Add special tokens [TEXT], [YEAR_RANGE]

138e85b verified 21 days ago

47.8 kB

	---
	library_name: sentence-transformers
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:187491593
	- loss:CustomTripletLoss
	widget:
	- source_sentence: 1.2 ML temsirolimus 25 MG/ML Injection
	sentences:
	- Temsirolimus 25 MG/1 ML Intravenous Solution
	- C3537356
	- C1949367
	- 0.5 ML influenza A virus vaccine, A-Victoria-361-2011 (H3N2)-like virus 0.03 MG/ML
	/ influenza A-California-7-2009-(H1N1)v-like virus vaccine 0.03 MG/ML / influenza
	B virus vaccine B/Brisbane/60/2008 antigen 0.03 MG/ML / influenza B virus vaccine,
	B-Massachusetts-2-2012-like virus 0.03 MG/ML Prefilled Syringe [Fluzone Quadrivalent
	2013-2014 Formula]
	- source_sentence: spastic ataxia type 2
	sentences:
	- ZPLD2P gene
	- C5240110
	- Kinesin Family Member 1C wt Allele
	- C5443974
	- source_sentence: アルコール性発作
	sentences:
	- C0586323
	- C4295585
	- epilepsy; alcohol
	- HELLP syndrome second trimester
	- source_sentence: Tergitol
	sentences:
	- C0439129
	- F 82526
	- C1563639
	- CD 3 color developer, sulfate, hydrate (2:3:1)
	- source_sentence: Albendazol
	sentences:
	- N-(p-(((2,4-diamino-5-methyl-6-quinazolinyl)methyl)amino)benzoyl)-L-glutamic acid
	- C0699923
	- C0130494
	- SKF-92058
	---

	# SentenceTransformer

	This is a [sentence-transformers](https://www.SBERT.net) model trained. 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: [Unknown](https://huggingface.co/unknown) -->
	- Maximum Sequence Length: 1024 tokens
	- Output Dimensionality: 384 tokens
	- 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': 1024, '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})
	)
	```

	## 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("pankajrajdeo/2812371_bioformer_16L")
	# Run inference
	sentences = [
	'Albendazol',
	'SKF-92058',
	'C0130494',
	]
	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: 187,491,593 training samples
	* Columns: <code>anchor</code>, <code>positive</code>, <code>negative_id</code>, <code>positive_id</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative_id \| positive_id \| negative \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \| string \| string \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 13.27 tokens</li><li>max: 247 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 12.25 tokens</li><li>max: 157 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 6.27 tokens</li><li>max: 7 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 6.49 tokens</li><li>max: 7 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 13.53 tokens</li><li>max: 118 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative_id \| positive_id \| negative \|
	\|:----------------------------------------------\|:------------------------------------------------------------------------------------------------\|:----------------------\|:----------------------\|:------------------------------------------------------------------------------------------------\|
	\| <code>Zaburzenie metabolizmu minerałów</code> \| <code>Distúrbio não especificado do metabolismo de minerais</code> \| <code>C2887914</code> \| <code>C0154260</code> \| <code>Acute alcoholic hepatic failure</code> \|
	\| <code>testy funkčnosti placenty</code> \| <code>Metoder som brukes til å vurdere morkakefunksjon.</code> \| <code>C2350391</code> \| <code>C0032049</code> \| <code>Hjärtmuskelscintigrafi</code> \|
	\| <code>Tsefapiriin:Susc:Pt:Is:OrdQn</code> \| <code>cefapirina:susceptibilidad:punto en el tiempo:cepa clínica:ordinal o cuantitativo:</code> \| <code>C0942365</code> \| <code>C0801894</code> \| <code>2 proyecciones:hallazgo:punto en el tiempo:tobillo.izquierdo:Narrativo:radiografía</code> \|
	* Loss: <code>__main__.CustomTripletLoss</code> with these parameters:
	```json
	{
	"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
	"triplet_margin": 5
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `per_device_train_batch_size`: 50
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 5
	- `warmup_ratio`: 0.1
	- `fp16`: True

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: no
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 50
	- `per_device_eval_batch_size`: 8
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_steps`: None
	- `learning_rate`: 2e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 5
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.1
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: True
	- `save_on_each_node`: False
	- `save_only_model`: False
	- `restore_callback_states_from_checkpoint`: False
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `use_ipex`: False
	- `bf16`: False
	- `fp16`: True
	- `fp16_opt_level`: O1
	- `half_precision_backend`: auto
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `local_rank`: 0
	- `ddp_backend`: None
	- `tpu_num_cores`: None
	- `tpu_metrics_debug`: False
	- `debug`: []
	- `dataloader_drop_last`: False
	- `dataloader_num_workers`: 0
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: False
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_min_num_params`: 0
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `fsdp_transformer_layer_cls_to_wrap`: None
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch
	- `optim_args`: None
	- `adafactor`: False
	- `group_by_length`: False
	- `length_column_name`: length
	- `ddp_find_unused_parameters`: None
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `skip_memory_metrics`: True
	- `use_legacy_prediction_loop`: False
	- `push_to_hub`: False
	- `resume_from_checkpoint`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_private_repo`: False
	- `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`: False
	- `use_liger_kernel`: False
	- `eval_use_gather_object`: False
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	<details><summary>Click to expand</summary>

	\| Epoch \| Step \| Training Loss \|
	\|:------:\|:-------:\|:-------------:\|
	\| 0.5000 \| 1875000 \| 0.1053 \|
	\| 0.5003 \| 1876000 \| 0.0899 \|
	\| 0.5006 \| 1877000 \| 0.0978 \|
	\| 0.5008 \| 1878000 \| 0.0928 \|
	\| 0.5011 \| 1879000 \| 0.0887 \|
	\| 0.5014 \| 1880000 \| 0.0921 \|
	\| 0.5016 \| 1881000 \| 0.0908 \|
	\| 0.5019 \| 1882000 \| 0.0925 \|
	\| 0.5022 \| 1883000 \| 0.0886 \|
	\| 0.5024 \| 1884000 \| 0.0924 \|
	\| 0.5027 \| 1885000 \| 0.0932 \|
	\| 0.5030 \| 1886000 \| 0.0938 \|
	\| 0.5032 \| 1887000 \| 0.0976 \|
	\| 0.5035 \| 1888000 \| 0.087 \|
	\| 0.5038 \| 1889000 \| 0.0882 \|
	\| 0.5040 \| 1890000 \| 0.0955 \|
	\| 0.5043 \| 1891000 \| 0.0927 \|
	\| 0.5046 \| 1892000 \| 0.0922 \|
	\| 0.5048 \| 1893000 \| 0.086 \|
	\| 0.5051 \| 1894000 \| 0.0899 \|
	\| 0.5054 \| 1895000 \| 0.0941 \|
	\| 0.5056 \| 1896000 \| 0.0924 \|
	\| 0.5059 \| 1897000 \| 0.0941 \|
	\| 0.5062 \| 1898000 \| 0.0904 \|
	\| 0.5064 \| 1899000 \| 0.09 \|
	\| 0.5067 \| 1900000 \| 0.0928 \|
	\| 0.5070 \| 1901000 \| 0.088 \|
	\| 0.5072 \| 1902000 \| 0.0924 \|
	\| 0.5075 \| 1903000 \| 0.0927 \|
	\| 0.5078 \| 1904000 \| 0.0912 \|
	\| 0.5080 \| 1905000 \| 0.0971 \|
	\| 0.5083 \| 1906000 \| 0.0973 \|
	\| 0.5086 \| 1907000 \| 0.0932 \|
	\| 0.5088 \| 1908000 \| 0.092 \|
	\| 0.5091 \| 1909000 \| 0.0894 \|
	\| 0.5094 \| 1910000 \| 0.0866 \|
	\| 0.5096 \| 1911000 \| 0.0951 \|
	\| 0.5099 \| 1912000 \| 0.0924 \|
	\| 0.5102 \| 1913000 \| 0.0913 \|
	\| 0.5104 \| 1914000 \| 0.0921 \|
	\| 0.5107 \| 1915000 \| 0.0915 \|
	\| 0.5110 \| 1916000 \| 0.0897 \|
	\| 0.5112 \| 1917000 \| 0.0932 \|
	\| 0.5115 \| 1918000 \| 0.0871 \|
	\| 0.5118 \| 1919000 \| 0.0872 \|
	\| 0.5120 \| 1920000 \| 0.0962 \|
	\| 0.5123 \| 1921000 \| 0.0902 \|
	\| 0.5126 \| 1922000 \| 0.0939 \|
	\| 0.5128 \| 1923000 \| 0.0873 \|
	\| 0.5131 \| 1924000 \| 0.0841 \|
	\| 0.5134 \| 1925000 \| 0.0863 \|
	\| 0.5136 \| 1926000 \| 0.0941 \|
	\| 0.5139 \| 1927000 \| 0.0905 \|
	\| 0.5142 \| 1928000 \| 0.0876 \|
	\| 0.5144 \| 1929000 \| 0.0866 \|
	\| 0.5147 \| 1930000 \| 0.0921 \|
	\| 0.5150 \| 1931000 \| 0.0973 \|
	\| 0.5152 \| 1932000 \| 0.0937 \|
	\| 0.5155 \| 1933000 \| 0.0899 \|
	\| 0.5158 \| 1934000 \| 0.0965 \|
	\| 0.5160 \| 1935000 \| 0.0942 \|
	\| 0.5163 \| 1936000 \| 0.0927 \|
	\| 0.5166 \| 1937000 \| 0.0897 \|
	\| 0.5168 \| 1938000 \| 0.094 \|
	\| 0.5171 \| 1939000 \| 0.0874 \|
	\| 0.5174 \| 1940000 \| 0.0954 \|
	\| 0.5176 \| 1941000 \| 0.0904 \|
	\| 0.5179 \| 1942000 \| 0.0913 \|
	\| 0.5182 \| 1943000 \| 0.0891 \|
	\| 0.5184 \| 1944000 \| 0.0941 \|
	\| 0.5187 \| 1945000 \| 0.0908 \|
	\| 0.5190 \| 1946000 \| 0.0903 \|
	\| 0.5192 \| 1947000 \| 0.0957 \|
	\| 0.5195 \| 1948000 \| 0.0875 \|
	\| 0.5198 \| 1949000 \| 0.0895 \|
	\| 0.5200 \| 1950000 \| 0.0883 \|
	\| 0.5203 \| 1951000 \| 0.0942 \|
	\| 0.5206 \| 1952000 \| 0.091 \|
	\| 0.5208 \| 1953000 \| 0.0874 \|
	\| 0.5211 \| 1954000 \| 0.0921 \|
	\| 0.5214 \| 1955000 \| 0.0967 \|
	\| 0.5216 \| 1956000 \| 0.0962 \|
	\| 0.5219 \| 1957000 \| 0.0942 \|
	\| 0.5222 \| 1958000 \| 0.0818 \|
	\| 0.5224 \| 1959000 \| 0.0861 \|
	\| 0.5227 \| 1960000 \| 0.0849 \|
	\| 0.5230 \| 1961000 \| 0.0894 \|
	\| 0.5232 \| 1962000 \| 0.101 \|
	\| 0.5235 \| 1963000 \| 0.0832 \|
	\| 0.5238 \| 1964000 \| 0.0901 \|
	\| 0.5240 \| 1965000 \| 0.0949 \|
	\| 0.5243 \| 1966000 \| 0.0942 \|
	\| 0.5246 \| 1967000 \| 0.0897 \|
	\| 0.5248 \| 1968000 \| 0.0894 \|
	\| 0.5251 \| 1969000 \| 0.0846 \|
	\| 0.5254 \| 1970000 \| 0.087 \|
	\| 0.5256 \| 1971000 \| 0.086 \|
	\| 0.5259 \| 1972000 \| 0.086 \|
	\| 0.5262 \| 1973000 \| 0.0913 \|
	\| 0.5264 \| 1974000 \| 0.0916 \|
	\| 0.5267 \| 1975000 \| 0.0867 \|
	\| 0.5270 \| 1976000 \| 0.085 \|
	\| 0.5272 \| 1977000 \| 0.0863 \|
	\| 0.5275 \| 1978000 \| 0.0927 \|
	\| 0.5278 \| 1979000 \| 0.0866 \|
	\| 0.5280 \| 1980000 \| 0.0865 \|
	\| 0.5283 \| 1981000 \| 0.0898 \|
	\| 0.5286 \| 1982000 \| 0.0917 \|
	\| 0.5288 \| 1983000 \| 0.0864 \|
	\| 0.5291 \| 1984000 \| 0.0937 \|
	\| 0.5294 \| 1985000 \| 0.0916 \|
	\| 0.5296 \| 1986000 \| 0.0913 \|
	\| 0.5299 \| 1987000 \| 0.0927 \|
	\| 0.5302 \| 1988000 \| 0.0947 \|
	\| 0.5304 \| 1989000 \| 0.0939 \|
	\| 0.5307 \| 1990000 \| 0.0864 \|
	\| 0.5310 \| 1991000 \| 0.0816 \|
	\| 0.5312 \| 1992000 \| 0.0931 \|
	\| 0.5315 \| 1993000 \| 0.0906 \|
	\| 0.5318 \| 1994000 \| 0.0907 \|
	\| 0.5320 \| 1995000 \| 0.0895 \|
	\| 0.5323 \| 1996000 \| 0.0913 \|
	\| 0.5326 \| 1997000 \| 0.0915 \|
	\| 0.5328 \| 1998000 \| 0.0909 \|
	\| 0.5331 \| 1999000 \| 0.0917 \|
	\| 0.5334 \| 2000000 \| 0.0828 \|
	\| 0.5336 \| 2001000 \| 0.0865 \|
	\| 0.5339 \| 2002000 \| 0.0864 \|
	\| 0.5342 \| 2003000 \| 0.0887 \|
	\| 0.5344 \| 2004000 \| 0.0871 \|
	\| 0.5347 \| 2005000 \| 0.0903 \|
	\| 0.5350 \| 2006000 \| 0.092 \|
	\| 0.5352 \| 2007000 \| 0.083 \|
	\| 0.5355 \| 2008000 \| 0.0934 \|
	\| 0.5358 \| 2009000 \| 0.0885 \|
	\| 0.5360 \| 2010000 \| 0.0841 \|
	\| 0.5363 \| 2011000 \| 0.0919 \|
	\| 0.5366 \| 2012000 \| 0.0909 \|
	\| 0.5368 \| 2013000 \| 0.0899 \|
	\| 0.5371 \| 2014000 \| 0.0905 \|
	\| 0.5374 \| 2015000 \| 0.0917 \|
	\| 0.5376 \| 2016000 \| 0.0936 \|
	\| 0.5379 \| 2017000 \| 0.0926 \|
	\| 0.5382 \| 2018000 \| 0.0884 \|
	\| 0.5384 \| 2019000 \| 0.0909 \|
	\| 0.5387 \| 2020000 \| 0.0858 \|
	\| 0.5390 \| 2021000 \| 0.0927 \|
	\| 0.5392 \| 2022000 \| 0.0908 \|
	\| 0.5395 \| 2023000 \| 0.0936 \|
	\| 0.5398 \| 2024000 \| 0.0896 \|
	\| 0.5400 \| 2025000 \| 0.0948 \|
	\| 0.5403 \| 2026000 \| 0.091 \|
	\| 0.5406 \| 2027000 \| 0.0917 \|
	\| 0.5408 \| 2028000 \| 0.0866 \|
	\| 0.5411 \| 2029000 \| 0.0925 \|
	\| 0.5414 \| 2030000 \| 0.0846 \|
	\| 0.5416 \| 2031000 \| 0.0878 \|
	\| 0.5419 \| 2032000 \| 0.0792 \|
	\| 0.5422 \| 2033000 \| 0.0872 \|
	\| 0.5424 \| 2034000 \| 0.088 \|
	\| 0.5427 \| 2035000 \| 0.0972 \|
	\| 0.5430 \| 2036000 \| 0.081 \|
	\| 0.5432 \| 2037000 \| 0.0901 \|
	\| 0.5435 \| 2038000 \| 0.092 \|
	\| 0.5438 \| 2039000 \| 0.0902 \|
	\| 0.5440 \| 2040000 \| 0.091 \|
	\| 0.5443 \| 2041000 \| 0.0876 \|
	\| 0.5446 \| 2042000 \| 0.0799 \|
	\| 0.5448 \| 2043000 \| 0.0921 \|
	\| 0.5451 \| 2044000 \| 0.0823 \|
	\| 0.5454 \| 2045000 \| 0.0846 \|
	\| 0.5456 \| 2046000 \| 0.0863 \|
	\| 0.5459 \| 2047000 \| 0.0893 \|
	\| 0.5462 \| 2048000 \| 0.0829 \|
	\| 0.5464 \| 2049000 \| 0.0913 \|
	\| 0.5467 \| 2050000 \| 0.0956 \|
	\| 0.5470 \| 2051000 \| 0.0879 \|
	\| 0.5472 \| 2052000 \| 0.0849 \|
	\| 0.5475 \| 2053000 \| 0.0931 \|
	\| 0.5478 \| 2054000 \| 0.0822 \|
	\| 0.5480 \| 2055000 \| 0.086 \|
	\| 0.5483 \| 2056000 \| 0.0866 \|
	\| 0.5486 \| 2057000 \| 0.0943 \|
	\| 0.5488 \| 2058000 \| 0.0868 \|
	\| 0.5491 \| 2059000 \| 0.0918 \|
	\| 0.5494 \| 2060000 \| 0.0856 \|
	\| 0.5496 \| 2061000 \| 0.0841 \|
	\| 0.5499 \| 2062000 \| 0.0838 \|
	\| 0.5502 \| 2063000 \| 0.0906 \|
	\| 0.5504 \| 2064000 \| 0.0892 \|
	\| 0.5507 \| 2065000 \| 0.092 \|
	\| 0.5510 \| 2066000 \| 0.0917 \|
	\| 0.5512 \| 2067000 \| 0.0929 \|
	\| 0.5515 \| 2068000 \| 0.0847 \|
	\| 0.5518 \| 2069000 \| 0.0862 \|
	\| 0.5520 \| 2070000 \| 0.0879 \|
	\| 0.5523 \| 2071000 \| 0.0867 \|
	\| 0.5526 \| 2072000 \| 0.0868 \|
	\| 0.5528 \| 2073000 \| 0.0911 \|
	\| 0.5531 \| 2074000 \| 0.0869 \|
	\| 0.5534 \| 2075000 \| 0.0858 \|
	\| 0.5536 \| 2076000 \| 0.0882 \|
	\| 0.5539 \| 2077000 \| 0.086 \|
	\| 0.5542 \| 2078000 \| 0.0868 \|
	\| 0.5544 \| 2079000 \| 0.0879 \|
	\| 0.5547 \| 2080000 \| 0.0847 \|
	\| 0.5550 \| 2081000 \| 0.0907 \|
	\| 0.5552 \| 2082000 \| 0.0897 \|
	\| 0.5555 \| 2083000 \| 0.0894 \|
	\| 0.5558 \| 2084000 \| 0.0939 \|
	\| 0.5560 \| 2085000 \| 0.0878 \|
	\| 0.5563 \| 2086000 \| 0.0885 \|
	\| 0.5566 \| 2087000 \| 0.0905 \|
	\| 0.5568 \| 2088000 \| 0.092 \|
	\| 0.5571 \| 2089000 \| 0.0845 \|
	\| 0.5574 \| 2090000 \| 0.0854 \|
	\| 0.5576 \| 2091000 \| 0.0896 \|
	\| 0.5579 \| 2092000 \| 0.0858 \|
	\| 0.5582 \| 2093000 \| 0.0881 \|
	\| 0.5584 \| 2094000 \| 0.0891 \|
	\| 0.5587 \| 2095000 \| 0.0872 \|
	\| 0.5590 \| 2096000 \| 0.09 \|
	\| 0.5592 \| 2097000 \| 0.0835 \|
	\| 0.5595 \| 2098000 \| 0.0911 \|
	\| 0.5598 \| 2099000 \| 0.0909 \|
	\| 0.5600 \| 2100000 \| 0.087 \|
	\| 0.5603 \| 2101000 \| 0.099 \|
	\| 0.5606 \| 2102000 \| 0.0855 \|
	\| 0.5608 \| 2103000 \| 0.0883 \|
	\| 0.5611 \| 2104000 \| 0.0919 \|
	\| 0.5614 \| 2105000 \| 0.0906 \|
	\| 0.5616 \| 2106000 \| 0.0925 \|
	\| 0.5619 \| 2107000 \| 0.0874 \|
	\| 0.5622 \| 2108000 \| 0.0901 \|
	\| 0.5624 \| 2109000 \| 0.0839 \|
	\| 0.5627 \| 2110000 \| 0.0882 \|
	\| 0.5630 \| 2111000 \| 0.0851 \|
	\| 0.5632 \| 2112000 \| 0.0902 \|
	\| 0.5635 \| 2113000 \| 0.0874 \|
	\| 0.5638 \| 2114000 \| 0.0875 \|
	\| 0.5640 \| 2115000 \| 0.0866 \|
	\| 0.5643 \| 2116000 \| 0.0909 \|
	\| 0.5646 \| 2117000 \| 0.0905 \|
	\| 0.5648 \| 2118000 \| 0.0915 \|
	\| 0.5651 \| 2119000 \| 0.0871 \|
	\| 0.5654 \| 2120000 \| 0.0823 \|
	\| 0.5656 \| 2121000 \| 0.0923 \|
	\| 0.5659 \| 2122000 \| 0.0886 \|
	\| 0.5662 \| 2123000 \| 0.0824 \|
	\| 0.5664 \| 2124000 \| 0.0871 \|
	\| 0.5667 \| 2125000 \| 0.0808 \|
	\| 0.5670 \| 2126000 \| 0.0897 \|
	\| 0.5672 \| 2127000 \| 0.0862 \|
	\| 0.5675 \| 2128000 \| 0.0896 \|
	\| 0.5678 \| 2129000 \| 0.09 \|
	\| 0.5680 \| 2130000 \| 0.092 \|
	\| 0.5683 \| 2131000 \| 0.0875 \|
	\| 0.5686 \| 2132000 \| 0.0844 \|
	\| 0.5688 \| 2133000 \| 0.0838 \|
	\| 0.5691 \| 2134000 \| 0.0871 \|
	\| 0.5694 \| 2135000 \| 0.0812 \|
	\| 0.5696 \| 2136000 \| 0.0892 \|
	\| 0.5699 \| 2137000 \| 0.0819 \|
	\| 0.5702 \| 2138000 \| 0.0862 \|
	\| 0.5704 \| 2139000 \| 0.0895 \|
	\| 0.5707 \| 2140000 \| 0.0881 \|
	\| 0.5710 \| 2141000 \| 0.0854 \|
	\| 0.5712 \| 2142000 \| 0.0852 \|
	\| 0.5715 \| 2143000 \| 0.0825 \|
	\| 0.5718 \| 2144000 \| 0.0893 \|
	\| 0.5720 \| 2145000 \| 0.0884 \|
	\| 0.5723 \| 2146000 \| 0.0841 \|
	\| 0.5726 \| 2147000 \| 0.0897 \|
	\| 0.5728 \| 2148000 \| 0.0869 \|
	\| 0.5731 \| 2149000 \| 0.0831 \|
	\| 0.5734 \| 2150000 \| 0.0852 \|
	\| 0.5736 \| 2151000 \| 0.0858 \|
	\| 0.5739 \| 2152000 \| 0.0878 \|
	\| 0.5742 \| 2153000 \| 0.0879 \|
	\| 0.5744 \| 2154000 \| 0.08 \|
	\| 0.5747 \| 2155000 \| 0.0893 \|
	\| 0.5750 \| 2156000 \| 0.0868 \|
	\| 0.5752 \| 2157000 \| 0.0835 \|
	\| 0.5755 \| 2158000 \| 0.0832 \|
	\| 0.5758 \| 2159000 \| 0.0896 \|
	\| 0.5760 \| 2160000 \| 0.0856 \|
	\| 0.5763 \| 2161000 \| 0.0857 \|
	\| 0.5766 \| 2162000 \| 0.093 \|
	\| 0.5768 \| 2163000 \| 0.0933 \|
	\| 0.5771 \| 2164000 \| 0.0863 \|
	\| 0.5774 \| 2165000 \| 0.0857 \|
	\| 0.5776 \| 2166000 \| 0.0894 \|
	\| 0.5779 \| 2167000 \| 0.0836 \|
	\| 0.5782 \| 2168000 \| 0.0893 \|
	\| 0.5784 \| 2169000 \| 0.0803 \|
	\| 0.5787 \| 2170000 \| 0.081 \|
	\| 0.5790 \| 2171000 \| 0.089 \|
	\| 0.5792 \| 2172000 \| 0.0829 \|
	\| 0.5795 \| 2173000 \| 0.0884 \|
	\| 0.5798 \| 2174000 \| 0.0852 \|
	\| 0.5800 \| 2175000 \| 0.0798 \|
	\| 0.5803 \| 2176000 \| 0.0752 \|
	\| 0.5806 \| 2177000 \| 0.0828 \|
	\| 0.5808 \| 2178000 \| 0.0848 \|
	\| 0.5811 \| 2179000 \| 0.0895 \|
	\| 0.5814 \| 2180000 \| 0.0846 \|
	\| 0.5816 \| 2181000 \| 0.0841 \|
	\| 0.5819 \| 2182000 \| 0.0868 \|
	\| 0.5822 \| 2183000 \| 0.0885 \|
	\| 0.5824 \| 2184000 \| 0.0874 \|
	\| 0.5827 \| 2185000 \| 0.0865 \|
	\| 0.5830 \| 2186000 \| 0.0838 \|
	\| 0.5832 \| 2187000 \| 0.081 \|
	\| 0.5835 \| 2188000 \| 0.0829 \|
	\| 0.5838 \| 2189000 \| 0.0801 \|
	\| 0.5840 \| 2190000 \| 0.0861 \|
	\| 0.5843 \| 2191000 \| 0.08 \|
	\| 0.5846 \| 2192000 \| 0.0842 \|
	\| 0.5848 \| 2193000 \| 0.0831 \|
	\| 0.5851 \| 2194000 \| 0.0842 \|
	\| 0.5854 \| 2195000 \| 0.0836 \|
	\| 0.5856 \| 2196000 \| 0.0811 \|
	\| 0.5859 \| 2197000 \| 0.0851 \|
	\| 0.5862 \| 2198000 \| 0.0854 \|
	\| 0.5864 \| 2199000 \| 0.0857 \|
	\| 0.5867 \| 2200000 \| 0.089 \|
	\| 0.5870 \| 2201000 \| 0.0794 \|
	\| 0.5872 \| 2202000 \| 0.0908 \|
	\| 0.5875 \| 2203000 \| 0.0852 \|
	\| 0.5878 \| 2204000 \| 0.0866 \|
	\| 0.5880 \| 2205000 \| 0.085 \|
	\| 0.5883 \| 2206000 \| 0.0895 \|
	\| 0.5886 \| 2207000 \| 0.089 \|
	\| 0.5888 \| 2208000 \| 0.087 \|
	\| 0.5891 \| 2209000 \| 0.0822 \|
	\| 0.5894 \| 2210000 \| 0.09 \|
	\| 0.5896 \| 2211000 \| 0.0858 \|
	\| 0.5899 \| 2212000 \| 0.0836 \|
	\| 0.5902 \| 2213000 \| 0.0837 \|
	\| 0.5904 \| 2214000 \| 0.0881 \|
	\| 0.5907 \| 2215000 \| 0.0789 \|
	\| 0.5910 \| 2216000 \| 0.0796 \|
	\| 0.5912 \| 2217000 \| 0.0834 \|
	\| 0.5915 \| 2218000 \| 0.0839 \|
	\| 0.5918 \| 2219000 \| 0.0787 \|
	\| 0.5920 \| 2220000 \| 0.0825 \|
	\| 0.5923 \| 2221000 \| 0.0863 \|
	\| 0.5926 \| 2222000 \| 0.0862 \|
	\| 0.5928 \| 2223000 \| 0.0837 \|
	\| 0.5931 \| 2224000 \| 0.0781 \|
	\| 0.5934 \| 2225000 \| 0.0867 \|
	\| 0.5936 \| 2226000 \| 0.0897 \|
	\| 0.5939 \| 2227000 \| 0.0825 \|
	\| 0.5942 \| 2228000 \| 0.0798 \|
	\| 0.5944 \| 2229000 \| 0.086 \|
	\| 0.5947 \| 2230000 \| 0.0807 \|
	\| 0.5950 \| 2231000 \| 0.0788 \|
	\| 0.5952 \| 2232000 \| 0.0851 \|
	\| 0.5955 \| 2233000 \| 0.0844 \|
	\| 0.5958 \| 2234000 \| 0.0779 \|
	\| 0.5960 \| 2235000 \| 0.0804 \|
	\| 0.5963 \| 2236000 \| 0.0799 \|
	\| 0.5966 \| 2237000 \| 0.0843 \|
	\| 0.5968 \| 2238000 \| 0.0794 \|
	\| 0.5971 \| 2239000 \| 0.0848 \|
	\| 0.5974 \| 2240000 \| 0.0854 \|
	\| 0.5976 \| 2241000 \| 0.0906 \|
	\| 0.5979 \| 2242000 \| 0.0855 \|
	\| 0.5982 \| 2243000 \| 0.0793 \|
	\| 0.5984 \| 2244000 \| 0.0845 \|
	\| 0.5987 \| 2245000 \| 0.0854 \|
	\| 0.5990 \| 2246000 \| 0.0868 \|
	\| 0.5992 \| 2247000 \| 0.0867 \|
	\| 0.5995 \| 2248000 \| 0.0869 \|
	\| 0.5998 \| 2249000 \| 0.0853 \|
	\| 0.6000 \| 2250000 \| 0.0844 \|
	\| 0.6003 \| 2251000 \| 0.089 \|
	\| 0.6006 \| 2252000 \| 0.0789 \|
	\| 0.6008 \| 2253000 \| 0.0808 \|
	\| 0.6011 \| 2254000 \| 0.0854 \|
	\| 0.6014 \| 2255000 \| 0.0856 \|
	\| 0.6016 \| 2256000 \| 0.0874 \|
	\| 0.6019 \| 2257000 \| 0.0893 \|
	\| 0.6022 \| 2258000 \| 0.0772 \|
	\| 0.6024 \| 2259000 \| 0.0804 \|
	\| 0.6027 \| 2260000 \| 0.0903 \|
	\| 0.6030 \| 2261000 \| 0.0883 \|
	\| 0.6032 \| 2262000 \| 0.0841 \|
	\| 0.6035 \| 2263000 \| 0.0862 \|
	\| 0.6038 \| 2264000 \| 0.0806 \|
	\| 0.6040 \| 2265000 \| 0.0839 \|
	\| 0.6043 \| 2266000 \| 0.0816 \|
	\| 0.6046 \| 2267000 \| 0.0851 \|
	\| 0.6048 \| 2268000 \| 0.0786 \|
	\| 0.6051 \| 2269000 \| 0.0815 \|
	\| 0.6054 \| 2270000 \| 0.0875 \|
	\| 0.6056 \| 2271000 \| 0.0813 \|
	\| 0.6059 \| 2272000 \| 0.085 \|
	\| 0.6062 \| 2273000 \| 0.0818 \|
	\| 0.6064 \| 2274000 \| 0.0833 \|
	\| 0.6067 \| 2275000 \| 0.0891 \|
	\| 0.6070 \| 2276000 \| 0.0869 \|
	\| 0.6072 \| 2277000 \| 0.0818 \|
	\| 0.6075 \| 2278000 \| 0.0874 \|
	\| 0.6078 \| 2279000 \| 0.0787 \|
	\| 0.6080 \| 2280000 \| 0.0782 \|
	\| 0.6083 \| 2281000 \| 0.0809 \|
	\| 0.6086 \| 2282000 \| 0.083 \|
	\| 0.6088 \| 2283000 \| 0.082 \|
	\| 0.6091 \| 2284000 \| 0.0872 \|
	\| 0.6094 \| 2285000 \| 0.0851 \|
	\| 0.6096 \| 2286000 \| 0.087 \|
	\| 0.6099 \| 2287000 \| 0.0848 \|
	\| 0.6102 \| 2288000 \| 0.0821 \|
	\| 0.6104 \| 2289000 \| 0.085 \|
	\| 0.6107 \| 2290000 \| 0.0838 \|
	\| 0.6110 \| 2291000 \| 0.081 \|
	\| 0.6112 \| 2292000 \| 0.0809 \|
	\| 0.6115 \| 2293000 \| 0.0781 \|
	\| 0.6118 \| 2294000 \| 0.0796 \|
	\| 0.6120 \| 2295000 \| 0.0828 \|
	\| 0.6123 \| 2296000 \| 0.0833 \|
	\| 0.6126 \| 2297000 \| 0.0859 \|
	\| 0.6128 \| 2298000 \| 0.0824 \|
	\| 0.6131 \| 2299000 \| 0.0825 \|
	\| 0.6134 \| 2300000 \| 0.0909 \|
	\| 0.6136 \| 2301000 \| 0.0856 \|
	\| 0.6139 \| 2302000 \| 0.0827 \|
	\| 0.6142 \| 2303000 \| 0.0842 \|
	\| 0.6144 \| 2304000 \| 0.0798 \|
	\| 0.6147 \| 2305000 \| 0.0797 \|
	\| 0.6150 \| 2306000 \| 0.0812 \|
	\| 0.6152 \| 2307000 \| 0.0812 \|
	\| 0.6155 \| 2308000 \| 0.0897 \|
	\| 0.6158 \| 2309000 \| 0.0833 \|
	\| 0.6160 \| 2310000 \| 0.0835 \|
	\| 0.6163 \| 2311000 \| 0.0848 \|
	\| 0.6166 \| 2312000 \| 0.0858 \|
	\| 0.6168 \| 2313000 \| 0.0738 \|
	\| 0.6171 \| 2314000 \| 0.08 \|
	\| 0.6174 \| 2315000 \| 0.0784 \|
	\| 0.6176 \| 2316000 \| 0.0797 \|
	\| 0.6179 \| 2317000 \| 0.0791 \|
	\| 0.6182 \| 2318000 \| 0.0873 \|
	\| 0.6184 \| 2319000 \| 0.0825 \|
	\| 0.6187 \| 2320000 \| 0.0883 \|
	\| 0.6190 \| 2321000 \| 0.084 \|
	\| 0.6192 \| 2322000 \| 0.0801 \|
	\| 0.6195 \| 2323000 \| 0.0856 \|
	\| 0.6198 \| 2324000 \| 0.0764 \|
	\| 0.6200 \| 2325000 \| 0.088 \|
	\| 0.6203 \| 2326000 \| 0.0814 \|
	\| 0.6206 \| 2327000 \| 0.0857 \|
	\| 0.6208 \| 2328000 \| 0.0873 \|
	\| 0.6211 \| 2329000 \| 0.0846 \|
	\| 0.6214 \| 2330000 \| 0.0871 \|
	\| 0.6216 \| 2331000 \| 0.0798 \|
	\| 0.6219 \| 2332000 \| 0.0908 \|
	\| 0.6222 \| 2333000 \| 0.0799 \|
	\| 0.6224 \| 2334000 \| 0.0801 \|
	\| 0.6227 \| 2335000 \| 0.0813 \|
	\| 0.6230 \| 2336000 \| 0.0868 \|
	\| 0.6232 \| 2337000 \| 0.0794 \|
	\| 0.6235 \| 2338000 \| 0.0869 \|
	\| 0.6238 \| 2339000 \| 0.0799 \|
	\| 0.6240 \| 2340000 \| 0.0793 \|
	\| 0.6243 \| 2341000 \| 0.0801 \|
	\| 0.6246 \| 2342000 \| 0.0836 \|
	\| 0.6248 \| 2343000 \| 0.0836 \|
	\| 0.6251 \| 2344000 \| 0.0855 \|
	\| 0.6254 \| 2345000 \| 0.0792 \|
	\| 0.6256 \| 2346000 \| 0.0805 \|
	\| 0.6259 \| 2347000 \| 0.0807 \|
	\| 0.6262 \| 2348000 \| 0.0815 \|
	\| 0.6264 \| 2349000 \| 0.0864 \|
	\| 0.6267 \| 2350000 \| 0.0745 \|
	\| 0.6270 \| 2351000 \| 0.0813 \|
	\| 0.6272 \| 2352000 \| 0.0882 \|
	\| 0.6275 \| 2353000 \| 0.0789 \|
	\| 0.6278 \| 2354000 \| 0.0756 \|
	\| 0.6280 \| 2355000 \| 0.0863 \|
	\| 0.6283 \| 2356000 \| 0.0833 \|
	\| 0.6286 \| 2357000 \| 0.0739 \|
	\| 0.6288 \| 2358000 \| 0.081 \|
	\| 0.6291 \| 2359000 \| 0.0776 \|
	\| 0.6294 \| 2360000 \| 0.0805 \|
	\| 0.6296 \| 2361000 \| 0.0806 \|
	\| 0.6299 \| 2362000 \| 0.0882 \|
	\| 0.6302 \| 2363000 \| 0.0823 \|
	\| 0.6304 \| 2364000 \| 0.09 \|
	\| 0.6307 \| 2365000 \| 0.0763 \|
	\| 0.6310 \| 2366000 \| 0.0796 \|
	\| 0.6312 \| 2367000 \| 0.0835 \|
	\| 0.6315 \| 2368000 \| 0.0803 \|
	\| 0.6318 \| 2369000 \| 0.084 \|
	\| 0.6320 \| 2370000 \| 0.084 \|
	\| 0.6323 \| 2371000 \| 0.076 \|
	\| 0.6326 \| 2372000 \| 0.0749 \|
	\| 0.6328 \| 2373000 \| 0.0795 \|
	\| 0.6331 \| 2374000 \| 0.0813 \|
	\| 0.6334 \| 2375000 \| 0.0825 \|
	\| 0.6336 \| 2376000 \| 0.0829 \|
	\| 0.6339 \| 2377000 \| 0.0818 \|
	\| 0.6342 \| 2378000 \| 0.0797 \|
	\| 0.6344 \| 2379000 \| 0.0846 \|
	\| 0.6347 \| 2380000 \| 0.0832 \|
	\| 0.6350 \| 2381000 \| 0.082 \|
	\| 0.6352 \| 2382000 \| 0.0842 \|
	\| 0.6355 \| 2383000 \| 0.0849 \|
	\| 0.6358 \| 2384000 \| 0.08 \|
	\| 0.6360 \| 2385000 \| 0.0805 \|
	\| 0.6363 \| 2386000 \| 0.0787 \|
	\| 0.6366 \| 2387000 \| 0.088 \|
	\| 0.6368 \| 2388000 \| 0.0883 \|
	\| 0.6371 \| 2389000 \| 0.0807 \|
	\| 0.6374 \| 2390000 \| 0.0786 \|
	\| 0.6376 \| 2391000 \| 0.0836 \|
	\| 0.6379 \| 2392000 \| 0.0795 \|
	\| 0.6382 \| 2393000 \| 0.0801 \|
	\| 0.6384 \| 2394000 \| 0.085 \|
	\| 0.6387 \| 2395000 \| 0.0815 \|
	\| 0.6390 \| 2396000 \| 0.0845 \|
	\| 0.6392 \| 2397000 \| 0.0798 \|
	\| 0.6395 \| 2398000 \| 0.0836 \|
	\| 0.6398 \| 2399000 \| 0.0803 \|
	\| 0.6400 \| 2400000 \| 0.0817 \|
	\| 0.6403 \| 2401000 \| 0.0894 \|
	\| 0.6406 \| 2402000 \| 0.0809 \|
	\| 0.6408 \| 2403000 \| 0.0761 \|
	\| 0.6411 \| 2404000 \| 0.0809 \|
	\| 0.6414 \| 2405000 \| 0.0777 \|
	\| 0.6416 \| 2406000 \| 0.0794 \|
	\| 0.6419 \| 2407000 \| 0.0787 \|
	\| 0.6422 \| 2408000 \| 0.081 \|
	\| 0.6424 \| 2409000 \| 0.0847 \|
	\| 0.6427 \| 2410000 \| 0.0823 \|
	\| 0.6430 \| 2411000 \| 0.0751 \|
	\| 0.6432 \| 2412000 \| 0.0859 \|
	\| 0.6435 \| 2413000 \| 0.0805 \|
	\| 0.6438 \| 2414000 \| 0.082 \|
	\| 0.6440 \| 2415000 \| 0.0861 \|
	\| 0.6443 \| 2416000 \| 0.0842 \|
	\| 0.6446 \| 2417000 \| 0.0876 \|
	\| 0.6448 \| 2418000 \| 0.074 \|
	\| 0.6451 \| 2419000 \| 0.0818 \|
	\| 0.6454 \| 2420000 \| 0.0836 \|
	\| 0.6456 \| 2421000 \| 0.082 \|
	\| 0.6459 \| 2422000 \| 0.0749 \|
	\| 0.6462 \| 2423000 \| 0.0865 \|
	\| 0.6464 \| 2424000 \| 0.0809 \|
	\| 0.6467 \| 2425000 \| 0.0854 \|
	\| 0.6470 \| 2426000 \| 0.0829 \|
	\| 0.6472 \| 2427000 \| 0.08 \|
	\| 0.6475 \| 2428000 \| 0.0873 \|
	\| 0.6478 \| 2429000 \| 0.0757 \|
	\| 0.6480 \| 2430000 \| 0.0788 \|
	\| 0.6483 \| 2431000 \| 0.082 \|
	\| 0.6486 \| 2432000 \| 0.0834 \|
	\| 0.6488 \| 2433000 \| 0.0795 \|
	\| 0.6491 \| 2434000 \| 0.0859 \|
	\| 0.6494 \| 2435000 \| 0.0839 \|
	\| 0.6496 \| 2436000 \| 0.0874 \|
	\| 0.6499 \| 2437000 \| 0.0812 \|
	\| 0.6502 \| 2438000 \| 0.0824 \|
	\| 0.6504 \| 2439000 \| 0.0794 \|
	\| 0.6507 \| 2440000 \| 0.0795 \|
	\| 0.6510 \| 2441000 \| 0.0826 \|
	\| 0.6512 \| 2442000 \| 0.0813 \|
	\| 0.6515 \| 2443000 \| 0.0788 \|
	\| 0.6518 \| 2444000 \| 0.0848 \|
	\| 0.6520 \| 2445000 \| 0.0826 \|
	\| 0.6523 \| 2446000 \| 0.0762 \|
	\| 0.6526 \| 2447000 \| 0.0802 \|
	\| 0.6528 \| 2448000 \| 0.0871 \|
	\| 0.6531 \| 2449000 \| 0.0803 \|
	\| 0.6534 \| 2450000 \| 0.0797 \|
	\| 0.6536 \| 2451000 \| 0.0842 \|
	\| 0.6539 \| 2452000 \| 0.0819 \|
	\| 0.6542 \| 2453000 \| 0.0848 \|
	\| 0.6544 \| 2454000 \| 0.08 \|
	\| 0.6547 \| 2455000 \| 0.0815 \|
	\| 0.6550 \| 2456000 \| 0.0806 \|
	\| 0.6552 \| 2457000 \| 0.0811 \|
	\| 0.6555 \| 2458000 \| 0.0798 \|
	\| 0.6558 \| 2459000 \| 0.0789 \|
	\| 0.6560 \| 2460000 \| 0.0793 \|
	\| 0.6563 \| 2461000 \| 0.0821 \|
	\| 0.6566 \| 2462000 \| 0.0835 \|
	\| 0.6568 \| 2463000 \| 0.0833 \|
	\| 0.6571 \| 2464000 \| 0.0821 \|
	\| 0.6574 \| 2465000 \| 0.088 \|
	\| 0.6576 \| 2466000 \| 0.0822 \|
	\| 0.6579 \| 2467000 \| 0.0749 \|
	\| 0.6582 \| 2468000 \| 0.0787 \|
	\| 0.6584 \| 2469000 \| 0.0793 \|
	\| 0.6587 \| 2470000 \| 0.0793 \|
	\| 0.6590 \| 2471000 \| 0.0807 \|
	\| 0.6592 \| 2472000 \| 0.0767 \|
	\| 0.6595 \| 2473000 \| 0.0823 \|
	\| 0.6598 \| 2474000 \| 0.0867 \|
	\| 0.6600 \| 2475000 \| 0.0834 \|
	\| 0.6603 \| 2476000 \| 0.0821 \|
	\| 0.6606 \| 2477000 \| 0.0787 \|
	\| 0.6608 \| 2478000 \| 0.077 \|
	\| 0.6611 \| 2479000 \| 0.0771 \|
	\| 0.6614 \| 2480000 \| 0.0822 \|
	\| 0.6616 \| 2481000 \| 0.0824 \|
	\| 0.6619 \| 2482000 \| 0.0786 \|
	\| 0.6622 \| 2483000 \| 0.0795 \|
	\| 0.6624 \| 2484000 \| 0.0718 \|
	\| 0.6627 \| 2485000 \| 0.0807 \|
	\| 0.6630 \| 2486000 \| 0.0791 \|
	\| 0.6632 \| 2487000 \| 0.0801 \|
	\| 0.6635 \| 2488000 \| 0.0843 \|
	\| 0.6638 \| 2489000 \| 0.0843 \|
	\| 0.6640 \| 2490000 \| 0.0771 \|
	\| 0.6643 \| 2491000 \| 0.083 \|
	\| 0.6646 \| 2492000 \| 0.0824 \|
	\| 0.6648 \| 2493000 \| 0.0841 \|
	\| 0.6651 \| 2494000 \| 0.0823 \|
	\| 0.6654 \| 2495000 \| 0.0795 \|
	\| 0.6656 \| 2496000 \| 0.0825 \|
	\| 0.6659 \| 2497000 \| 0.0803 \|
	\| 0.6662 \| 2498000 \| 0.0843 \|
	\| 0.6664 \| 2499000 \| 0.0787 \|
	\| 0.6667 \| 2500000 \| 0.0817 \|
	\| 0.6670 \| 2501000 \| 0.0816 \|
	\| 0.6672 \| 2502000 \| 0.0793 \|
	\| 0.6675 \| 2503000 \| 0.0823 \|
	\| 0.6678 \| 2504000 \| 0.0764 \|
	\| 0.6680 \| 2505000 \| 0.0782 \|
	\| 0.6683 \| 2506000 \| 0.0807 \|
	\| 0.6686 \| 2507000 \| 0.0824 \|
	\| 0.6688 \| 2508000 \| 0.0768 \|
	\| 0.6691 \| 2509000 \| 0.0859 \|
	\| 0.6694 \| 2510000 \| 0.0791 \|
	\| 0.6696 \| 2511000 \| 0.0789 \|
	\| 0.6699 \| 2512000 \| 0.0848 \|
	\| 0.6702 \| 2513000 \| 0.0749 \|
	\| 0.6704 \| 2514000 \| 0.0776 \|
	\| 0.6707 \| 2515000 \| 0.0735 \|
	\| 0.6710 \| 2516000 \| 0.0778 \|
	\| 0.6712 \| 2517000 \| 0.0801 \|
	\| 0.6715 \| 2518000 \| 0.0798 \|
	\| 0.6718 \| 2519000 \| 0.0784 \|
	\| 0.6720 \| 2520000 \| 0.0781 \|
	\| 0.6723 \| 2521000 \| 0.0818 \|
	\| 0.6726 \| 2522000 \| 0.0762 \|
	\| 0.6728 \| 2523000 \| 0.0806 \|
	\| 0.6731 \| 2524000 \| 0.0773 \|
	\| 0.6734 \| 2525000 \| 0.0772 \|
	\| 0.6736 \| 2526000 \| 0.0782 \|
	\| 0.6739 \| 2527000 \| 0.0767 \|
	\| 0.6742 \| 2528000 \| 0.0828 \|
	\| 0.6744 \| 2529000 \| 0.0829 \|
	\| 0.6747 \| 2530000 \| 0.0792 \|
	\| 0.6750 \| 2531000 \| 0.0797 \|
	\| 0.6752 \| 2532000 \| 0.0823 \|
	\| 0.6755 \| 2533000 \| 0.0772 \|
	\| 0.6758 \| 2534000 \| 0.0765 \|
	\| 0.6760 \| 2535000 \| 0.075 \|
	\| 0.6763 \| 2536000 \| 0.0786 \|
	\| 0.6766 \| 2537000 \| 0.0785 \|
	\| 0.6768 \| 2538000 \| 0.0877 \|
	\| 0.6771 \| 2539000 \| 0.0747 \|
	\| 0.6774 \| 2540000 \| 0.0755 \|
	\| 0.6776 \| 2541000 \| 0.082 \|
	\| 0.6779 \| 2542000 \| 0.0759 \|
	\| 0.6782 \| 2543000 \| 0.0831 \|
	\| 0.6784 \| 2544000 \| 0.0811 \|
	\| 0.6787 \| 2545000 \| 0.0795 \|
	\| 0.6790 \| 2546000 \| 0.0852 \|
	\| 0.6792 \| 2547000 \| 0.0832 \|
	\| 0.6795 \| 2548000 \| 0.0793 \|
	\| 0.6798 \| 2549000 \| 0.0832 \|
	\| 0.6800 \| 2550000 \| 0.0799 \|
	\| 0.6803 \| 2551000 \| 0.0733 \|
	\| 0.6806 \| 2552000 \| 0.0809 \|
	\| 0.6808 \| 2553000 \| 0.0772 \|
	\| 0.6811 \| 2554000 \| 0.0801 \|
	\| 0.6814 \| 2555000 \| 0.0794 \|
	\| 0.6816 \| 2556000 \| 0.0792 \|
	\| 0.6819 \| 2557000 \| 0.0847 \|
	\| 0.6822 \| 2558000 \| 0.0748 \|
	\| 0.6824 \| 2559000 \| 0.0813 \|
	\| 0.6827 \| 2560000 \| 0.0741 \|
	\| 0.6830 \| 2561000 \| 0.0851 \|
	\| 0.6832 \| 2562000 \| 0.0763 \|
	\| 0.6835 \| 2563000 \| 0.0841 \|
	\| 0.6838 \| 2564000 \| 0.0762 \|
	\| 0.6840 \| 2565000 \| 0.0752 \|
	\| 0.6843 \| 2566000 \| 0.0857 \|
	\| 0.6846 \| 2567000 \| 0.0824 \|
	\| 0.6848 \| 2568000 \| 0.0762 \|
	\| 0.6851 \| 2569000 \| 0.0754 \|
	\| 0.6854 \| 2570000 \| 0.0795 \|
	\| 0.6856 \| 2571000 \| 0.0829 \|
	\| 0.6859 \| 2572000 \| 0.0839 \|
	\| 0.6862 \| 2573000 \| 0.0779 \|
	\| 0.6864 \| 2574000 \| 0.08 \|
	\| 0.6867 \| 2575000 \| 0.0722 \|
	\| 0.6870 \| 2576000 \| 0.0796 \|
	\| 0.6872 \| 2577000 \| 0.0831 \|
	\| 0.6875 \| 2578000 \| 0.0795 \|
	\| 0.6878 \| 2579000 \| 0.0827 \|
	\| 0.6880 \| 2580000 \| 0.0821 \|
	\| 0.6883 \| 2581000 \| 0.074 \|
	\| 0.6886 \| 2582000 \| 0.0811 \|
	\| 0.6888 \| 2583000 \| 0.0758 \|
	\| 0.6891 \| 2584000 \| 0.0742 \|
	\| 0.6894 \| 2585000 \| 0.0744 \|
	\| 0.6896 \| 2586000 \| 0.081 \|
	\| 0.6899 \| 2587000 \| 0.0738 \|
	\| 0.6902 \| 2588000 \| 0.0844 \|
	\| 0.6904 \| 2589000 \| 0.0773 \|
	\| 0.6907 \| 2590000 \| 0.0756 \|
	\| 0.6910 \| 2591000 \| 0.0805 \|
	\| 0.6912 \| 2592000 \| 0.0812 \|
	\| 0.6915 \| 2593000 \| 0.0757 \|
	\| 0.6918 \| 2594000 \| 0.0802 \|
	\| 0.6920 \| 2595000 \| 0.0813 \|
	\| 0.6923 \| 2596000 \| 0.0769 \|
	\| 0.6926 \| 2597000 \| 0.0752 \|
	\| 0.6928 \| 2598000 \| 0.0843 \|
	\| 0.6931 \| 2599000 \| 0.0755 \|
	\| 0.6934 \| 2600000 \| 0.0837 \|
	\| 0.6936 \| 2601000 \| 0.0823 \|
	\| 0.6939 \| 2602000 \| 0.0728 \|
	\| 0.6942 \| 2603000 \| 0.0811 \|
	\| 0.6944 \| 2604000 \| 0.0802 \|
	\| 0.6947 \| 2605000 \| 0.0758 \|
	\| 0.6950 \| 2606000 \| 0.0797 \|
	\| 0.6952 \| 2607000 \| 0.0841 \|
	\| 0.6955 \| 2608000 \| 0.0788 \|
	\| 0.6958 \| 2609000 \| 0.0811 \|
	\| 0.6960 \| 2610000 \| 0.0788 \|
	\| 0.6963 \| 2611000 \| 0.0786 \|
	\| 0.6966 \| 2612000 \| 0.0722 \|
	\| 0.6968 \| 2613000 \| 0.0853 \|
	\| 0.6971 \| 2614000 \| 0.0755 \|
	\| 0.6974 \| 2615000 \| 0.0818 \|
	\| 0.6976 \| 2616000 \| 0.0792 \|
	\| 0.6979 \| 2617000 \| 0.0854 \|
	\| 0.6982 \| 2618000 \| 0.0735 \|
	\| 0.6984 \| 2619000 \| 0.0786 \|
	\| 0.6987 \| 2620000 \| 0.0805 \|
	\| 0.6990 \| 2621000 \| 0.0756 \|
	\| 0.6992 \| 2622000 \| 0.0792 \|
	\| 0.6995 \| 2623000 \| 0.0761 \|
	\| 0.6998 \| 2624000 \| 0.0762 \|
	\| 0.7000 \| 2625000 \| 0.0778 \|
	\| 0.7003 \| 2626000 \| 0.0826 \|
	\| 0.7006 \| 2627000 \| 0.0789 \|
	\| 0.7008 \| 2628000 \| 0.0786 \|
	\| 0.7011 \| 2629000 \| 0.0792 \|
	\| 0.7014 \| 2630000 \| 0.0816 \|
	\| 0.7016 \| 2631000 \| 0.0751 \|
	\| 0.7019 \| 2632000 \| 0.0729 \|
	\| 0.7022 \| 2633000 \| 0.0776 \|
	\| 0.7024 \| 2634000 \| 0.0823 \|
	\| 0.7027 \| 2635000 \| 0.0808 \|
	\| 0.7030 \| 2636000 \| 0.079 \|
	\| 0.7032 \| 2637000 \| 0.0792 \|
	\| 0.7035 \| 2638000 \| 0.0761 \|
	\| 0.7038 \| 2639000 \| 0.0795 \|
	\| 0.7040 \| 2640000 \| 0.0806 \|
	\| 0.7043 \| 2641000 \| 0.0793 \|
	\| 0.7046 \| 2642000 \| 0.086 \|
	\| 0.7048 \| 2643000 \| 0.0765 \|
	\| 0.7051 \| 2644000 \| 0.0745 \|
	\| 0.7054 \| 2645000 \| 0.0771 \|
	\| 0.7056 \| 2646000 \| 0.0808 \|
	\| 0.7059 \| 2647000 \| 0.0805 \|
	\| 0.7062 \| 2648000 \| 0.0759 \|
	\| 0.7064 \| 2649000 \| 0.0709 \|
	\| 0.7067 \| 2650000 \| 0.0787 \|
	\| 0.7070 \| 2651000 \| 0.08 \|
	\| 0.7072 \| 2652000 \| 0.0826 \|
	\| 0.7075 \| 2653000 \| 0.085 \|
	\| 0.7078 \| 2654000 \| 0.08 \|
	\| 0.7080 \| 2655000 \| 0.0762 \|
	\| 0.7083 \| 2656000 \| 0.0769 \|
	\| 0.7086 \| 2657000 \| 0.0783 \|
	\| 0.7088 \| 2658000 \| 0.0837 \|
	\| 0.7091 \| 2659000 \| 0.0803 \|
	\| 0.7094 \| 2660000 \| 0.0809 \|
	\| 0.7096 \| 2661000 \| 0.0764 \|
	\| 0.7099 \| 2662000 \| 0.0791 \|
	\| 0.7102 \| 2663000 \| 0.0829 \|
	\| 0.7104 \| 2664000 \| 0.0767 \|
	\| 0.7107 \| 2665000 \| 0.0799 \|
	\| 0.7110 \| 2666000 \| 0.0789 \|
	\| 0.7112 \| 2667000 \| 0.0781 \|
	\| 0.7115 \| 2668000 \| 0.0813 \|
	\| 0.7118 \| 2669000 \| 0.0793 \|
	\| 0.7120 \| 2670000 \| 0.0793 \|
	\| 0.7123 \| 2671000 \| 0.0815 \|
	\| 0.7126 \| 2672000 \| 0.0816 \|
	\| 0.7128 \| 2673000 \| 0.0774 \|
	\| 0.7131 \| 2674000 \| 0.0785 \|
	\| 0.7134 \| 2675000 \| 0.0711 \|
	\| 0.7136 \| 2676000 \| 0.0799 \|
	\| 0.7139 \| 2677000 \| 0.0758 \|
	\| 0.7142 \| 2678000 \| 0.08 \|
	\| 0.7144 \| 2679000 \| 0.081 \|
	\| 0.7147 \| 2680000 \| 0.0797 \|
	\| 0.7150 \| 2681000 \| 0.0798 \|
	\| 0.7152 \| 2682000 \| 0.0775 \|
	\| 0.7155 \| 2683000 \| 0.0766 \|
	\| 0.7158 \| 2684000 \| 0.0803 \|
	\| 0.7160 \| 2685000 \| 0.0743 \|
	\| 0.7163 \| 2686000 \| 0.0764 \|
	\| 0.7166 \| 2687000 \| 0.0773 \|
	\| 0.7168 \| 2688000 \| 0.0773 \|
	\| 0.7171 \| 2689000 \| 0.0769 \|
	\| 0.7174 \| 2690000 \| 0.0753 \|
	\| 0.7176 \| 2691000 \| 0.072 \|
	\| 0.7179 \| 2692000 \| 0.0779 \|
	\| 0.7182 \| 2693000 \| 0.0778 \|
	\| 0.7184 \| 2694000 \| 0.0743 \|
	\| 0.7187 \| 2695000 \| 0.0764 \|
	\| 0.7190 \| 2696000 \| 0.0762 \|
	\| 0.7192 \| 2697000 \| 0.0791 \|
	\| 0.7195 \| 2698000 \| 0.0804 \|
	\| 0.7198 \| 2699000 \| 0.0769 \|
	\| 0.7200 \| 2700000 \| 0.0787 \|
	\| 0.7203 \| 2701000 \| 0.0804 \|
	\| 0.7206 \| 2702000 \| 0.0746 \|
	\| 0.7208 \| 2703000 \| 0.0813 \|
	\| 0.7211 \| 2704000 \| 0.0783 \|
	\| 0.7214 \| 2705000 \| 0.0783 \|
	\| 0.7216 \| 2706000 \| 0.0748 \|
	\| 0.7219 \| 2707000 \| 0.0813 \|
	\| 0.7222 \| 2708000 \| 0.0885 \|
	\| 0.7224 \| 2709000 \| 0.0749 \|
	\| 0.7227 \| 2710000 \| 0.0812 \|
	\| 0.7230 \| 2711000 \| 0.0749 \|
	\| 0.7232 \| 2712000 \| 0.0787 \|
	\| 0.7235 \| 2713000 \| 0.0823 \|
	\| 0.7238 \| 2714000 \| 0.0754 \|
	\| 0.7240 \| 2715000 \| 0.0773 \|
	\| 0.7243 \| 2716000 \| 0.0774 \|
	\| 0.7246 \| 2717000 \| 0.0785 \|
	\| 0.7248 \| 2718000 \| 0.0813 \|
	\| 0.7251 \| 2719000 \| 0.0855 \|
	\| 0.7254 \| 2720000 \| 0.0812 \|
	\| 0.7256 \| 2721000 \| 0.0751 \|
	\| 0.7259 \| 2722000 \| 0.0778 \|
	\| 0.7262 \| 2723000 \| 0.0756 \|
	\| 0.7264 \| 2724000 \| 0.0808 \|
	\| 0.7267 \| 2725000 \| 0.0768 \|
	\| 0.7270 \| 2726000 \| 0.0775 \|
	\| 0.7272 \| 2727000 \| 0.0789 \|
	\| 0.7275 \| 2728000 \| 0.077 \|
	\| 0.7278 \| 2729000 \| 0.0795 \|
	\| 0.7280 \| 2730000 \| 0.0805 \|
	\| 0.7283 \| 2731000 \| 0.069 \|
	\| 0.7286 \| 2732000 \| 0.0807 \|
	\| 0.7288 \| 2733000 \| 0.0806 \|
	\| 0.7291 \| 2734000 \| 0.0805 \|
	\| 0.7294 \| 2735000 \| 0.0746 \|
	\| 0.7296 \| 2736000 \| 0.0823 \|
	\| 0.7299 \| 2737000 \| 0.0752 \|
	\| 0.7302 \| 2738000 \| 0.0761 \|
	\| 0.7304 \| 2739000 \| 0.079 \|
	\| 0.7307 \| 2740000 \| 0.0772 \|
	\| 0.7310 \| 2741000 \| 0.0781 \|
	\| 0.7312 \| 2742000 \| 0.0774 \|
	\| 0.7315 \| 2743000 \| 0.0805 \|
	\| 0.7318 \| 2744000 \| 0.0784 \|
	\| 0.7320 \| 2745000 \| 0.0783 \|
	\| 0.7323 \| 2746000 \| 0.0761 \|
	\| 0.7326 \| 2747000 \| 0.0772 \|
	\| 0.7328 \| 2748000 \| 0.0755 \|
	\| 0.7331 \| 2749000 \| 0.0733 \|
	\| 0.7334 \| 2750000 \| 0.0744 \|
	\| 0.7336 \| 2751000 \| 0.0737 \|
	\| 0.7339 \| 2752000 \| 0.0747 \|
	\| 0.7342 \| 2753000 \| 0.0742 \|
	\| 0.7344 \| 2754000 \| 0.0789 \|
	\| 0.7347 \| 2755000 \| 0.0788 \|
	\| 0.7350 \| 2756000 \| 0.0789 \|
	\| 0.7352 \| 2757000 \| 0.0763 \|
	\| 0.7355 \| 2758000 \| 0.0751 \|
	\| 0.7358 \| 2759000 \| 0.0745 \|
	\| 0.7360 \| 2760000 \| 0.0814 \|
	\| 0.7363 \| 2761000 \| 0.0792 \|
	\| 0.7366 \| 2762000 \| 0.0748 \|
	\| 0.7368 \| 2763000 \| 0.0822 \|
	\| 0.7371 \| 2764000 \| 0.0754 \|
	\| 0.7374 \| 2765000 \| 0.0765 \|
	\| 0.7376 \| 2766000 \| 0.074 \|
	\| 0.7379 \| 2767000 \| 0.0691 \|
	\| 0.7382 \| 2768000 \| 0.0754 \|
	\| 0.7384 \| 2769000 \| 0.0703 \|
	\| 0.7387 \| 2770000 \| 0.0795 \|
	\| 0.7390 \| 2771000 \| 0.0792 \|
	\| 0.7392 \| 2772000 \| 0.0741 \|
	\| 0.7395 \| 2773000 \| 0.0712 \|
	\| 0.7398 \| 2774000 \| 0.0713 \|
	\| 0.7400 \| 2775000 \| 0.071 \|
	\| 0.7403 \| 2776000 \| 0.079 \|
	\| 0.7406 \| 2777000 \| 0.0737 \|
	\| 0.7408 \| 2778000 \| 0.0751 \|
	\| 0.7411 \| 2779000 \| 0.074 \|
	\| 0.7414 \| 2780000 \| 0.0737 \|
	\| 0.7416 \| 2781000 \| 0.0814 \|
	\| 0.7419 \| 2782000 \| 0.0779 \|
	\| 0.7422 \| 2783000 \| 0.0769 \|
	\| 0.7424 \| 2784000 \| 0.0798 \|
	\| 0.7427 \| 2785000 \| 0.077 \|
	\| 0.7430 \| 2786000 \| 0.0713 \|
	\| 0.7432 \| 2787000 \| 0.0719 \|
	\| 0.7435 \| 2788000 \| 0.0776 \|
	\| 0.7438 \| 2789000 \| 0.0818 \|
	\| 0.7440 \| 2790000 \| 0.0763 \|
	\| 0.7443 \| 2791000 \| 0.0759 \|
	\| 0.7446 \| 2792000 \| 0.0753 \|
	\| 0.7448 \| 2793000 \| 0.0736 \|
	\| 0.7451 \| 2794000 \| 0.0801 \|
	\| 0.7454 \| 2795000 \| 0.0722 \|
	\| 0.7456 \| 2796000 \| 0.081 \|
	\| 0.7459 \| 2797000 \| 0.0714 \|
	\| 0.7462 \| 2798000 \| 0.0762 \|
	\| 0.7464 \| 2799000 \| 0.0809 \|
	\| 0.7467 \| 2800000 \| 0.0816 \|
	\| 0.7470 \| 2801000 \| 0.0794 \|
	\| 0.7472 \| 2802000 \| 0.078 \|
	\| 0.7475 \| 2803000 \| 0.0758 \|
	\| 0.7478 \| 2804000 \| 0.0796 \|
	\| 0.7480 \| 2805000 \| 0.0763 \|
	\| 0.7483 \| 2806000 \| 0.0751 \|
	\| 0.7486 \| 2807000 \| 0.0741 \|
	\| 0.7488 \| 2808000 \| 0.0777 \|
	\| 0.7491 \| 2809000 \| 0.0795 \|
	\| 0.7494 \| 2810000 \| 0.0806 \|
	\| 0.7496 \| 2811000 \| 0.0768 \|
	\| 0.7499 \| 2812000 \| 0.0774 \|

	</details>

	### Framework Versions
	- Python: 3.12.2
	- Sentence Transformers: 3.2.1
	- Transformers: 4.46.1
	- PyTorch: 2.5.0
	- Accelerate: 1.0.1
	- Datasets: 3.0.2
	- Tokenizers: 0.20.1

	## 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",
	}
	```

	#### CustomTripletLoss
	```bibtex
	@misc{hermans2017defense,
	title={In Defense of the Triplet Loss for Person Re-Identification},
	author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
	year={2017},
	eprint={1703.07737},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
	}
	```

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