  | 0.0003        |
+| 3.0688 | 44200  | 0.0002        |
+| 3.0757 | 44300  | 0.0003        |
+| 3.0827 | 44400  | 0.0003        |
+| 3.0896 | 44500  | 0.0003        |
+| 3.0966 | 44600  | 0.0003        |
+| 3.1035 | 44700  | 0.0003        |
+| 3.1105 | 44800  | 0.0003        |
+| 3.1174 | 44900  | 0.0003        |
+| 3.1243 | 45000  | 0.0003        |
+| 3.1313 | 45100  | 0.0003        |
+| 3.1382 | 45200  | 0.0003        |
+| 3.1452 | 45300  | 0.0003        |
+| 3.1521 | 45400  | 0.0003        |
+| 3.1591 | 45500  | 0.0003        |
+| 3.1660 | 45600  | 0.0003        |
+| 3.1730 | 45700  | 0.0003        |
+| 3.1799 | 45800  | 0.0003        |
+| 3.1868 | 45900  | 0.0003        |
+| 3.1938 | 46000  | 0.0003        |
+| 3.2007 | 46100  | 0.0003        |
+| 3.2077 | 46200  | 0.0002        |
+| 3.2146 | 46300  | 0.0002        |
+| 3.2216 | 46400  | 0.0003        |
+| 3.2285 | 46500  | 0.0003        |
+| 3.2354 | 46600  | 0.0003        |
+| 3.2424 | 46700  | 0.0003        |
+| 3.2493 | 46800  | 0.0003        |
+| 3.2563 | 46900  | 0.0003        |
+| 3.2632 | 47000  | 0.0003        |
+| 3.2702 | 47100  | 0.0003        |
+| 3.2771 | 47200  | 0.0003        |
+| 3.2840 | 47300  | 0.0003        |
+| 3.2910 | 47400  | 0.0003        |
+| 3.2979 | 47500  | 0.0002        |
+| 3.3049 | 47600  | 0.0003        |
+| 3.3118 | 47700  | 0.0003        |
+| 3.3188 | 47800  | 0.0003        |
+| 3.3257 | 47900  | 0.0003        |
+| 3.3326 | 48000  | 0.0003        |
+| 3.3396 | 48100  | 0.0003        |
+| 3.3465 | 48200  | 0.0003        |
+| 3.3535 | 48300  | 0.0003        |
+| 3.3604 | 48400  | 0.0003        |
+| 3.3674 | 48500  | 0.0003        |
+| 3.3743 | 48600  | 0.0003        |
+| 3.3812 | 48700  | 0.0002        |
+| 3.3882 | 48800  | 0.0003        |
+| 3.3951 | 48900  | 0.0003        |
+| 3.4021 | 49000  | 0.0003        |
+| 3.4090 | 49100  | 0.0002        |
+| 3.4160 | 49200  | 0.0003        |
+| 3.4229 | 49300  | 0.0003        |
+| 3.4298 | 49400  | 0.0003        |
+| 3.4368 | 49500  | 0.0003        |
+| 3.4437 | 49600  | 0.0003        |
+| 3.4507 | 49700  | 0.0003        |
+| 3.4576 | 49800  | 0.0003        |
+| 3.4646 | 49900  | 0.0003        |
+| 3.4715 | 50000  | 0.0003        |
+| 3.4784 | 50100  | 0.0003        |
+| 3.4854 | 50200  | 0.0003        |
+| 3.4923 | 50300  | 0.0003        |
+| 3.4993 | 50400  | 0.0002        |
+| 3.5062 | 50500  | 0.0003        |
+| 3.5132 | 50600  | 0.0003        |
+| 3.5201 | 50700  | 0.0003        |
+| 3.5270 | 50800  | 0.0003        |
+| 3.5340 | 50900  | 0.0002        |
+| 3.5409 | 51000  | 0.0002        |
+| 3.5479 | 51100  | 0.0003        |
+| 3.5548 | 51200  | 0.0003        |
+| 3.5618 | 51300  | 0.0002        |
+| 3.5687 | 51400  | 0.0003        |
+| 3.5756 | 51500  | 0.0003        |
+| 3.5826 | 51600  | 0.0002        |
+| 3.5895 | 51700  | 0.0003        |
+| 3.5965 | 51800  | 0.0003        |
+| 3.6034 | 51900  | 0.0003        |
+| 3.6104 | 52000  | 0.0002        |
+| 3.6173 | 52100  | 0.0003        |
+| 3.6242 | 52200  | 0.0002        |
+| 3.6312 | 52300  | 0.0002        |
+| 3.6381 | 52400  | 0.0003        |
+| 3.6451 | 52500  | 0.0003        |
+| 3.6520 | 52600  | 0.0002        |
+| 3.6590 | 52700  | 0.0002        |
+| 3.6659 | 52800  | 0.0002        |
+| 3.6728 | 52900  | 0.0003        |
+| 3.6798 | 53000  | 0.0002        |
+| 3.6867 | 53100  | 0.0002        |
+| 3.6937 | 53200  | 0.0002        |
+| 3.7006 | 53300  | 0.0003        |
+| 3.7076 | 53400  | 0.0003        |
+| 3.7145 | 53500  | 0.0002        |
+| 3.7214 | 53600  | 0.0002        |
+| 3.7284 | 53700  | 0.0002        |
+| 3.7353 | 53800  | 0.0002        |
+| 3.7423 | 53900  | 0.0002        |
+| 3.7492 | 54000  | 0.0002        |
+| 3.7562 | 54100  | 0.0002        |
+| 3.7631 | 54200  | 0.0002        |
+| 3.7700 | 54300  | 0.0002        |
+| 3.7770 | 54400  | 0.0003        |
+| 3.7839 | 54500  | 0.0003        |
+| 3.7909 | 54600  | 0.0003        |
+| 3.7978 | 54700  | 0.0003        |
+| 3.8048 | 54800  | 0.0002        |
+| 3.8117 | 54900  | 0.0003        |
+| 3.8186 | 55000  | 0.0002        |
+| 3.8256 | 55100  | 0.0003        |
+| 3.8325 | 55200  | 0.0002        |
+| 3.8395 | 55300  | 0.0003        |
+| 3.8464 | 55400  | 0.0003        |
+| 3.8534 | 55500  | 0.0002        |
+| 3.8603 | 55600  | 0.0002        |
+| 3.8672 | 55700  | 0.0003        |
+| 3.8742 | 55800  | 0.0003        |
+| 3.8811 | 55900  | 0.0003        |
+| 3.8881 | 56000  | 0.0003        |
+| 3.8950 | 56100  | 0.0002        |
+| 3.9020 | 56200  | 0.0003        |
+| 3.9089 | 56300  | 0.0003        |
+| 3.9159 | 56400  | 0.0003        |
+| 3.9228 | 56500  | 0.0003        |
+| 3.9297 | 56600  | 0.0002        |
+| 3.9367 | 56700  | 0.0003        |
+| 3.9436 | 56800  | 0.0003        |
+| 3.9506 | 56900  | 0.0003        |
+| 3.9575 | 57000  | 0.0003        |
+| 3.9645 | 57100  | 0.0002        |
+| 3.9714 | 57200  | 0.0002        |
+| 3.9783 | 57300  | 0.0003        |
+| 3.9853 | 57400  | 0.0002        |
+| 3.9922 | 57500  | 0.0003        |
+| 3.9992 | 57600  | 0.0002        |
+| 4.0061 | 57700  | 0.0002        |
+| 4.0131 | 57800  | 0.0002        |
+| 4.0200 | 57900  | 0.0002        |
+| 4.0269 | 58000  | 0.0002        |
+| 4.0339 | 58100  | 0.0002        |
+| 4.0408 | 58200  | 0.0003        |
+| 4.0478 | 58300  | 0.0002        |
+| 4.0547 | 58400  | 0.0002        |
+| 4.0617 | 58500  | 0.0003        |
+| 4.0686 | 58600  | 0.0002        |
+| 4.0755 | 58700  | 0.0002        |
+| 4.0825 | 58800  | 0.0002        |
+| 4.0894 | 58900  | 0.0002        |
+| 4.0964 | 59000  | 0.0002        |
+| 4.1033 | 59100  | 0.0002        |
+| 4.1103 | 59200  | 0.0002        |
+| 4.1172 | 59300  | 0.0002        |
+| 4.1241 | 59400  | 0.0002        |
+| 4.1311 | 59500  | 0.0003        |
+| 4.1380 | 59600  | 0.0003        |
+| 4.1450 | 59700  | 0.0003        |
+| 4.1519 | 59800  | 0.0003        |
+| 4.1589 | 59900  | 0.0002        |
+| 4.1658 | 60000  | 0.0003        |
+| 4.1727 | 60100  | 0.0002        |
+| 4.1797 | 60200  | 0.0002        |
+| 4.1866 | 60300  | 0.0002        |
+| 4.1936 | 60400  | 0.0002        |
+| 4.2005 | 60500  | 0.0002        |
+| 4.2075 | 60600  | 0.0002        |
+| 4.2144 | 60700  | 0.0002        |
+| 4.2213 | 60800  | 0.0002        |
+| 4.2283 | 60900  | 0.0002        |
+| 4.2352 | 61000  | 0.0002        |
+| 4.2422 | 61100  | 0.0002        |
+| 4.2491 | 61200  | 0.0003        |
+| 4.2561 | 61300  | 0.0002        |
+| 4.2630 | 61400  | 0.0003        |
+| 4.2699 | 61500  | 0.0002        |
+| 4.2769 | 61600  | 0.0002        |
+| 4.2838 | 61700  | 0.0003        |
+| 4.2908 | 61800  | 0.0002        |
+| 4.2977 | 61900  | 0.0002        |
+| 4.3047 | 62000  | 0.0003        |
+| 4.3116 | 62100  | 0.0002        |
+| 4.3185 | 62200  | 0.0002        |
+| 4.3255 | 62300  | 0.0002        |
+| 4.3324 | 62400  | 0.0002        |
+| 4.3394 | 62500  | 0.0002        |
+| 4.3463 | 62600  | 0.0002        |
+| 4.3533 | 62700  | 0.0002        |
+| 4.3602 | 62800  | 0.0003        |
+| 4.3671 | 62900  | 0.0002        |
+| 4.3741 | 63000  | 0.0002        |
+| 4.3810 | 63100  | 0.0002        |
+| 4.3880 | 63200  | 0.0002        |
+| 4.3949 | 63300  | 0.0002        |
+| 4.4019 | 63400  | 0.0002        |
+| 4.4088 | 63500  | 0.0002        |
+| 4.4157 | 63600  | 0.0002        |
+| 4.4227 | 63700  | 0.0002        |
+| 4.4296 | 63800  | 0.0003        |
+| 4.4366 | 63900  | 0.0002        |
+| 4.4435 | 64000  | 0.0002        |
+| 4.4505 | 64100  | 0.0002        |
+| 4.4574 | 64200  | 0.0002        |
+| 4.4643 | 64300  | 0.0002        |
+| 4.4713 | 64400  | 0.0002        |
+| 4.4782 | 64500  | 0.0002        |
+| 4.4852 | 64600  | 0.0003        |
+| 4.4921 | 64700  | 0.0002        |
+| 4.4991 | 64800  | 0.0002        |
+| 4.5060 | 64900  | 0.0002        |
+| 4.5129 | 65000  | 0.0002        |
+| 4.5199 | 65100  | 0.0002        |
+| 4.5268 | 65200  | 0.0002        |
+| 4.5338 | 65300  | 0.0002        |
+| 4.5407 | 65400  | 0.0002        |
+| 4.5477 | 65500  | 0.0002        |
+| 4.5546 | 65600  | 0.0002        |
+| 4.5615 | 65700  | 0.0002        |
+| 4.5685 | 65800  | 0.0002        |
+| 4.5754 | 65900  | 0.0002        |
+| 4.5824 | 66000  | 0.0002        |
+| 4.5893 | 66100  | 0.0002        |
+| 4.5963 | 66200  | 0.0002        |
+| 4.6032 | 66300  | 0.0002        |
+| 4.6102 | 66400  | 0.0002        |
+| 4.6171 | 66500  | 0.0002        |
+| 4.6240 | 66600  | 0.0002        |
+| 4.6310 | 66700  | 0.0002        |
+| 4.6379 | 66800  | 0.0002        |
+| 4.6449 | 66900  | 0.0002        |
+| 4.6518 | 67000  | 0.0002        |
+| 4.6588 | 67100  | 0.0002        |
+| 4.6657 | 67200  | 0.0002        |
+| 4.6726 | 67300  | 0.0002        |
+| 4.6796 | 67400  | 0.0002        |
+| 4.6865 | 67500  | 0.0002        |
+| 4.6935 | 67600  | 0.0002        |
+| 4.7004 | 67700  | 0.0002        |
+| 4.7074 | 67800  | 0.0002        |
+| 4.7143 | 67900  | 0.0002        |
+| 4.7212 | 68000  | 0.0002        |
+| 4.7282 | 68100  | 0.0002        |
+| 4.7351 | 68200  | 0.0002        |
+| 4.7421 | 68300  | 0.0002        |
+| 4.7490 | 68400  | 0.0002        |
+| 4.7560 | 68500  | 0.0002        |
+| 4.7629 | 68600  | 0.0002        |
+| 4.7698 | 68700  | 0.0002        |
+| 4.7768 | 68800  | 0.0002        |
+| 4.7837 | 68900  | 0.0002        |
+| 4.7907 | 69000  | 0.0003        |
+| 4.7976 | 69100  | 0.0002        |
+| 4.8046 | 69200  | 0.0002        |
+| 4.8115 | 69300  | 0.0003        |
+| 4.8184 | 69400  | 0.0002        |
+| 4.8254 | 69500  | 0.0003        |
+| 4.8323 | 69600  | 0.0002        |
+| 4.8393 | 69700  | 0.0002        |
+| 4.8462 | 69800  | 0.0002        |
+| 4.8532 | 69900  | 0.0002        |
+| 4.8601 | 70000  | 0.0002        |
+| 4.8670 | 70100  | 0.0002        |
+| 4.8740 | 70200  | 0.0002        |
+| 4.8809 | 70300  | 0.0002        |
+| 4.8879 | 70400  | 0.0002        |
+| 4.8948 | 70500  | 0.0002        |
+| 4.9018 | 70600  | 0.0002        |
+| 4.9087 | 70700  | 0.0002        |
+| 4.9156 | 70800  | 0.0002        |
+| 4.9226 | 70900  | 0.0002        |
+| 4.9295 | 71000  | 0.0002        |
+| 4.9365 | 71100  | 0.0002        |
+| 4.9434 | 71200  | 0.0002        |
+| 4.9504 | 71300  | 0.0003        |
+| 4.9573 | 71400  | 0.0002        |
+| 4.9642 | 71500  | 0.0002        |
+| 4.9712 | 71600  | 0.0002        |
+| 4.9781 | 71700  | 0.0002        |
+| 4.9851 | 71800  | 0.0002        |
+| 4.9920 | 71900  | 0.0002        |
+| 4.9990 | 72000  | 0.0002        |
+| 5.0059 | 72100  | 0.0002        |
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+| 5.0198 | 72300  | 0.0002        |
+| 5.0267 | 72400  | 0.0002        |
+| 5.0337 | 72500  | 0.0002        |
+| 5.0406 | 72600  | 0.0002        |
+| 5.0476 | 72700  | 0.0002        |
+| 5.0545 | 72800  | 0.0002        |
+| 5.0614 | 72900  | 0.0002        |
+| 5.0684 | 73000  | 0.0002        |
+| 5.0753 | 73100  | 0.0002        |
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+| 5.0892 | 73300  | 0.0002        |
+| 5.0962 | 73400  | 0.0002        |
+| 5.1031 | 73500  | 0.0002        |
+| 5.1100 | 73600  | 0.0002        |
+| 5.1170 | 73700  | 0.0002        |
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+| 5.1309 | 73900  | 0.0002        |
+| 5.1378 | 74000  | 0.0002        |
+| 5.1448 | 74100  | 0.0002        |
+| 5.1517 | 74200  | 0.0002        |
+| 5.1586 | 74300  | 0.0002        |
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+| 5.1725 | 74500  | 0.0002        |
+| 5.1795 | 74600  | 0.0002        |
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+| 5.1934 | 74800  | 0.0002        |
+| 5.2003 | 74900  | 0.0002        |
+| 5.2072 | 75000  | 0.0002        |
+| 5.2142 | 75100  | 0.0002        |
+| 5.2211 | 75200  | 0.0002        |
+| 5.2281 | 75300  | 0.0002        |
+| 5.2350 | 75400  | 0.0002        |
+| 5.2420 | 75500  | 0.0002        |
+| 5.2489 | 75600  | 0.0002        |
+| 5.2558 | 75700  | 0.0002        |
+| 5.2628 | 75800  | 0.0002        |
+| 5.2697 | 75900  | 0.0002        |
+| 5.2767 | 76000  | 0.0002        |
+| 5.2836 | 76100  | 0.0002        |
+| 5.2906 | 76200  | 0.0002        |
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+| 5.3322 | 76800  | 0.0002        |
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+| 5.3461 | 77000  | 0.0002        |
+| 5.3531 | 77100  | 0.0002        |
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+| 5.4155 | 78000  | 0.0002        |
+| 5.4225 | 78100  | 0.0002        |
+| 5.4294 | 78200  | 0.0002        |
+| 5.4364 | 78300  | 0.0002        |
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+| 5.4503 | 78500  | 0.0002        |
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+| 5.4780 | 78900  | 0.0002        |
+| 5.4850 | 79000  | 0.0002        |
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+| 5.4989 | 79200  | 0.0002        |
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+| 5.6169 | 80900  | 0.0002        |
+| 5.6238 | 81000  | 0.0002        |
+| 5.6308 | 81100  | 0.0002        |
+| 5.6377 | 81200  | 0.0002        |
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+| 5.6585 | 81500  | 0.0002        |
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+| 5.6794 | 81800  | 0.0002        |
+| 5.6863 | 81900  | 0.0002        |
+| 5.6933 | 82000  | 0.0002        |
+| 5.7002 | 82100  | 0.0002        |
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+| 5.7141 | 82300  | 0.0002        |
+| 5.7210 | 82400  | 0.0002        |
+| 5.7280 | 82500  | 0.0002        |
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+| 5.7488 | 82800  | 0.0002        |
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+| 5.7627 | 83000  | 0.0002        |
+| 5.7696 | 83100  | 0.0002        |
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+| 5.8182 | 83800  | 0.0002        |
+| 5.8252 | 83900  | 0.0003        |
+| 5.8321 | 84000  | 0.0002        |
+| 5.8391 | 84100  | 0.0002        |
+| 5.8460 | 84200  | 0.0002        |
+| 5.8529 | 84300  | 0.0002        |
+| 5.8599 | 84400  | 0.0002        |
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+| 5.8877 | 84800  | 0.0002        |
+| 5.8946 | 84900  | 0.0002        |
+| 5.9015 | 85000  | 0.0002        |
+| 5.9085 | 85100  | 0.0002        |
+| 5.9154 | 85200  | 0.0002        |
+| 5.9224 | 85300  | 0.0002        |
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+| 5.9710 | 86000  | 0.0002        |
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+| 5.9849 | 86200  | 0.0002        |
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+| 6.0404 | 87000  | 0.0002        |
+| 6.0474 | 87100  | 0.0002        |
+| 6.0543 | 87200  | 0.0002        |
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+| 6.1098 | 88000  | 0.0002        |
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+| 6.1237 | 88200  | 0.0002        |
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+| 6.1793 | 89000  | 0.0002        |
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+| 6.2001 | 89300  | 0.0002        |
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+| 6.2487 | 90000  | 0.0002        |
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+| 6.2626 | 90200  | 0.0002        |
+| 6.2695 | 90300  | 0.0002        |
+| 6.2765 | 90400  | 0.0002        |
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+| 6.3042 | 90800  | 0.0002        |
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+| 6.3181 | 91000  | 0.0002        |
+| 6.3251 | 91100  | 0.0002        |
+| 6.3320 | 91200  | 0.0002        |
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+| 6.3528 | 91500  | 0.0002        |
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+| 6.3737 | 91800  | 0.0002        |
+| 6.3806 | 91900  | 0.0002        |
+| 6.3876 | 92000  | 0.0002        |
+| 6.3945 | 92100  | 0.0002        |
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+| 6.4153 | 92400  | 0.0002        |
+| 6.4223 | 92500  | 0.0002        |
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+| 6.4431 | 92800  | 0.0002        |
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+| 6.4778 | 93300  | 0.0002        |
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+| 9.9493 | 143300 | 0.0002        |
+| 9.9563 | 143400 | 0.0002        |
+| 9.9632 | 143500 | 0.0002        |
+| 9.9701 | 143600 | 0.0002        |
+| 9.9771 | 143700 | 0.0002        |
+| 9.9840 | 143800 | 0.0002        |
+| 9.9910 | 143900 | 0.0002        |
+| 9.9979 | 144000 | 0.0002        |
+
+</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",
+}
+```
+
+<!--
+## 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"
+}

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:eabea5925e09078fa18fc96436d93f9550842bc24a313f057b3249f0f2703433
+oid sha256:1df9676d169e37787b16a73004a69e7003d02e0b5029bdf67efd4f19f07e65ef
 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
+}