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UMLS-ED-Bioformer-16L-V-1.25-SpecialTokensUntrained

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:187491593
loss:CustomTripletLoss
text-embeddings-inference
Inference Endpoints
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UMLS-ED-Bioformer-16L-V-1.25-SpecialTokensUntrained / README.md
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---
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: Hylocharis xantusii
sentences:
- Xantus's hummingbird
- C5721346
- C1623532
- Iole viridescens viridescens
- source_sentence: HTLV1+2 RNA XXX Ql PCR
sentences:
- HTLV 1+2 RNA:MevcEşik:Zmlı:XXX:Srl:Prob.amf.hdf
- Nota de progreso:Tipo:Punto temporal:{Configuración}:Documento:Pain medicine
- C0368469
- C4070921
- source_sentence: Degeneração Nigroestriatal
sentences:
- C0270733
- >-
hiperinsulinismo debido a deficiencia de 3-hidroxiacil-coenzima A
deshidrogenasa de cadena corta
- Striatonigral atrophy
- C4303473
- source_sentence: Clostridioides difficile As:titer:moment:serum:semikwantitatief
sentences:
- Dehidroepiandrosteron:MevcEşik:Zmlı:İdrar:Srl
- C0485219
- C0364328
- Clostridium difficile Ac:Título:Pt:Soro:Qn
- source_sentence: E Vicotrat
sentences:
- C2742706
- C2350910
- germanium L-cysteine alpha-tocopherol complex
- Eosine I Bluish, Dipotassium Salt
base_model:
- pankajrajdeo/UMLS-ED-Bioformer-16L-V-1.25
---
# 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/937457_bioformer_16L")
# Run inference
sentences = [
'E Vicotrat',
'Eosine I Bluish, Dipotassium Salt',
'C2742706',
]
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
- `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 |
|:------:|:------:|:-------------:|
| 0.0003 | 1000 | 1.0069 |
| 0.0005 | 2000 | 0.9728 |
| 0.0008 | 3000 | 0.9549 |
| 0.0011 | 4000 | 0.9217 |
| 0.0013 | 5000 | 0.9116 |
| 0.0016 | 6000 | 0.8662 |
| 0.0019 | 7000 | 0.8412 |
| 0.0021 | 8000 | 0.7979 |
| 0.0024 | 9000 | 0.7829 |
| 0.0027 | 10000 | 0.7578 |
| 0.0029 | 11000 | 0.7402 |
| 0.0032 | 12000 | 0.7069 |
| 0.0035 | 13000 | 0.6906 |
| 0.0037 | 14000 | 0.6644 |
| 0.0040 | 15000 | 0.6516 |
| 0.0043 | 16000 | 0.6344 |
| 0.0045 | 17000 | 0.6395 |
| 0.0048 | 18000 | 0.6082 |
| 0.0051 | 19000 | 0.5944 |
| 0.0053 | 20000 | 0.5955 |
| 0.0056 | 21000 | 0.576 |
| 0.0059 | 22000 | 0.5723 |
| 0.0061 | 23000 | 0.5475 |
| 0.0064 | 24000 | 0.5452 |
| 0.0067 | 25000 | 0.5485 |
| 0.0069 | 26000 | 0.5143 |
| 0.0072 | 27000 | 0.5062 |
| 0.0075 | 28000 | 0.5118 |
| 0.0077 | 29000 | 0.4992 |
| 0.0080 | 30000 | 0.5031 |
| 0.0083 | 31000 | 0.4762 |
| 0.0085 | 32000 | 0.4773 |
| 0.0088 | 33000 | 0.4742 |
| 0.0091 | 34000 | 0.4692 |
| 0.0093 | 35000 | 0.464 |
| 0.0096 | 36000 | 0.4687 |
| 0.0099 | 37000 | 0.4592 |
| 0.0101 | 38000 | 0.4468 |
| 0.0104 | 39000 | 0.4425 |
| 0.0107 | 40000 | 0.4477 |
| 0.0109 | 41000 | 0.4336 |
| 0.0112 | 42000 | 0.4331 |
| 0.0115 | 43000 | 0.4248 |
| 0.0117 | 44000 | 0.4189 |
| 0.0120 | 45000 | 0.4147 |
| 0.0123 | 46000 | 0.4112 |
| 0.0125 | 47000 | 0.4051 |
| 0.0128 | 48000 | 0.399 |
| 0.0131 | 49000 | 0.3921 |
| 0.0133 | 50000 | 0.3917 |
| 0.0136 | 51000 | 0.4058 |
| 0.0139 | 52000 | 0.3843 |
| 0.0141 | 53000 | 0.3811 |
| 0.0144 | 54000 | 0.3733 |
| 0.0147 | 55000 | 0.3787 |
| 0.0149 | 56000 | 0.3859 |
| 0.0152 | 57000 | 0.3742 |
| 0.0155 | 58000 | 0.3682 |
| 0.0157 | 59000 | 0.3705 |
| 0.0160 | 60000 | 0.3483 |
| 0.0163 | 61000 | 0.3469 |
| 0.0165 | 62000 | 0.3586 |
| 0.0168 | 63000 | 0.3346 |
| 0.0171 | 64000 | 0.3474 |
| 0.0173 | 65000 | 0.3625 |
| 0.0176 | 66000 | 0.3501 |
| 0.0179 | 67000 | 0.3456 |
| 0.0181 | 68000 | 0.3383 |
| 0.0184 | 69000 | 0.3457 |
| 0.0187 | 70000 | 0.3437 |
| 0.0189 | 71000 | 0.3395 |
| 0.0192 | 72000 | 0.3399 |
| 0.0195 | 73000 | 0.324 |
| 0.0197 | 74000 | 0.338 |
| 0.0200 | 75000 | 0.3268 |
| 0.0203 | 76000 | 0.3298 |
| 0.0205 | 77000 | 0.3282 |
| 0.0208 | 78000 | 0.3356 |
| 0.0211 | 79000 | 0.3187 |
| 0.0213 | 80000 | 0.3155 |
| 0.0216 | 81000 | 0.3181 |
| 0.0219 | 82000 | 0.3085 |
| 0.0221 | 83000 | 0.3168 |
| 0.0224 | 84000 | 0.3162 |
| 0.0227 | 85000 | 0.3126 |
| 0.0229 | 86000 | 0.3026 |
| 0.0232 | 87000 | 0.3017 |
| 0.0235 | 88000 | 0.2963 |
| 0.0237 | 89000 | 0.3002 |
| 0.0240 | 90000 | 0.297 |
| 0.0243 | 91000 | 0.2993 |
| 0.0245 | 92000 | 0.306 |
| 0.0248 | 93000 | 0.2964 |
| 0.0251 | 94000 | 0.2992 |
| 0.0253 | 95000 | 0.2921 |
| 0.0256 | 96000 | 0.3103 |
| 0.0259 | 97000 | 0.2897 |
| 0.0261 | 98000 | 0.2843 |
| 0.0264 | 99000 | 0.2914 |
| 0.0267 | 100000 | 0.2952 |
| 0.0269 | 101000 | 0.2922 |
| 0.0272 | 102000 | 0.2807 |
| 0.0275 | 103000 | 0.2797 |
| 0.0277 | 104000 | 0.2849 |
| 0.0280 | 105000 | 0.2959 |
| 0.0283 | 106000 | 0.2823 |
| 0.0285 | 107000 | 0.2637 |
| 0.0288 | 108000 | 0.2804 |
| 0.0291 | 109000 | 0.2761 |
| 0.0293 | 110000 | 0.2821 |
| 0.0296 | 111000 | 0.2876 |
| 0.0299 | 112000 | 0.2699 |
| 0.0301 | 113000 | 0.2758 |
| 0.0304 | 114000 | 0.2802 |
| 0.0307 | 115000 | 0.2689 |
| 0.0309 | 116000 | 0.2871 |
| 0.0312 | 117000 | 0.2603 |
| 0.0315 | 118000 | 0.2728 |
| 0.0317 | 119000 | 0.2769 |
| 0.0320 | 120000 | 0.2527 |
| 0.0323 | 121000 | 0.2677 |
| 0.0325 | 122000 | 0.2748 |
| 0.0328 | 123000 | 0.2648 |
| 0.0331 | 124000 | 0.2645 |
| 0.0333 | 125000 | 0.2637 |
| 0.0336 | 126000 | 0.2613 |
| 0.0339 | 127000 | 0.261 |
| 0.0341 | 128000 | 0.2568 |
| 0.0344 | 129000 | 0.2611 |
| 0.0347 | 130000 | 0.2486 |
| 0.0349 | 131000 | 0.2535 |
| 0.0352 | 132000 | 0.2525 |
| 0.0355 | 133000 | 0.2457 |
| 0.0357 | 134000 | 0.2545 |
| 0.0360 | 135000 | 0.2596 |
| 0.0363 | 136000 | 0.2505 |
| 0.0365 | 137000 | 0.2454 |
| 0.0368 | 138000 | 0.2696 |
| 0.0371 | 139000 | 0.2567 |
| 0.0373 | 140000 | 0.2517 |
| 0.0376 | 141000 | 0.2436 |
| 0.0379 | 142000 | 0.2452 |
| 0.0381 | 143000 | 0.2427 |
| 0.0384 | 144000 | 0.2525 |
| 0.0387 | 145000 | 0.243 |
| 0.0389 | 146000 | 0.2417 |
| 0.0392 | 147000 | 0.2599 |
| 0.0395 | 148000 | 0.246 |
| 0.0397 | 149000 | 0.2379 |
| 0.0400 | 150000 | 0.2449 |
| 0.0403 | 151000 | 0.2333 |
| 0.0405 | 152000 | 0.2399 |
| 0.0408 | 153000 | 0.2409 |
| 0.0411 | 154000 | 0.2407 |
| 0.0413 | 155000 | 0.2369 |
| 0.0416 | 156000 | 0.2361 |
| 0.0419 | 157000 | 0.2331 |
| 0.0421 | 158000 | 0.232 |
| 0.0424 | 159000 | 0.2337 |
| 0.0427 | 160000 | 0.2331 |
| 0.0429 | 161000 | 0.2328 |
| 0.0432 | 162000 | 0.2278 |
| 0.0435 | 163000 | 0.2335 |
| 0.0437 | 164000 | 0.2301 |
| 0.0440 | 165000 | 0.2381 |
| 0.0443 | 166000 | 0.2298 |
| 0.0445 | 167000 | 0.2355 |
| 0.0448 | 168000 | 0.2254 |
| 0.0451 | 169000 | 0.2301 |
| 0.0453 | 170000 | 0.2319 |
| 0.0456 | 171000 | 0.2314 |
| 0.0459 | 172000 | 0.236 |
| 0.0461 | 173000 | 0.2348 |
| 0.0464 | 174000 | 0.231 |
| 0.0467 | 175000 | 0.2291 |
| 0.0469 | 176000 | 0.2246 |
| 0.0472 | 177000 | 0.2259 |
| 0.0475 | 178000 | 0.2254 |
| 0.0477 | 179000 | 0.2223 |
| 0.0480 | 180000 | 0.2285 |
| 0.0483 | 181000 | 0.2306 |
| 0.0485 | 182000 | 0.2233 |
| 0.0488 | 183000 | 0.2117 |
| 0.0491 | 184000 | 0.2219 |
| 0.0493 | 185000 | 0.2226 |
| 0.0496 | 186000 | 0.2161 |
| 0.0499 | 187000 | 0.2195 |
| 0.0501 | 188000 | 0.2208 |
| 0.0504 | 189000 | 0.2198 |
| 0.0507 | 190000 | 0.2236 |
| 0.0509 | 191000 | 0.2178 |
| 0.0512 | 192000 | 0.2087 |
| 0.0515 | 193000 | 0.2222 |
| 0.0517 | 194000 | 0.211 |
| 0.0520 | 195000 | 0.2287 |
| 0.0523 | 196000 | 0.2219 |
| 0.0525 | 197000 | 0.2096 |
| 0.0528 | 198000 | 0.2112 |
| 0.0531 | 199000 | 0.2108 |
| 0.0533 | 200000 | 0.2098 |
| 0.0536 | 201000 | 0.2176 |
| 0.0539 | 202000 | 0.2118 |
| 0.0541 | 203000 | 0.2248 |
| 0.0544 | 204000 | 0.2124 |
| 0.0547 | 205000 | 0.2133 |
| 0.0549 | 206000 | 0.2101 |
| 0.0552 | 207000 | 0.208 |
| 0.0555 | 208000 | 0.2129 |
| 0.0557 | 209000 | 0.208 |
| 0.0560 | 210000 | 0.2093 |
| 0.0563 | 211000 | 0.2123 |
| 0.0565 | 212000 | 0.205 |
| 0.0568 | 213000 | 0.2012 |
| 0.0571 | 214000 | 0.2078 |
| 0.0573 | 215000 | 0.2107 |
| 0.0576 | 216000 | 0.206 |
| 0.0579 | 217000 | 0.2055 |
| 0.0581 | 218000 | 0.2067 |
| 0.0584 | 219000 | 0.2143 |
| 0.0587 | 220000 | 0.204 |
| 0.0589 | 221000 | 0.2071 |
| 0.0592 | 222000 | 0.2026 |
| 0.0595 | 223000 | 0.1994 |
| 0.0597 | 224000 | 0.2045 |
| 0.0600 | 225000 | 0.2155 |
| 0.0603 | 226000 | 0.2075 |
| 0.0605 | 227000 | 0.195 |
| 0.0608 | 228000 | 0.2028 |
| 0.0611 | 229000 | 0.1973 |
| 0.0613 | 230000 | 0.2034 |
| 0.0616 | 231000 | 0.2039 |
| 0.0619 | 232000 | 0.1937 |
| 0.0621 | 233000 | 0.2 |
| 0.0624 | 234000 | 0.1958 |
| 0.0627 | 235000 | 0.1986 |
| 0.0629 | 236000 | 0.1975 |
| 0.0632 | 237000 | 0.2061 |
| 0.0635 | 238000 | 0.2021 |
| 0.0637 | 239000 | 0.1957 |
| 0.0640 | 240000 | 0.1997 |
| 0.0643 | 241000 | 0.1968 |
| 0.0645 | 242000 | 0.1881 |
| 0.0648 | 243000 | 0.2038 |
| 0.0651 | 244000 | 0.1991 |
| 0.0653 | 245000 | 0.1841 |
| 0.0656 | 246000 | 0.1919 |
| 0.0659 | 247000 | 0.187 |
| 0.0661 | 248000 | 0.1889 |
| 0.0664 | 249000 | 0.1987 |
| 0.0667 | 250000 | 0.1992 |
| 0.0669 | 251000 | 0.1913 |
| 0.0672 | 252000 | 0.1995 |
| 0.0675 | 253000 | 0.1875 |
| 0.0677 | 254000 | 0.1923 |
| 0.0680 | 255000 | 0.1773 |
| 0.0683 | 256000 | 0.1869 |
| 0.0685 | 257000 | 0.1975 |
| 0.0688 | 258000 | 0.1865 |
| 0.0691 | 259000 | 0.1889 |
| 0.0693 | 260000 | 0.1896 |
| 0.0696 | 261000 | 0.1829 |
| 0.0699 | 262000 | 0.1843 |
| 0.0701 | 263000 | 0.195 |
| 0.0704 | 264000 | 0.1818 |
| 0.0707 | 265000 | 0.1855 |
| 0.0709 | 266000 | 0.1841 |
| 0.0712 | 267000 | 0.1889 |
| 0.0715 | 268000 | 0.1814 |
| 0.0717 | 269000 | 0.1917 |
| 0.0720 | 270000 | 0.1862 |
| 0.0723 | 271000 | 0.1869 |
| 0.0725 | 272000 | 0.1859 |
| 0.0728 | 273000 | 0.182 |
| 0.0731 | 274000 | 0.1896 |
| 0.0733 | 275000 | 0.1936 |
| 0.0736 | 276000 | 0.1846 |
| 0.0739 | 277000 | 0.18 |
| 0.0741 | 278000 | 0.1812 |
| 0.0744 | 279000 | 0.1859 |
| 0.0747 | 280000 | 0.1785 |
| 0.0749 | 281000 | 0.1806 |
| 0.0752 | 282000 | 0.182 |
| 0.0755 | 283000 | 0.1848 |
| 0.0757 | 284000 | 0.1798 |
| 0.0760 | 285000 | 0.1853 |
| 0.0763 | 286000 | 0.1834 |
| 0.0765 | 287000 | 0.1815 |
| 0.0768 | 288000 | 0.1819 |
| 0.0771 | 289000 | 0.1808 |
| 0.0773 | 290000 | 0.1851 |
| 0.0776 | 291000 | 0.1823 |
| 0.0779 | 292000 | 0.179 |
| 0.0781 | 293000 | 0.1825 |
| 0.0784 | 294000 | 0.1751 |
| 0.0787 | 295000 | 0.1778 |
| 0.0789 | 296000 | 0.1773 |
| 0.0792 | 297000 | 0.1795 |
| 0.0795 | 298000 | 0.1854 |
| 0.0797 | 299000 | 0.1818 |
| 0.0800 | 300000 | 0.1734 |
| 0.0803 | 301000 | 0.1787 |
| 0.0805 | 302000 | 0.1807 |
| 0.0808 | 303000 | 0.1817 |
| 0.0811 | 304000 | 0.1722 |
| 0.0813 | 305000 | 0.1762 |
| 0.0816 | 306000 | 0.1741 |
| 0.0819 | 307000 | 0.1754 |
| 0.0821 | 308000 | 0.1713 |
| 0.0824 | 309000 | 0.1724 |
| 0.0827 | 310000 | 0.1745 |
| 0.0829 | 311000 | 0.1774 |
| 0.0832 | 312000 | 0.1763 |
| 0.0835 | 313000 | 0.1768 |
| 0.0837 | 314000 | 0.1717 |
| 0.0840 | 315000 | 0.1692 |
| 0.0843 | 316000 | 0.1721 |
| 0.0845 | 317000 | 0.1673 |
| 0.0848 | 318000 | 0.1762 |
| 0.0851 | 319000 | 0.1784 |
| 0.0853 | 320000 | 0.1697 |
| 0.0856 | 321000 | 0.172 |
| 0.0859 | 322000 | 0.1658 |
| 0.0861 | 323000 | 0.1761 |
| 0.0864 | 324000 | 0.1729 |
| 0.0867 | 325000 | 0.1672 |
| 0.0869 | 326000 | 0.1671 |
| 0.0872 | 327000 | 0.1685 |
| 0.0875 | 328000 | 0.1729 |
| 0.0877 | 329000 | 0.166 |
| 0.0880 | 330000 | 0.1712 |
| 0.0883 | 331000 | 0.1737 |
| 0.0885 | 332000 | 0.1723 |
| 0.0888 | 333000 | 0.1705 |
| 0.0891 | 334000 | 0.1718 |
| 0.0893 | 335000 | 0.1689 |
| 0.0896 | 336000 | 0.1747 |
| 0.0899 | 337000 | 0.1696 |
| 0.0901 | 338000 | 0.1712 |
| 0.0904 | 339000 | 0.1674 |
| 0.0907 | 340000 | 0.1709 |
| 0.0909 | 341000 | 0.169 |
| 0.0912 | 342000 | 0.1714 |
| 0.0915 | 343000 | 0.1544 |
| 0.0917 | 344000 | 0.1755 |
| 0.0920 | 345000 | 0.1689 |
| 0.0923 | 346000 | 0.1561 |
| 0.0925 | 347000 | 0.1712 |
| 0.0928 | 348000 | 0.1583 |
| 0.0931 | 349000 | 0.159 |
| 0.0933 | 350000 | 0.1715 |
| 0.0936 | 351000 | 0.1608 |
| 0.0939 | 352000 | 0.1703 |
| 0.0941 | 353000 | 0.1682 |
| 0.0944 | 354000 | 0.1622 |
| 0.0947 | 355000 | 0.1663 |
| 0.0949 | 356000 | 0.1632 |
| 0.0952 | 357000 | 0.1663 |
| 0.0955 | 358000 | 0.1643 |
| 0.0957 | 359000 | 0.1674 |
| 0.0960 | 360000 | 0.1634 |
| 0.0963 | 361000 | 0.1616 |
| 0.0965 | 362000 | 0.1691 |
| 0.0968 | 363000 | 0.1594 |
| 0.0971 | 364000 | 0.1589 |
| 0.0973 | 365000 | 0.1568 |
| 0.0976 | 366000 | 0.1586 |
| 0.0979 | 367000 | 0.1555 |
| 0.0981 | 368000 | 0.161 |
| 0.0984 | 369000 | 0.1615 |
| 0.0987 | 370000 | 0.1691 |
| 0.0989 | 371000 | 0.151 |
| 0.0992 | 372000 | 0.1653 |
| 0.0995 | 373000 | 0.1545 |
| 0.0997 | 374000 | 0.1627 |
| 0.1000 | 375000 | 0.1688 |
| 0.1003 | 376000 | 0.1594 |
| 0.1005 | 377000 | 0.1619 |
| 0.1008 | 378000 | 0.1517 |
| 0.1011 | 379000 | 0.1605 |
| 0.1013 | 380000 | 0.1576 |
| 0.1016 | 381000 | 0.1589 |
| 0.1019 | 382000 | 0.1643 |
| 0.1021 | 383000 | 0.164 |
| 0.1024 | 384000 | 0.158 |
| 0.1027 | 385000 | 0.1584 |
| 0.1029 | 386000 | 0.1565 |
| 0.1032 | 387000 | 0.1566 |
| 0.1035 | 388000 | 0.1625 |
| 0.1037 | 389000 | 0.1569 |
| 0.1040 | 390000 | 0.159 |
| 0.1043 | 391000 | 0.1541 |
| 0.1045 | 392000 | 0.159 |
| 0.1048 | 393000 | 0.1536 |
| 0.1051 | 394000 | 0.166 |
| 0.1053 | 395000 | 0.1639 |
| 0.1056 | 396000 | 0.1491 |
| 0.1059 | 397000 | 0.1567 |
| 0.1061 | 398000 | 0.1566 |
| 0.1064 | 399000 | 0.1641 |
| 0.1067 | 400000 | 0.1552 |
| 0.1069 | 401000 | 0.1476 |
| 0.1072 | 402000 | 0.157 |
| 0.1075 | 403000 | 0.1538 |
| 0.1077 | 404000 | 0.152 |
| 0.1080 | 405000 | 0.1525 |
| 0.1083 | 406000 | 0.155 |
| 0.1085 | 407000 | 0.1538 |
| 0.1088 | 408000 | 0.1506 |
| 0.1091 | 409000 | 0.1481 |
| 0.1093 | 410000 | 0.1603 |
| 0.1096 | 411000 | 0.1509 |
| 0.1099 | 412000 | 0.1628 |
| 0.1101 | 413000 | 0.151 |
| 0.1104 | 414000 | 0.1581 |
| 0.1107 | 415000 | 0.1511 |
| 0.1109 | 416000 | 0.1552 |
| 0.1112 | 417000 | 0.1553 |
| 0.1115 | 418000 | 0.1508 |
| 0.1117 | 419000 | 0.1515 |
| 0.1120 | 420000 | 0.1526 |
| 0.1123 | 421000 | 0.15 |
| 0.1125 | 422000 | 0.1497 |
| 0.1128 | 423000 | 0.1526 |
| 0.1131 | 424000 | 0.1547 |
| 0.1133 | 425000 | 0.151 |
| 0.1136 | 426000 | 0.1471 |
| 0.1139 | 427000 | 0.1576 |
| 0.1141 | 428000 | 0.1522 |
| 0.1144 | 429000 | 0.1506 |
| 0.1147 | 430000 | 0.1495 |
| 0.1149 | 431000 | 0.1518 |
| 0.1152 | 432000 | 0.1467 |
| 0.1155 | 433000 | 0.1511 |
| 0.1157 | 434000 | 0.1516 |
| 0.1160 | 435000 | 0.1476 |
| 0.1163 | 436000 | 0.1526 |
| 0.1165 | 437000 | 0.1474 |
| 0.1168 | 438000 | 0.1445 |
| 0.1171 | 439000 | 0.1408 |
| 0.1173 | 440000 | 0.1412 |
| 0.1176 | 441000 | 0.1445 |
| 0.1179 | 442000 | 0.145 |
| 0.1181 | 443000 | 0.1402 |
| 0.1184 | 444000 | 0.154 |
| 0.1187 | 445000 | 0.1446 |
| 0.1189 | 446000 | 0.1476 |
| 0.1192 | 447000 | 0.1565 |
| 0.1195 | 448000 | 0.1409 |
| 0.1197 | 449000 | 0.1511 |
| 0.1200 | 450000 | 0.139 |
| 0.1203 | 451000 | 0.1463 |
| 0.1205 | 452000 | 0.1453 |
| 0.1208 | 453000 | 0.1432 |
| 0.1211 | 454000 | 0.1559 |
| 0.1213 | 455000 | 0.1354 |
| 0.1216 | 456000 | 0.1419 |
| 0.1219 | 457000 | 0.1452 |
| 0.1221 | 458000 | 0.147 |
| 0.1224 | 459000 | 0.1453 |
| 0.1227 | 460000 | 0.153 |
| 0.1229 | 461000 | 0.1496 |
| 0.1232 | 462000 | 0.1464 |
| 0.1235 | 463000 | 0.1423 |
| 0.1237 | 464000 | 0.1403 |
| 0.1240 | 465000 | 0.1458 |
| 0.1243 | 466000 | 0.1508 |
| 0.1245 | 467000 | 0.1442 |
| 0.1248 | 468000 | 0.1521 |
| 0.1251 | 469000 | 0.1424 |
| 0.1253 | 470000 | 0.1545 |
| 0.1256 | 471000 | 0.1389 |
| 0.1259 | 472000 | 0.1408 |
| 0.1261 | 473000 | 0.1398 |
| 0.1264 | 474000 | 0.1333 |
| 0.1267 | 475000 | 0.1436 |
| 0.1269 | 476000 | 0.1423 |
| 0.1272 | 477000 | 0.1393 |
| 0.1275 | 478000 | 0.1465 |
| 0.1277 | 479000 | 0.1484 |
| 0.1280 | 480000 | 0.1412 |
| 0.1283 | 481000 | 0.143 |
| 0.1285 | 482000 | 0.139 |
| 0.1288 | 483000 | 0.1447 |
| 0.1291 | 484000 | 0.1388 |
| 0.1293 | 485000 | 0.1414 |
| 0.1296 | 486000 | 0.1444 |
| 0.1299 | 487000 | 0.1365 |
| 0.1301 | 488000 | 0.1403 |
| 0.1304 | 489000 | 0.1398 |
| 0.1307 | 490000 | 0.1302 |
| 0.1309 | 491000 | 0.1443 |
| 0.1312 | 492000 | 0.1402 |
| 0.1315 | 493000 | 0.1451 |
| 0.1317 | 494000 | 0.1397 |
| 0.1320 | 495000 | 0.137 |
| 0.1323 | 496000 | 0.1493 |
| 0.1325 | 497000 | 0.1415 |
| 0.1328 | 498000 | 0.1365 |
| 0.1331 | 499000 | 0.1323 |
| 0.1333 | 500000 | 0.1384 |
| 0.1336 | 501000 | 0.1307 |
| 0.1339 | 502000 | 0.1385 |
| 0.1341 | 503000 | 0.1394 |
| 0.1344 | 504000 | 0.1393 |
| 0.1347 | 505000 | 0.1455 |
| 0.1349 | 506000 | 0.1374 |
| 0.1352 | 507000 | 0.1381 |
| 0.1355 | 508000 | 0.1363 |
| 0.1357 | 509000 | 0.1392 |
| 0.1360 | 510000 | 0.1399 |
| 0.1363 | 511000 | 0.1356 |
| 0.1365 | 512000 | 0.1395 |
| 0.1368 | 513000 | 0.1402 |
| 0.1371 | 514000 | 0.1382 |
| 0.1373 | 515000 | 0.1408 |
| 0.1376 | 516000 | 0.1398 |
| 0.1379 | 517000 | 0.1405 |
| 0.1381 | 518000 | 0.1351 |
| 0.1384 | 519000 | 0.1371 |
| 0.1387 | 520000 | 0.1302 |
| 0.1389 | 521000 | 0.14 |
| 0.1392 | 522000 | 0.1363 |
| 0.1395 | 523000 | 0.1313 |
| 0.1397 | 524000 | 0.1299 |
| 0.1400 | 525000 | 0.1372 |
| 0.1403 | 526000 | 0.1416 |
| 0.1405 | 527000 | 0.1295 |
| 0.1408 | 528000 | 0.1359 |
| 0.1411 | 529000 | 0.1383 |
| 0.1413 | 530000 | 0.1378 |
| 0.1416 | 531000 | 0.135 |
| 0.1419 | 532000 | 0.1405 |
| 0.1421 | 533000 | 0.14 |
| 0.1424 | 534000 | 0.1321 |
| 0.1427 | 535000 | 0.1303 |
| 0.1429 | 536000 | 0.1319 |
| 0.1432 | 537000 | 0.1312 |
| 0.1435 | 538000 | 0.1338 |
| 0.1437 | 539000 | 0.1361 |
| 0.1440 | 540000 | 0.139 |
| 0.1443 | 541000 | 0.1364 |
| 0.1445 | 542000 | 0.1316 |
| 0.1448 | 543000 | 0.1331 |
| 0.1451 | 544000 | 0.1269 |
| 0.1453 | 545000 | 0.1294 |
| 0.1456 | 546000 | 0.135 |
| 0.1459 | 547000 | 0.1328 |
| 0.1461 | 548000 | 0.1296 |
| 0.1464 | 549000 | 0.1305 |
| 0.1467 | 550000 | 0.1334 |
| 0.1469 | 551000 | 0.1362 |
| 0.1472 | 552000 | 0.1318 |
| 0.1475 | 553000 | 0.1312 |
| 0.1477 | 554000 | 0.1293 |
| 0.1480 | 555000 | 0.1324 |
| 0.1483 | 556000 | 0.1256 |
| 0.1485 | 557000 | 0.1227 |
| 0.1488 | 558000 | 0.1239 |
| 0.1491 | 559000 | 0.1287 |
| 0.1493 | 560000 | 0.1307 |
| 0.1496 | 561000 | 0.1336 |
| 0.1499 | 562000 | 0.133 |
| 0.1501 | 563000 | 0.1278 |
| 0.1504 | 564000 | 0.1339 |
| 0.1507 | 565000 | 0.1321 |
| 0.1509 | 566000 | 0.1322 |
| 0.1512 | 567000 | 0.1262 |
| 0.1515 | 568000 | 0.1331 |
| 0.1517 | 569000 | 0.1361 |
| 0.1520 | 570000 | 0.1307 |
| 0.1523 | 571000 | 0.133 |
| 0.1525 | 572000 | 0.1293 |
| 0.1528 | 573000 | 0.1283 |
| 0.1531 | 574000 | 0.1275 |
| 0.1533 | 575000 | 0.1329 |
| 0.1536 | 576000 | 0.1307 |
| 0.1539 | 577000 | 0.1245 |
| 0.1541 | 578000 | 0.1313 |
| 0.1544 | 579000 | 0.1256 |
| 0.1547 | 580000 | 0.1257 |
| 0.1549 | 581000 | 0.1194 |
| 0.1552 | 582000 | 0.125 |
| 0.1555 | 583000 | 0.1345 |
| 0.1557 | 584000 | 0.1308 |
| 0.1560 | 585000 | 0.1318 |
| 0.1563 | 586000 | 0.1348 |
| 0.1565 | 587000 | 0.1231 |
| 0.1568 | 588000 | 0.1282 |
| 0.1571 | 589000 | 0.1281 |
| 0.1573 | 590000 | 0.1221 |
| 0.1576 | 591000 | 0.1234 |
| 0.1579 | 592000 | 0.1334 |
| 0.1581 | 593000 | 0.1249 |
| 0.1584 | 594000 | 0.1216 |
| 0.1587 | 595000 | 0.1295 |
| 0.1589 | 596000 | 0.1191 |
| 0.1592 | 597000 | 0.1267 |
| 0.1595 | 598000 | 0.1273 |
| 0.1597 | 599000 | 0.124 |
| 0.1600 | 600000 | 0.1271 |
| 0.1603 | 601000 | 0.1284 |
| 0.1605 | 602000 | 0.1285 |
| 0.1608 | 603000 | 0.1288 |
| 0.1611 | 604000 | 0.1252 |
| 0.1613 | 605000 | 0.1255 |
| 0.1616 | 606000 | 0.1289 |
| 0.1619 | 607000 | 0.1294 |
| 0.1621 | 608000 | 0.1294 |
| 0.1624 | 609000 | 0.1288 |
| 0.1627 | 610000 | 0.1336 |
| 0.1629 | 611000 | 0.125 |
| 0.1632 | 612000 | 0.1288 |
| 0.1635 | 613000 | 0.122 |
| 0.1637 | 614000 | 0.1204 |
| 0.1640 | 615000 | 0.1245 |
| 0.1643 | 616000 | 0.1303 |
| 0.1645 | 617000 | 0.1187 |
| 0.1648 | 618000 | 0.1223 |
| 0.1651 | 619000 | 0.1311 |
| 0.1653 | 620000 | 0.1202 |
| 0.1656 | 621000 | 0.1271 |
| 0.1659 | 622000 | 0.1218 |
| 0.1661 | 623000 | 0.1218 |
| 0.1664 | 624000 | 0.1247 |
| 0.1667 | 625000 | 0.1289 |
| 0.1669 | 626000 | 0.1261 |
| 0.1672 | 627000 | 0.1262 |
| 0.1675 | 628000 | 0.1251 |
| 0.1677 | 629000 | 0.1271 |
| 0.1680 | 630000 | 0.1243 |
| 0.1683 | 631000 | 0.1266 |
| 0.1685 | 632000 | 0.1257 |
| 0.1688 | 633000 | 0.1215 |
| 0.1691 | 634000 | 0.1236 |
| 0.1693 | 635000 | 0.1267 |
| 0.1696 | 636000 | 0.1209 |
| 0.1699 | 637000 | 0.1188 |
| 0.1701 | 638000 | 0.1267 |
| 0.1704 | 639000 | 0.1259 |
| 0.1707 | 640000 | 0.1225 |
| 0.1709 | 641000 | 0.1183 |
| 0.1712 | 642000 | 0.1202 |
| 0.1715 | 643000 | 0.1279 |
| 0.1717 | 644000 | 0.1191 |
| 0.1720 | 645000 | 0.1206 |
| 0.1723 | 646000 | 0.1178 |
| 0.1725 | 647000 | 0.1234 |
| 0.1728 | 648000 | 0.1259 |
| 0.1731 | 649000 | 0.1227 |
| 0.1733 | 650000 | 0.1211 |
| 0.1736 | 651000 | 0.1216 |
| 0.1739 | 652000 | 0.1182 |
| 0.1741 | 653000 | 0.1205 |
| 0.1744 | 654000 | 0.1187 |
| 0.1747 | 655000 | 0.1144 |
| 0.1749 | 656000 | 0.1216 |
| 0.1752 | 657000 | 0.1287 |
| 0.1755 | 658000 | 0.122 |
| 0.1757 | 659000 | 0.1213 |
| 0.1760 | 660000 | 0.1217 |
| 0.1763 | 661000 | 0.1256 |
| 0.1765 | 662000 | 0.1227 |
| 0.1768 | 663000 | 0.1219 |
| 0.1771 | 664000 | 0.1261 |
| 0.1773 | 665000 | 0.1169 |
| 0.1776 | 666000 | 0.1192 |
| 0.1779 | 667000 | 0.1187 |
| 0.1781 | 668000 | 0.1117 |
| 0.1784 | 669000 | 0.1189 |
| 0.1787 | 670000 | 0.12 |
| 0.1789 | 671000 | 0.1204 |
| 0.1792 | 672000 | 0.1208 |
| 0.1795 | 673000 | 0.119 |
| 0.1797 | 674000 | 0.1161 |
| 0.1800 | 675000 | 0.1167 |
| 0.1803 | 676000 | 0.1235 |
| 0.1805 | 677000 | 0.1276 |
| 0.1808 | 678000 | 0.1188 |
| 0.1811 | 679000 | 0.1135 |
| 0.1813 | 680000 | 0.1187 |
| 0.1816 | 681000 | 0.1165 |
| 0.1819 | 682000 | 0.1224 |
| 0.1821 | 683000 | 0.125 |
| 0.1824 | 684000 | 0.1146 |
| 0.1827 | 685000 | 0.1162 |
| 0.1829 | 686000 | 0.1172 |
| 0.1832 | 687000 | 0.1197 |
| 0.1835 | 688000 | 0.113 |
| 0.1837 | 689000 | 0.1216 |
| 0.1840 | 690000 | 0.1144 |
| 0.1843 | 691000 | 0.1274 |
| 0.1845 | 692000 | 0.1136 |
| 0.1848 | 693000 | 0.1202 |
| 0.1851 | 694000 | 0.1249 |
| 0.1853 | 695000 | 0.1195 |
| 0.1856 | 696000 | 0.1158 |
| 0.1859 | 697000 | 0.1145 |
| 0.1861 | 698000 | 0.1187 |
| 0.1864 | 699000 | 0.1173 |
| 0.1867 | 700000 | 0.1181 |
| 0.1869 | 701000 | 0.1236 |
| 0.1872 | 702000 | 0.1223 |
| 0.1875 | 703000 | 0.1147 |
| 0.1877 | 704000 | 0.1197 |
| 0.1880 | 705000 | 0.1125 |
| 0.1883 | 706000 | 0.1175 |
| 0.1885 | 707000 | 0.1239 |
| 0.1888 | 708000 | 0.1263 |
| 0.1891 | 709000 | 0.1229 |
| 0.1893 | 710000 | 0.1202 |
| 0.1896 | 711000 | 0.1159 |
| 0.1899 | 712000 | 0.1232 |
| 0.1901 | 713000 | 0.1197 |
| 0.1904 | 714000 | 0.121 |
| 0.1907 | 715000 | 0.1189 |
| 0.1909 | 716000 | 0.1183 |
| 0.1912 | 717000 | 0.1091 |
| 0.1915 | 718000 | 0.1186 |
| 0.1917 | 719000 | 0.115 |
| 0.1920 | 720000 | 0.1146 |
| 0.1923 | 721000 | 0.1165 |
| 0.1925 | 722000 | 0.1192 |
| 0.1928 | 723000 | 0.1163 |
| 0.1931 | 724000 | 0.1162 |
| 0.1933 | 725000 | 0.1156 |
| 0.1936 | 726000 | 0.1218 |
| 0.1939 | 727000 | 0.1154 |
| 0.1941 | 728000 | 0.1131 |
| 0.1944 | 729000 | 0.118 |
| 0.1947 | 730000 | 0.1156 |
| 0.1949 | 731000 | 0.1193 |
| 0.1952 | 732000 | 0.1143 |
| 0.1955 | 733000 | 0.1211 |
| 0.1957 | 734000 | 0.1187 |
| 0.1960 | 735000 | 0.12 |
| 0.1963 | 736000 | 0.1164 |
| 0.1965 | 737000 | 0.1173 |
| 0.1968 | 738000 | 0.1151 |
| 0.1971 | 739000 | 0.1143 |
| 0.1973 | 740000 | 0.1141 |
| 0.1976 | 741000 | 0.1174 |
| 0.1979 | 742000 | 0.1185 |
| 0.1981 | 743000 | 0.1133 |
| 0.1984 | 744000 | 0.1174 |
| 0.1987 | 745000 | 0.1154 |
| 0.1989 | 746000 | 0.1138 |
| 0.1992 | 747000 | 0.1203 |
| 0.1995 | 748000 | 0.1119 |
| 0.1997 | 749000 | 0.111 |
| 0.2000 | 750000 | 0.1174 |
| 0.2003 | 751000 | 0.1204 |
| 0.2005 | 752000 | 0.1177 |
| 0.2008 | 753000 | 0.1139 |
| 0.2011 | 754000 | 0.1138 |
| 0.2013 | 755000 | 0.1179 |
| 0.2016 | 756000 | 0.1094 |
| 0.2019 | 757000 | 0.1092 |
| 0.2021 | 758000 | 0.1108 |
| 0.2024 | 759000 | 0.1125 |
| 0.2027 | 760000 | 0.1202 |
| 0.2029 | 761000 | 0.1119 |
| 0.2032 | 762000 | 0.1151 |
| 0.2035 | 763000 | 0.1169 |
| 0.2037 | 764000 | 0.1109 |
| 0.2040 | 765000 | 0.1112 |
| 0.2043 | 766000 | 0.1102 |
| 0.2045 | 767000 | 0.119 |
| 0.2048 | 768000 | 0.1131 |
| 0.2051 | 769000 | 0.1155 |
| 0.2053 | 770000 | 0.1133 |
| 0.2056 | 771000 | 0.1127 |
| 0.2059 | 772000 | 0.1116 |
| 0.2061 | 773000 | 0.1122 |
| 0.2064 | 774000 | 0.1151 |
| 0.2067 | 775000 | 0.1163 |
| 0.2069 | 776000 | 0.1162 |
| 0.2072 | 777000 | 0.1096 |
| 0.2075 | 778000 | 0.1151 |
| 0.2077 | 779000 | 0.1156 |
| 0.2080 | 780000 | 0.1135 |
| 0.2083 | 781000 | 0.1084 |
| 0.2085 | 782000 | 0.114 |
| 0.2088 | 783000 | 0.1128 |
| 0.2091 | 784000 | 0.1142 |
| 0.2093 | 785000 | 0.1092 |
| 0.2096 | 786000 | 0.1067 |
| 0.2099 | 787000 | 0.1156 |
| 0.2101 | 788000 | 0.1094 |
| 0.2104 | 789000 | 0.1078 |
| 0.2107 | 790000 | 0.1133 |
| 0.2109 | 791000 | 0.1165 |
| 0.2112 | 792000 | 0.1116 |
| 0.2115 | 793000 | 0.1111 |
| 0.2117 | 794000 | 0.1086 |
| 0.2120 | 795000 | 0.1114 |
| 0.2123 | 796000 | 0.1069 |
| 0.2125 | 797000 | 0.1094 |
| 0.2128 | 798000 | 0.1125 |
| 0.2131 | 799000 | 0.112 |
| 0.2133 | 800000 | 0.1107 |
| 0.2136 | 801000 | 0.1085 |
| 0.2139 | 802000 | 0.1067 |
| 0.2141 | 803000 | 0.1149 |
| 0.2144 | 804000 | 0.1068 |
| 0.2147 | 805000 | 0.1124 |
| 0.2149 | 806000 | 0.1109 |
| 0.2152 | 807000 | 0.1094 |
| 0.2155 | 808000 | 0.1097 |
| 0.2157 | 809000 | 0.1106 |
| 0.2160 | 810000 | 0.1152 |
| 0.2163 | 811000 | 0.1123 |
| 0.2165 | 812000 | 0.1102 |
| 0.2168 | 813000 | 0.11 |
| 0.2171 | 814000 | 0.1 |
| 0.2173 | 815000 | 0.1127 |
| 0.2176 | 816000 | 0.1135 |
| 0.2179 | 817000 | 0.1127 |
| 0.2181 | 818000 | 0.108 |
| 0.2184 | 819000 | 0.1119 |
| 0.2187 | 820000 | 0.1103 |
| 0.2189 | 821000 | 0.1084 |
| 0.2192 | 822000 | 0.1076 |
| 0.2195 | 823000 | 0.1145 |
| 0.2197 | 824000 | 0.109 |
| 0.2200 | 825000 | 0.1119 |
| 0.2203 | 826000 | 0.1117 |
| 0.2205 | 827000 | 0.1117 |
| 0.2208 | 828000 | 0.1062 |
| 0.2211 | 829000 | 0.1113 |
| 0.2213 | 830000 | 0.1101 |
| 0.2216 | 831000 | 0.1053 |
| 0.2219 | 832000 | 0.1122 |
| 0.2221 | 833000 | 0.1091 |
| 0.2224 | 834000 | 0.1106 |
| 0.2227 | 835000 | 0.1062 |
| 0.2229 | 836000 | 0.1091 |
| 0.2232 | 837000 | 0.1144 |
| 0.2235 | 838000 | 0.1106 |
| 0.2237 | 839000 | 0.1058 |
| 0.2240 | 840000 | 0.1085 |
| 0.2243 | 841000 | 0.1154 |
| 0.2245 | 842000 | 0.1096 |
| 0.2248 | 843000 | 0.1062 |
| 0.2251 | 844000 | 0.1089 |
| 0.2253 | 845000 | 0.108 |
| 0.2256 | 846000 | 0.1086 |
| 0.2259 | 847000 | 0.1084 |
| 0.2261 | 848000 | 0.1056 |
| 0.2264 | 849000 | 0.1042 |
| 0.2267 | 850000 | 0.1204 |
| 0.2269 | 851000 | 0.1053 |
| 0.2272 | 852000 | 0.1053 |
| 0.2275 | 853000 | 0.1065 |
| 0.2277 | 854000 | 0.1157 |
| 0.2280 | 855000 | 0.1112 |
| 0.2283 | 856000 | 0.1058 |
| 0.2285 | 857000 | 0.1084 |
| 0.2288 | 858000 | 0.1066 |
| 0.2291 | 859000 | 0.1116 |
| 0.2293 | 860000 | 0.1047 |
| 0.2296 | 861000 | 0.1145 |
| 0.2299 | 862000 | 0.1094 |
| 0.2301 | 863000 | 0.1108 |
| 0.2304 | 864000 | 0.1038 |
| 0.2307 | 865000 | 0.1044 |
| 0.2309 | 866000 | 0.106 |
| 0.2312 | 867000 | 0.105 |
| 0.2315 | 868000 | 0.108 |
| 0.2317 | 869000 | 0.1108 |
| 0.2320 | 870000 | 0.113 |
| 0.2323 | 871000 | 0.108 |
| 0.2325 | 872000 | 0.1069 |
| 0.2328 | 873000 | 0.1098 |
| 0.2331 | 874000 | 0.1021 |
| 0.2333 | 875000 | 0.109 |
| 0.2336 | 876000 | 0.1104 |
| 0.2339 | 877000 | 0.1043 |
| 0.2341 | 878000 | 0.1057 |
| 0.2344 | 879000 | 0.105 |
| 0.2347 | 880000 | 0.1042 |
| 0.2349 | 881000 | 0.1116 |
| 0.2352 | 882000 | 0.1151 |
| 0.2355 | 883000 | 0.1043 |
| 0.2357 | 884000 | 0.1023 |
| 0.2360 | 885000 | 0.1084 |
| 0.2363 | 886000 | 0.1103 |
| 0.2365 | 887000 | 0.1028 |
| 0.2368 | 888000 | 0.1055 |
| 0.2371 | 889000 | 0.1023 |
| 0.2373 | 890000 | 0.1099 |
| 0.2376 | 891000 | 0.1037 |
| 0.2379 | 892000 | 0.1068 |
| 0.2381 | 893000 | 0.1128 |
| 0.2384 | 894000 | 0.1023 |
| 0.2387 | 895000 | 0.1023 |
| 0.2389 | 896000 | 0.106 |
| 0.2392 | 897000 | 0.1005 |
| 0.2395 | 898000 | 0.1013 |
| 0.2397 | 899000 | 0.1131 |
| 0.2400 | 900000 | 0.107 |
| 0.2403 | 901000 | 0.1096 |
| 0.2405 | 902000 | 0.0963 |
| 0.2408 | 903000 | 0.1076 |
| 0.2411 | 904000 | 0.102 |
| 0.2413 | 905000 | 0.1147 |
| 0.2416 | 906000 | 0.1111 |
| 0.2419 | 907000 | 0.1035 |
| 0.2421 | 908000 | 0.1059 |
| 0.2424 | 909000 | 0.1037 |
| 0.2427 | 910000 | 0.1047 |
| 0.2429 | 911000 | 0.1049 |
| 0.2432 | 912000 | 0.1097 |
| 0.2435 | 913000 | 0.1062 |
| 0.2437 | 914000 | 0.1016 |
| 0.2440 | 915000 | 0.1061 |
| 0.2443 | 916000 | 0.1089 |
| 0.2445 | 917000 | 0.1032 |
| 0.2448 | 918000 | 0.1053 |
| 0.2451 | 919000 | 0.1075 |
| 0.2453 | 920000 | 0.1048 |
| 0.2456 | 921000 | 0.1007 |
| 0.2459 | 922000 | 0.11 |
| 0.2461 | 923000 | 0.1034 |
| 0.2464 | 924000 | 0.1059 |
| 0.2467 | 925000 | 0.1063 |
| 0.2469 | 926000 | 0.1051 |
| 0.2472 | 927000 | 0.1064 |
| 0.2475 | 928000 | 0.0986 |
| 0.2477 | 929000 | 0.1037 |
| 0.2480 | 930000 | 0.1093 |
| 0.2483 | 931000 | 0.102 |
| 0.2485 | 932000 | 0.0985 |
| 0.2488 | 933000 | 0.1023 |
| 0.2491 | 934000 | 0.104 |
| 0.2493 | 935000 | 0.1108 |
| 0.2496 | 936000 | 0.1061 |
| 0.2499 | 937000 | 0.1053 |
</details>
### Framework Versions
- Python: 3.12.2
- Sentence Transformers: 3.2.1
- Transformers: 4.45.2
- PyTorch: 2.5.0
- Accelerate: 1.0.1
- Datasets: 3.0.1
- 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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