---
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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