FaMiniLm_Mizan3 / README.md

Add new SentenceTransformer model

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	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:1021596
	- loss:MultipleNegativesRankingLoss
	base_model: codersan/FaMiniLM
	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/FaMiniLM

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [codersan/FaMiniLM](https://huggingface.co/codersan/FaMiniLM). 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: [codersan/FaMiniLM](https://huggingface.co/codersan/FaMiniLM) <!-- at revision 22713fef958dd574a0171739cb8f8804c8650527 -->
	- 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/FaMiniLm_Mizan3")
	# 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, 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: 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: 4 tokens</li><li>mean: 46.68 tokens</li><li>max: 212 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 16.07 tokens</li><li>max: 81 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`: 16
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `load_best_model_at_end`: True
	- `push_to_hub`: True
	- `hub_model_id`: codersan/FaMiniLm_Mizan3
	- `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`: 16
	- `per_device_eval_batch_size`: 8
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_steps`: None
	- `learning_rate`: 2e-05
	- `weight_decay`: 0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1
	- `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`: True
	- `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/FaMiniLm_Mizan3
	- `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 \| - \|
	\| 0.0016 \| 100 \| 3.1518 \|
	\| 0.0031 \| 200 \| 3.1015 \|
	\| 0.0047 \| 300 \| 2.9207 \|
	\| 0.0063 \| 400 \| 2.8322 \|
	\| 0.0078 \| 500 \| 2.7199 \|
	\| 0.0094 \| 600 \| 2.6413 \|
	\| 0.0110 \| 700 \| 2.4895 \|
	\| 0.0125 \| 800 \| 2.4221 \|
	\| 0.0141 \| 900 \| 2.2712 \|
	\| 0.0157 \| 1000 \| 2.1497 \|
	\| 0.0172 \| 1100 \| 2.0346 \|
	\| 0.0188 \| 1200 \| 1.9132 \|
	\| 0.0204 \| 1300 \| 1.848 \|
	\| 0.0219 \| 1400 \| 1.7412 \|
	\| 0.0235 \| 1500 \| 1.6231 \|
	\| 0.0251 \| 1600 \| 1.5678 \|
	\| 0.0266 \| 1700 \| 1.4954 \|
	\| 0.0282 \| 1800 \| 1.4429 \|
	\| 0.0298 \| 1900 \| 1.4179 \|
	\| 0.0313 \| 2000 \| 1.3837 \|
	\| 0.0329 \| 2100 \| 1.3612 \|
	\| 0.0345 \| 2200 \| 1.3025 \|
	\| 0.0360 \| 2300 \| 1.2768 \|
	\| 0.0376 \| 2400 \| 1.2126 \|
	\| 0.0392 \| 2500 \| 1.1951 \|
	\| 0.0407 \| 2600 \| 1.1558 \|
	\| 0.0423 \| 2700 \| 1.1002 \|
	\| 0.0439 \| 2800 \| 1.1269 \|
	\| 0.0454 \| 2900 \| 1.0932 \|
	\| 0.0470 \| 3000 \| 1.0697 \|
	\| 0.0486 \| 3100 \| 1.0455 \|
	\| 0.0501 \| 3200 \| 1.0405 \|
	\| 0.0517 \| 3300 \| 0.9895 \|
	\| 0.0532 \| 3400 \| 0.9983 \|
	\| 0.0548 \| 3500 \| 0.9381 \|
	\| 0.0564 \| 3600 \| 0.9618 \|
	\| 0.0579 \| 3700 \| 0.9799 \|
	\| 0.0595 \| 3800 \| 0.8866 \|
	\| 0.0611 \| 3900 \| 0.9085 \|
	\| 0.0626 \| 4000 \| 0.9123 \|
	\| 0.0642 \| 4100 \| 0.9017 \|
	\| 0.0658 \| 4200 \| 0.8789 \|
	\| 0.0673 \| 4300 \| 0.8164 \|
	\| 0.0689 \| 4400 \| 0.8131 \|
	\| 0.0705 \| 4500 \| 0.7834 \|
	\| 0.0720 \| 4600 \| 0.7814 \|
	\| 0.0736 \| 4700 \| 0.7927 \|
	\| 0.0752 \| 4800 \| 0.8416 \|
	\| 0.0767 \| 4900 \| 0.73 \|
	\| 0.0783 \| 5000 \| 0.753 \|
	\| 0.0799 \| 5100 \| 0.7397 \|
	\| 0.0814 \| 5200 \| 0.7242 \|
	\| 0.0830 \| 5300 \| 0.734 \|
	\| 0.0846 \| 5400 \| 0.7379 \|
	\| 0.0861 \| 5500 \| 0.7255 \|
	\| 0.0877 \| 5600 \| 0.7621 \|
	\| 0.0893 \| 5700 \| 0.6825 \|
	\| 0.0908 \| 5800 \| 0.7056 \|
	\| 0.0924 \| 5900 \| 0.6877 \|
	\| 0.0940 \| 6000 \| 0.6865 \|
	\| 0.0955 \| 6100 \| 0.6652 \|
	\| 0.0971 \| 6200 \| 0.6445 \|
	\| 0.0987 \| 6300 \| 0.6548 \|
	\| 0.1002 \| 6400 \| 0.6556 \|
	\| 0.1018 \| 6500 \| 0.6544 \|
	\| 0.1034 \| 6600 \| 0.6496 \|
	\| 0.1049 \| 6700 \| 0.6158 \|
	\| 0.1065 \| 6800 \| 0.6693 \|
	\| 0.1081 \| 6900 \| 0.6179 \|
	\| 0.1096 \| 7000 \| 0.5527 \|
	\| 0.1112 \| 7100 \| 0.596 \|
	\| 0.1128 \| 7200 \| 0.5625 \|
	\| 0.1143 \| 7300 \| 0.592 \|
	\| 0.1159 \| 7400 \| 0.6063 \|
	\| 0.1175 \| 7500 \| 0.5163 \|
	\| 0.1190 \| 7600 \| 0.5472 \|
	\| 0.1206 \| 7700 \| 0.5849 \|
	\| 0.1222 \| 7800 \| 0.5948 \|
	\| 0.1237 \| 7900 \| 0.5245 \|
	\| 0.1253 \| 8000 \| 0.5561 \|
	\| 0.1269 \| 8100 \| 0.5175 \|
	\| 0.1284 \| 8200 \| 0.4929 \|
	\| 0.1300 \| 8300 \| 0.5158 \|
	\| 0.1316 \| 8400 \| 0.5429 \|
	\| 0.1331 \| 8500 \| 0.5324 \|
	\| 0.1347 \| 8600 \| 0.511 \|
	\| 0.1363 \| 8700 \| 0.5242 \|
	\| 0.1378 \| 8800 \| 0.5202 \|
	\| 0.1394 \| 8900 \| 0.4967 \|
	\| 0.1410 \| 9000 \| 0.5466 \|
	\| 0.1425 \| 9100 \| 0.4865 \|
	\| 0.1441 \| 9200 \| 0.5172 \|
	\| 0.1457 \| 9300 \| 0.51 \|
	\| 0.1472 \| 9400 \| 0.5204 \|
	\| 0.1488 \| 9500 \| 0.4851 \|
	\| 0.1504 \| 9600 \| 0.4726 \|
	\| 0.1519 \| 9700 \| 0.4608 \|
	\| 0.1535 \| 9800 \| 0.453 \|
	\| 0.1551 \| 9900 \| 0.4539 \|
	\| 0.1566 \| 10000 \| 0.442 \|
	\| 0.1582 \| 10100 \| 0.4632 \|
	\| 0.1597 \| 10200 \| 0.4024 \|
	\| 0.1613 \| 10300 \| 0.4516 \|
	\| 0.1629 \| 10400 \| 0.4551 \|
	\| 0.1644 \| 10500 \| 0.4598 \|
	\| 0.1660 \| 10600 \| 0.4791 \|
	\| 0.1676 \| 10700 \| 0.4295 \|
	\| 0.1691 \| 10800 \| 0.4552 \|
	\| 0.1707 \| 10900 \| 0.4548 \|
	\| 0.1723 \| 11000 \| 0.4795 \|
	\| 0.1738 \| 11100 \| 0.4694 \|
	\| 0.1754 \| 11200 \| 0.4049 \|
	\| 0.1770 \| 11300 \| 0.4473 \|
	\| 0.1785 \| 11400 \| 0.4161 \|
	\| 0.1801 \| 11500 \| 0.4106 \|
	\| 0.1817 \| 11600 \| 0.4276 \|
	\| 0.1832 \| 11700 \| 0.416 \|
	\| 0.1848 \| 11800 \| 0.4184 \|
	\| 0.1864 \| 11900 \| 0.4268 \|
	\| 0.1879 \| 12000 \| 0.4169 \|
	\| 0.1895 \| 12100 \| 0.4063 \|
	\| 0.1911 \| 12200 \| 0.4257 \|
	\| 0.1926 \| 12300 \| 0.4114 \|
	\| 0.1942 \| 12400 \| 0.3921 \|
	\| 0.1958 \| 12500 \| 0.4037 \|
	\| 0.1973 \| 12600 \| 0.4642 \|
	\| 0.1989 \| 12700 \| 0.3929 \|
	\| 0.2005 \| 12800 \| 0.4059 \|
	\| 0.2020 \| 12900 \| 0.4132 \|
	\| 0.2036 \| 13000 \| 0.4101 \|
	\| 0.2052 \| 13100 \| 0.4122 \|
	\| 0.2067 \| 13200 \| 0.3954 \|
	\| 0.2083 \| 13300 \| 0.3671 \|
	\| 0.2099 \| 13400 \| 0.4257 \|
	\| 0.2114 \| 13500 \| 0.3719 \|
	\| 0.2130 \| 13600 \| 0.3603 \|
	\| 0.2146 \| 13700 \| 0.3465 \|
	\| 0.2161 \| 13800 \| 0.3726 \|
	\| 0.2177 \| 13900 \| 0.4021 \|
	\| 0.2193 \| 14000 \| 0.3706 \|
	\| 0.2208 \| 14100 \| 0.3471 \|
	\| 0.2224 \| 14200 \| 0.3848 \|
	\| 0.2240 \| 14300 \| 0.3967 \|
	\| 0.2255 \| 14400 \| 0.3985 \|
	\| 0.2271 \| 14500 \| 0.3457 \|
	\| 0.2287 \| 14600 \| 0.3438 \|
	\| 0.2302 \| 14700 \| 0.3333 \|
	\| 0.2318 \| 14800 \| 0.3525 \|
	\| 0.2334 \| 14900 \| 0.3948 \|
	\| 0.2349 \| 15000 \| 0.3657 \|
	\| 0.2365 \| 15100 \| 0.3437 \|
	\| 0.2381 \| 15200 \| 0.361 \|
	\| 0.2396 \| 15300 \| 0.356 \|
	\| 0.2412 \| 15400 \| 0.3572 \|
	\| 0.2428 \| 15500 \| 0.3464 \|
	\| 0.2443 \| 15600 \| 0.3885 \|
	\| 0.2459 \| 15700 \| 0.3324 \|
	\| 0.2475 \| 15800 \| 0.3553 \|
	\| 0.2490 \| 15900 \| 0.3201 \|
	\| 0.2506 \| 16000 \| 0.4078 \|
	\| 0.2522 \| 16100 \| 0.3919 \|
	\| 0.2537 \| 16200 \| 0.3505 \|
	\| 0.2553 \| 16300 \| 0.3423 \|
	\| 0.2569 \| 16400 \| 0.3018 \|
	\| 0.2584 \| 16500 \| 0.3392 \|
	\| 0.2600 \| 16600 \| 0.3128 \|
	\| 0.2616 \| 16700 \| 0.3542 \|
	\| 0.2631 \| 16800 \| 0.3639 \|
	\| 0.2647 \| 16900 \| 0.3765 \|
	\| 0.2662 \| 17000 \| 0.3405 \|
	\| 0.2678 \| 17100 \| 0.326 \|
	\| 0.2694 \| 17200 \| 0.3591 \|
	\| 0.2709 \| 17300 \| 0.3087 \|
	\| 0.2725 \| 17400 \| 0.3336 \|
	\| 0.2741 \| 17500 \| 0.2889 \|
	\| 0.2756 \| 17600 \| 0.3341 \|
	\| 0.2772 \| 17700 \| 0.3468 \|
	\| 0.2788 \| 17800 \| 0.3033 \|
	\| 0.2803 \| 17900 \| 0.3482 \|
	\| 0.2819 \| 18000 \| 0.3649 \|
	\| 0.2835 \| 18100 \| 0.3134 \|
	\| 0.2850 \| 18200 \| 0.3264 \|
	\| 0.2866 \| 18300 \| 0.3127 \|
	\| 0.2882 \| 18400 \| 0.3483 \|
	\| 0.2897 \| 18500 \| 0.349 \|
	\| 0.2913 \| 18600 \| 0.2957 \|
	\| 0.2929 \| 18700 \| 0.3443 \|
	\| 0.2944 \| 18800 \| 0.2884 \|
	\| 0.2960 \| 18900 \| 0.34 \|
	\| 0.2976 \| 19000 \| 0.2875 \|
	\| 0.2991 \| 19100 \| 0.3322 \|
	\| 0.3007 \| 19200 \| 0.3438 \|
	\| 0.3023 \| 19300 \| 0.3188 \|
	\| 0.3038 \| 19400 \| 0.3315 \|
	\| 0.3054 \| 19500 \| 0.3018 \|
	\| 0.3070 \| 19600 \| 0.331 \|
	\| 0.3085 \| 19700 \| 0.34 \|
	\| 0.3101 \| 19800 \| 0.2819 \|
	\| 0.3117 \| 19900 \| 0.3218 \|
	\| 0.3132 \| 20000 \| 0.3026 \|
	\| 0.3148 \| 20100 \| 0.3341 \|
	\| 0.3164 \| 20200 \| 0.285 \|
	\| 0.3179 \| 20300 \| 0.3076 \|
	\| 0.3195 \| 20400 \| 0.3262 \|
	\| 0.3211 \| 20500 \| 0.3225 \|
	\| 0.3226 \| 20600 \| 0.293 \|
	\| 0.3242 \| 20700 \| 0.3187 \|
	\| 0.3258 \| 20800 \| 0.3255 \|
	\| 0.3273 \| 20900 \| 0.2978 \|
	\| 0.3289 \| 21000 \| 0.2946 \|
	\| 0.3305 \| 21100 \| 0.2887 \|
	\| 0.3320 \| 21200 \| 0.3098 \|
	\| 0.3336 \| 21300 \| 0.2942 \|
	\| 0.3352 \| 21400 \| 0.3134 \|
	\| 0.3367 \| 21500 \| 0.267 \|
	\| 0.3383 \| 21600 \| 0.2907 \|
	\| 0.3399 \| 21700 \| 0.2919 \|
	\| 0.3414 \| 21800 \| 0.2985 \|
	\| 0.3430 \| 21900 \| 0.2815 \|
	\| 0.3446 \| 22000 \| 0.2785 \|
	\| 0.3461 \| 22100 \| 0.2932 \|
	\| 0.3477 \| 22200 \| 0.2599 \|
	\| 0.3493 \| 22300 \| 0.2697 \|
	\| 0.3508 \| 22400 \| 0.3206 \|
	\| 0.3524 \| 22500 \| 0.2874 \|
	\| 0.3540 \| 22600 \| 0.2947 \|
	\| 0.3555 \| 22700 \| 0.2863 \|
	\| 0.3571 \| 22800 \| 0.2906 \|
	\| 0.3587 \| 22900 \| 0.3155 \|
	\| 0.3602 \| 23000 \| 0.304 \|
	\| 0.3618 \| 23100 \| 0.2769 \|
	\| 0.3634 \| 23200 \| 0.3024 \|
	\| 0.3649 \| 23300 \| 0.2877 \|
	\| 0.3665 \| 23400 \| 0.2907 \|
	\| 0.3681 \| 23500 \| 0.2813 \|
	\| 0.3696 \| 23600 \| 0.3059 \|
	\| 0.3712 \| 23700 \| 0.3004 \|
	\| 0.3727 \| 23800 \| 0.261 \|
	\| 0.3743 \| 23900 \| 0.2952 \|
	\| 0.3759 \| 24000 \| 0.2687 \|
	\| 0.3774 \| 24100 \| 0.2645 \|
	\| 0.3790 \| 24200 \| 0.323 \|
	\| 0.3806 \| 24300 \| 0.2982 \|
	\| 0.3821 \| 24400 \| 0.2797 \|
	\| 0.3837 \| 24500 \| 0.2661 \|
	\| 0.3853 \| 24600 \| 0.251 \|
	\| 0.3868 \| 24700 \| 0.2991 \|
	\| 0.3884 \| 24800 \| 0.2634 \|
	\| 0.3900 \| 24900 \| 0.2716 \|
	\| 0.3915 \| 25000 \| 0.2902 \|
	\| 0.3931 \| 25100 \| 0.276 \|
	\| 0.3947 \| 25200 \| 0.2695 \|
	\| 0.3962 \| 25300 \| 0.2415 \|
	\| 0.3978 \| 25400 \| 0.2694 \|
	\| 0.3994 \| 25500 \| 0.2604 \|
	\| 0.4009 \| 25600 \| 0.2966 \|
	\| 0.4025 \| 25700 \| 0.2798 \|
	\| 0.4041 \| 25800 \| 0.2354 \|
	\| 0.4056 \| 25900 \| 0.3068 \|
	\| 0.4072 \| 26000 \| 0.2434 \|
	\| 0.4088 \| 26100 \| 0.24 \|
	\| 0.4103 \| 26200 \| 0.2888 \|
	\| 0.4119 \| 26300 \| 0.2525 \|
	\| 0.4135 \| 26400 \| 0.2632 \|
	\| 0.4150 \| 26500 \| 0.2643 \|
	\| 0.4166 \| 26600 \| 0.2585 \|
	\| 0.4182 \| 26700 \| 0.236 \|
	\| 0.4197 \| 26800 \| 0.2796 \|
	\| 0.4213 \| 26900 \| 0.2658 \|
	\| 0.4229 \| 27000 \| 0.241 \|
	\| 0.4244 \| 27100 \| 0.2764 \|
	\| 0.4260 \| 27200 \| 0.2534 \|
	\| 0.4276 \| 27300 \| 0.2572 \|
	\| 0.4291 \| 27400 \| 0.2513 \|
	\| 0.4307 \| 27500 \| 0.2254 \|
	\| 0.4323 \| 27600 \| 0.2734 \|
	\| 0.4338 \| 27700 \| 0.2459 \|
	\| 0.4354 \| 27800 \| 0.2202 \|
	\| 0.4370 \| 27900 \| 0.2583 \|
	\| 0.4385 \| 28000 \| 0.2741 \|
	\| 0.4401 \| 28100 \| 0.2329 \|
	\| 0.4417 \| 28200 \| 0.2262 \|
	\| 0.4432 \| 28300 \| 0.2573 \|
	\| 0.4448 \| 28400 \| 0.2559 \|
	\| 0.4464 \| 28500 \| 0.3188 \|
	\| 0.4479 \| 28600 \| 0.2431 \|
	\| 0.4495 \| 28700 \| 0.275 \|
	\| 0.4511 \| 28800 \| 0.25 \|
	\| 0.4526 \| 28900 \| 0.2721 \|
	\| 0.4542 \| 29000 \| 0.2401 \|
	\| 0.4558 \| 29100 \| 0.2435 \|
	\| 0.4573 \| 29200 \| 0.2703 \|
	\| 0.4589 \| 29300 \| 0.2266 \|
	\| 0.4605 \| 29400 \| 0.263 \|
	\| 0.4620 \| 29500 \| 0.242 \|
	\| 0.4636 \| 29600 \| 0.2844 \|
	\| 0.4652 \| 29700 \| 0.2317 \|
	\| 0.4667 \| 29800 \| 0.2768 \|
	\| 0.4683 \| 29900 \| 0.2496 \|
	\| 0.4699 \| 30000 \| 0.2377 \|
	\| 0.4714 \| 30100 \| 0.2813 \|
	\| 0.4730 \| 30200 \| 0.2175 \|
	\| 0.4745 \| 30300 \| 0.2502 \|
	\| 0.4761 \| 30400 \| 0.2591 \|
	\| 0.4777 \| 30500 \| 0.2547 \|
	\| 0.4792 \| 30600 \| 0.2521 \|
	\| 0.4808 \| 30700 \| 0.263 \|
	\| 0.4824 \| 30800 \| 0.1986 \|
	\| 0.4839 \| 30900 \| 0.2437 \|
	\| 0.4855 \| 31000 \| 0.2397 \|
	\| 0.4871 \| 31100 \| 0.2424 \|
	\| 0.4886 \| 31200 \| 0.2785 \|
	\| 0.4902 \| 31300 \| 0.2517 \|
	\| 0.4918 \| 31400 \| 0.2467 \|
	\| 0.4933 \| 31500 \| 0.242 \|
	\| 0.4949 \| 31600 \| 0.26 \|
	\| 0.4965 \| 31700 \| 0.2345 \|
	\| 0.4980 \| 31800 \| 0.2228 \|
	\| 0.4996 \| 31900 \| 0.2455 \|
	\| 0.5012 \| 32000 \| 0.2505 \|
	\| 0.5027 \| 32100 \| 0.2352 \|
	\| 0.5043 \| 32200 \| 0.2529 \|
	\| 0.5059 \| 32300 \| 0.2537 \|
	\| 0.5074 \| 32400 \| 0.2147 \|
	\| 0.5090 \| 32500 \| 0.2085 \|
	\| 0.5106 \| 32600 \| 0.2472 \|
	\| 0.5121 \| 32700 \| 0.2487 \|
	\| 0.5137 \| 32800 \| 0.2543 \|
	\| 0.5153 \| 32900 \| 0.2519 \|
	\| 0.5168 \| 33000 \| 0.2589 \|
	\| 0.5184 \| 33100 \| 0.2232 \|
	\| 0.5200 \| 33200 \| 0.2148 \|
	\| 0.5215 \| 33300 \| 0.2377 \|
	\| 0.5231 \| 33400 \| 0.2311 \|
	\| 0.5247 \| 33500 \| 0.2153 \|
	\| 0.5262 \| 33600 \| 0.2138 \|
	\| 0.5278 \| 33700 \| 0.218 \|
	\| 0.5294 \| 33800 \| 0.2298 \|
	\| 0.5309 \| 33900 \| 0.2663 \|
	\| 0.5325 \| 34000 \| 0.2489 \|
	\| 0.5341 \| 34100 \| 0.2129 \|
	\| 0.5356 \| 34200 \| 0.2298 \|
	\| 0.5372 \| 34300 \| 0.2742 \|
	\| 0.5388 \| 34400 \| 0.2389 \|
	\| 0.5403 \| 34500 \| 0.2232 \|
	\| 0.5419 \| 34600 \| 0.1931 \|
	\| 0.5435 \| 34700 \| 0.2504 \|
	\| 0.5450 \| 34800 \| 0.2349 \|
	\| 0.5466 \| 34900 \| 0.22 \|
	\| 0.5482 \| 35000 \| 0.249 \|
	\| 0.5497 \| 35100 \| 0.2541 \|
	\| 0.5513 \| 35200 \| 0.2406 \|
	\| 0.5529 \| 35300 \| 0.2168 \|
	\| 0.5544 \| 35400 \| 0.2481 \|
	\| 0.5560 \| 35500 \| 0.2274 \|
	\| 0.5576 \| 35600 \| 0.2168 \|
	\| 0.5591 \| 35700 \| 0.2443 \|
	\| 0.5607 \| 35800 \| 0.2378 \|
	\| 0.5623 \| 35900 \| 0.2364 \|
	\| 0.5638 \| 36000 \| 0.2232 \|
	\| 0.5654 \| 36100 \| 0.2044 \|
	\| 0.5670 \| 36200 \| 0.2153 \|
	\| 0.5685 \| 36300 \| 0.2178 \|
	\| 0.5701 \| 36400 \| 0.2314 \|
	\| 0.5717 \| 36500 \| 0.2448 \|
	\| 0.5732 \| 36600 \| 0.2652 \|
	\| 0.5748 \| 36700 \| 0.2315 \|
	\| 0.5764 \| 36800 \| 0.2071 \|
	\| 0.5779 \| 36900 \| 0.2267 \|
	\| 0.5795 \| 37000 \| 0.2797 \|
	\| 0.5810 \| 37100 \| 0.2053 \|
	\| 0.5826 \| 37200 \| 0.2331 \|
	\| 0.5842 \| 37300 \| 0.2231 \|
	\| 0.5857 \| 37400 \| 0.2135 \|
	\| 0.5873 \| 37500 \| 0.2424 \|
	\| 0.5889 \| 37600 \| 0.2345 \|
	\| 0.5904 \| 37700 \| 0.2111 \|
	\| 0.5920 \| 37800 \| 0.2553 \|
	\| 0.5936 \| 37900 \| 0.2252 \|
	\| 0.5951 \| 38000 \| 0.2033 \|
	\| 0.5967 \| 38100 \| 0.2284 \|
	\| 0.5983 \| 38200 \| 0.213 \|
	\| 0.5998 \| 38300 \| 0.195 \|
	\| 0.6014 \| 38400 \| 0.1886 \|
	\| 0.6030 \| 38500 \| 0.2192 \|
	\| 0.6045 \| 38600 \| 0.2569 \|
	\| 0.6061 \| 38700 \| 0.1765 \|
	\| 0.6077 \| 38800 \| 0.2127 \|
	\| 0.6092 \| 38900 \| 0.2213 \|
	\| 0.6108 \| 39000 \| 0.2217 \|
	\| 0.6124 \| 39100 \| 0.2163 \|
	\| 0.6139 \| 39200 \| 0.2141 \|
	\| 0.6155 \| 39300 \| 0.2255 \|
	\| 0.6171 \| 39400 \| 0.2326 \|
	\| 0.6186 \| 39500 \| 0.2005 \|
	\| 0.6202 \| 39600 \| 0.2043 \|
	\| 0.6218 \| 39700 \| 0.2122 \|
	\| 0.6233 \| 39800 \| 0.2212 \|
	\| 0.6249 \| 39900 \| 0.2265 \|
	\| 0.6265 \| 40000 \| 0.2259 \|
	\| 0.6280 \| 40100 \| 0.2456 \|
	\| 0.6296 \| 40200 \| 0.2037 \|
	\| 0.6312 \| 40300 \| 0.2082 \|
	\| 0.6327 \| 40400 \| 0.2284 \|
	\| 0.6343 \| 40500 \| 0.2246 \|
	\| 0.6359 \| 40600 \| 0.1884 \|
	\| 0.6374 \| 40700 \| 0.1909 \|
	\| 0.6390 \| 40800 \| 0.2038 \|
	\| 0.6406 \| 40900 \| 0.2249 \|
	\| 0.6421 \| 41000 \| 0.2211 \|
	\| 0.6437 \| 41100 \| 0.2267 \|
	\| 0.6453 \| 41200 \| 0.1926 \|
	\| 0.6468 \| 41300 \| 0.1787 \|
	\| 0.6484 \| 41400 \| 0.2209 \|
	\| 0.6500 \| 41500 \| 0.2091 \|
	\| 0.6515 \| 41600 \| 0.2064 \|
	\| 0.6531 \| 41700 \| 0.2093 \|
	\| 0.6547 \| 41800 \| 0.2413 \|
	\| 0.6562 \| 41900 \| 0.2141 \|
	\| 0.6578 \| 42000 \| 0.2293 \|
	\| 0.6594 \| 42100 \| 0.2084 \|
	\| 0.6609 \| 42200 \| 0.2095 \|
	\| 0.6625 \| 42300 \| 0.2162 \|
	\| 0.6641 \| 42400 \| 0.2188 \|
	\| 0.6656 \| 42500 \| 0.1992 \|
	\| 0.6672 \| 42600 \| 0.2216 \|
	\| 0.6688 \| 42700 \| 0.2338 \|
	\| 0.6703 \| 42800 \| 0.1941 \|
	\| 0.6719 \| 42900 \| 0.2122 \|
	\| 0.6735 \| 43000 \| 0.194 \|
	\| 0.6750 \| 43100 \| 0.2413 \|
	\| 0.6766 \| 43200 \| 0.232 \|
	\| 0.6782 \| 43300 \| 0.2115 \|
	\| 0.6797 \| 43400 \| 0.2172 \|
	\| 0.6813 \| 43500 \| 0.2122 \|
	\| 0.6829 \| 43600 \| 0.2059 \|
	\| 0.6844 \| 43700 \| 0.2085 \|
	\| 0.6860 \| 43800 \| 0.2045 \|
	\| 0.6875 \| 43900 \| 0.1893 \|
	\| 0.6891 \| 44000 \| 0.204 \|
	\| 0.6907 \| 44100 \| 0.1991 \|
	\| 0.6922 \| 44200 \| 0.2342 \|
	\| 0.6938 \| 44300 \| 0.1834 \|
	\| 0.6954 \| 44400 \| 0.1979 \|
	\| 0.6969 \| 44500 \| 0.2302 \|
	\| 0.6985 \| 44600 \| 0.2144 \|
	\| 0.7001 \| 44700 \| 0.185 \|
	\| 0.7016 \| 44800 \| 0.2014 \|
	\| 0.7032 \| 44900 \| 0.1772 \|
	\| 0.7048 \| 45000 \| 0.1967 \|
	\| 0.7063 \| 45100 \| 0.1924 \|
	\| 0.7079 \| 45200 \| 0.2114 \|
	\| 0.7095 \| 45300 \| 0.2091 \|
	\| 0.7110 \| 45400 \| 0.2044 \|
	\| 0.7126 \| 45500 \| 0.2246 \|
	\| 0.7142 \| 45600 \| 0.2109 \|
	\| 0.7157 \| 45700 \| 0.1772 \|
	\| 0.7173 \| 45800 \| 0.1988 \|
	\| 0.7189 \| 45900 \| 0.2183 \|
	\| 0.7204 \| 46000 \| 0.1918 \|
	\| 0.7220 \| 46100 \| 0.2332 \|
	\| 0.7236 \| 46200 \| 0.2097 \|
	\| 0.7251 \| 46300 \| 0.2005 \|
	\| 0.7267 \| 46400 \| 0.189 \|
	\| 0.7283 \| 46500 \| 0.1993 \|
	\| 0.7298 \| 46600 \| 0.2224 \|
	\| 0.7314 \| 46700 \| 0.2 \|
	\| 0.7330 \| 46800 \| 0.1949 \|
	\| 0.7345 \| 46900 \| 0.2061 \|
	\| 0.7361 \| 47000 \| 0.211 \|
	\| 0.7377 \| 47100 \| 0.2393 \|
	\| 0.7392 \| 47200 \| 0.2498 \|
	\| 0.7408 \| 47300 \| 0.1811 \|
	\| 0.7424 \| 47400 \| 0.1873 \|
	\| 0.7439 \| 47500 \| 0.2238 \|
	\| 0.7455 \| 47600 \| 0.1918 \|
	\| 0.7471 \| 47700 \| 0.1805 \|
	\| 0.7486 \| 47800 \| 0.2256 \|
	\| 0.7502 \| 47900 \| 0.1901 \|
	\| 0.7518 \| 48000 \| 0.2344 \|
	\| 0.7533 \| 48100 \| 0.2212 \|
	\| 0.7549 \| 48200 \| 0.2089 \|
	\| 0.7565 \| 48300 \| 0.2169 \|
	\| 0.7580 \| 48400 \| 0.2152 \|
	\| 0.7596 \| 48500 \| 0.1831 \|
	\| 0.7612 \| 48600 \| 0.1521 \|
	\| 0.7627 \| 48700 \| 0.2177 \|
	\| 0.7643 \| 48800 \| 0.2035 \|
	\| 0.7659 \| 48900 \| 0.1713 \|
	\| 0.7674 \| 49000 \| 0.2547 \|
	\| 0.7690 \| 49100 \| 0.1802 \|
	\| 0.7706 \| 49200 \| 0.1975 \|
	\| 0.7721 \| 49300 \| 0.2107 \|
	\| 0.7737 \| 49400 \| 0.2078 \|
	\| 0.7753 \| 49500 \| 0.1917 \|
	\| 0.7768 \| 49600 \| 0.1917 \|
	\| 0.7784 \| 49700 \| 0.1948 \|
	\| 0.7800 \| 49800 \| 0.1881 \|
	\| 0.7815 \| 49900 \| 0.1799 \|
	\| 0.7831 \| 50000 \| 0.2184 \|
	\| 0.7847 \| 50100 \| 0.2323 \|
	\| 0.7862 \| 50200 \| 0.1949 \|
	\| 0.7878 \| 50300 \| 0.1908 \|
	\| 0.7894 \| 50400 \| 0.182 \|
	\| 0.7909 \| 50500 \| 0.1783 \|
	\| 0.7925 \| 50600 \| 0.2187 \|
	\| 0.7940 \| 50700 \| 0.1711 \|
	\| 0.7956 \| 50800 \| 0.2127 \|
	\| 0.7972 \| 50900 \| 0.1886 \|
	\| 0.7987 \| 51000 \| 0.1825 \|
	\| 0.8003 \| 51100 \| 0.206 \|
	\| 0.8019 \| 51200 \| 0.2058 \|
	\| 0.8034 \| 51300 \| 0.2065 \|
	\| 0.8050 \| 51400 \| 0.1857 \|
	\| 0.8066 \| 51500 \| 0.1853 \|
	\| 0.8081 \| 51600 \| 0.2035 \|
	\| 0.8097 \| 51700 \| 0.194 \|
	\| 0.8113 \| 51800 \| 0.2157 \|
	\| 0.8128 \| 51900 \| 0.1965 \|
	\| 0.8144 \| 52000 \| 0.1924 \|
	\| 0.8160 \| 52100 \| 0.1995 \|
	\| 0.8175 \| 52200 \| 0.2166 \|
	\| 0.8191 \| 52300 \| 0.15 \|
	\| 0.8207 \| 52400 \| 0.1507 \|
	\| 0.8222 \| 52500 \| 0.2096 \|
	\| 0.8238 \| 52600 \| 0.205 \|
	\| 0.8254 \| 52700 \| 0.207 \|
	\| 0.8269 \| 52800 \| 0.1735 \|
	\| 0.8285 \| 52900 \| 0.1748 \|
	\| 0.8301 \| 53000 \| 0.2401 \|
	\| 0.8316 \| 53100 \| 0.1749 \|
	\| 0.8332 \| 53200 \| 0.1996 \|
	\| 0.8348 \| 53300 \| 0.194 \|
	\| 0.8363 \| 53400 \| 0.1856 \|
	\| 0.8379 \| 53500 \| 0.1926 \|
	\| 0.8395 \| 53600 \| 0.1914 \|
	\| 0.8410 \| 53700 \| 0.1988 \|
	\| 0.8426 \| 53800 \| 0.1778 \|
	\| 0.8442 \| 53900 \| 0.1884 \|
	\| 0.8457 \| 54000 \| 0.1965 \|
	\| 0.8473 \| 54100 \| 0.2086 \|
	\| 0.8489 \| 54200 \| 0.1934 \|
	\| 0.8504 \| 54300 \| 0.1789 \|
	\| 0.8520 \| 54400 \| 0.1947 \|
	\| 0.8536 \| 54500 \| 0.1768 \|
	\| 0.8551 \| 54600 \| 0.2194 \|
	\| 0.8567 \| 54700 \| 0.1944 \|
	\| 0.8583 \| 54800 \| 0.1946 \|
	\| 0.8598 \| 54900 \| 0.1998 \|
	\| 0.8614 \| 55000 \| 0.1716 \|
	\| 0.8630 \| 55100 \| 0.202 \|
	\| 0.8645 \| 55200 \| 0.2069 \|
	\| 0.8661 \| 55300 \| 0.2221 \|
	\| 0.8677 \| 55400 \| 0.1859 \|
	\| 0.8692 \| 55500 \| 0.1817 \|
	\| 0.8708 \| 55600 \| 0.2091 \|
	\| 0.8724 \| 55700 \| 0.1756 \|
	\| 0.8739 \| 55800 \| 0.1982 \|
	\| 0.8755 \| 55900 \| 0.1947 \|
	\| 0.8771 \| 56000 \| 0.1745 \|
	\| 0.8786 \| 56100 \| 0.1914 \|
	\| 0.8802 \| 56200 \| 0.1867 \|
	\| 0.8818 \| 56300 \| 0.1935 \|
	\| 0.8833 \| 56400 \| 0.1844 \|
	\| 0.8849 \| 56500 \| 0.1704 \|
	\| 0.8865 \| 56600 \| 0.2127 \|
	\| 0.8880 \| 56700 \| 0.224 \|
	\| 0.8896 \| 56800 \| 0.2092 \|
	\| 0.8912 \| 56900 \| 0.2042 \|
	\| 0.8927 \| 57000 \| 0.1898 \|
	\| 0.8943 \| 57100 \| 0.1515 \|
	\| 0.8958 \| 57200 \| 0.1952 \|
	\| 0.8974 \| 57300 \| 0.17 \|
	\| 0.8990 \| 57400 \| 0.1843 \|
	\| 0.9005 \| 57500 \| 0.2019 \|
	\| 0.9021 \| 57600 \| 0.1724 \|
	\| 0.9037 \| 57700 \| 0.1912 \|
	\| 0.9052 \| 57800 \| 0.1979 \|
	\| 0.9068 \| 57900 \| 0.2014 \|
	\| 0.9084 \| 58000 \| 0.2063 \|
	\| 0.9099 \| 58100 \| 0.1794 \|
	\| 0.9115 \| 58200 \| 0.1972 \|
	\| 0.9131 \| 58300 \| 0.1501 \|
	\| 0.9146 \| 58400 \| 0.2001 \|
	\| 0.9162 \| 58500 \| 0.2082 \|
	\| 0.9178 \| 58600 \| 0.2076 \|
	\| 0.9193 \| 58700 \| 0.1722 \|
	\| 0.9209 \| 58800 \| 0.1954 \|
	\| 0.9225 \| 58900 \| 0.1604 \|
	\| 0.9240 \| 59000 \| 0.1816 \|
	\| 0.9256 \| 59100 \| 0.1809 \|
	\| 0.9272 \| 59200 \| 0.1762 \|
	\| 0.9287 \| 59300 \| 0.215 \|
	\| 0.9303 \| 59400 \| 0.1953 \|
	\| 0.9319 \| 59500 \| 0.1865 \|
	\| 0.9334 \| 59600 \| 0.208 \|
	\| 0.9350 \| 59700 \| 0.2035 \|
	\| 0.9366 \| 59800 \| 0.1966 \|
	\| 0.9381 \| 59900 \| 0.1777 \|
	\| 0.9397 \| 60000 \| 0.2044 \|
	\| 0.9413 \| 60100 \| 0.1773 \|
	\| 0.9428 \| 60200 \| 0.1843 \|
	\| 0.9444 \| 60300 \| 0.1786 \|
	\| 0.9460 \| 60400 \| 0.1958 \|
	\| 0.9475 \| 60500 \| 0.1959 \|
	\| 0.9491 \| 60600 \| 0.2047 \|
	\| 0.9507 \| 60700 \| 0.2 \|
	\| 0.9522 \| 60800 \| 0.1843 \|
	\| 0.9538 \| 60900 \| 0.1946 \|
	\| 0.9554 \| 61000 \| 0.1752 \|
	\| 0.9569 \| 61100 \| 0.1724 \|
	\| 0.9585 \| 61200 \| 0.1701 \|
	\| 0.9601 \| 61300 \| 0.1791 \|
	\| 0.9616 \| 61400 \| 0.1731 \|
	\| 0.9632 \| 61500 \| 0.203 \|
	\| 0.9648 \| 61600 \| 0.1985 \|
	\| 0.9663 \| 61700 \| 0.1968 \|
	\| 0.9679 \| 61800 \| 0.1719 \|
	\| 0.9695 \| 61900 \| 0.1608 \|
	\| 0.9710 \| 62000 \| 0.1691 \|
	\| 0.9726 \| 62100 \| 0.1761 \|
	\| 0.9742 \| 62200 \| 0.1805 \|
	\| 0.9757 \| 62300 \| 0.1732 \|
	\| 0.9773 \| 62400 \| 0.1657 \|
	\| 0.9789 \| 62500 \| 0.1757 \|
	\| 0.9804 \| 62600 \| 0.157 \|
	\| 0.9820 \| 62700 \| 0.1995 \|
	\| 0.9836 \| 62800 \| 0.1937 \|
	\| 0.9851 \| 62900 \| 0.1839 \|
	\| 0.9867 \| 63000 \| 0.194 \|
	\| 0.9883 \| 63100 \| 0.1755 \|
	\| 0.9898 \| 63200 \| 0.1819 \|
	\| 0.9914 \| 63300 \| 0.1918 \|
	\| 0.9930 \| 63400 \| 0.1636 \|
	\| 0.9945 \| 63500 \| 0.1731 \|
	\| 0.9961 \| 63600 \| 0.1671 \|
	\| 0.9977 \| 63700 \| 0.1704 \|
	\| 0.9992 \| 63800 \| 0.2089 \|

	* The bold row denotes the saved checkpoint.
	</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}
	}
	```

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