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1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- loss:CachedMultipleNegativesRankingLoss
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_ndcg@100
- cosine_mrr@10
- cosine_mrr@100
- cosine_map@100
model-index:
- name: SentenceTransformer
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: validation retrieval
type: validation_retrieval
metrics:
- type: cosine_accuracy@1
value: 0.57
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.78
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.85
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.91
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.57
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.47666666666666674
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.4120000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.31600000000000006
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.09586063814284759
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.1974064320530941
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.2626170559411045
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3592334048227218
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.466909740701314
name: Cosine Ndcg@10
- type: cosine_ndcg@100
value: 0.5134011865277004
name: Cosine Ndcg@100
- type: cosine_mrr@10
value: 0.6836785714285715
name: Cosine Mrr@10
- type: cosine_mrr@100
value: 0.6876848086206663
name: Cosine Mrr@100
- type: cosine_map@100
value: 0.31888680716468715
name: Cosine Map@100
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained on the generator dataset. It maps sentences & paragraphs to a 4096-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:** 32768 tokens
- **Output Dimensionality:** 4096 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- generator
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 32768, 'do_lower_case': False, 'architecture': 'Qwen3Model'})
(1): Pooling({'word_embedding_dimension': 2048, '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': True, '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("reasonwang/embedding-qwen3-1.7b-embedding_unicode_shuf")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 4096]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9014, 0.7471],
# [0.9014, 1.0000, 0.6621],
# [0.7471, 0.6621, 1.0000]])
```
<!--
### 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.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `validation_retrieval`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.57 |
| cosine_accuracy@3 | 0.78 |
| cosine_accuracy@5 | 0.85 |
| cosine_accuracy@10 | 0.91 |
| cosine_precision@1 | 0.57 |
| cosine_precision@3 | 0.4767 |
| cosine_precision@5 | 0.412 |
| cosine_precision@10 | 0.316 |
| cosine_recall@1 | 0.0959 |
| cosine_recall@3 | 0.1974 |
| cosine_recall@5 | 0.2626 |
| cosine_recall@10 | 0.3592 |
| cosine_ndcg@10 | 0.4669 |
| **cosine_ndcg@100** | **0.5134** |
| cosine_mrr@10 | 0.6837 |
| cosine_mrr@100 | 0.6877 |
| cosine_map@100 | 0.3189 |
<!--
## 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
#### generator
* Dataset: generator
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 4,
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 256
- `learning_rate`: 2e-05
- `max_steps`: 100000
- `log_level`: info
- `bf16`: True
- `dataloader_num_workers`: 1
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': False, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 256
- `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`: 3.0
- `max_steps`: 100000
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: info
- `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
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 1
- `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': False, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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`: None
- `hub_always_push`: False
- `hub_revision`: None
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | validation_retrieval_cosine_ndcg@100 |
|:------:|:-----:|:-------------:|:------------------------------------:|
| 1e-05 | 1 | 5.2241 | - |
| 0.0001 | 10 | 3.8307 | - |
| 0.0002 | 20 | 2.8362 | - |
| 0.0003 | 30 | 2.5535 | - |
| 0.0004 | 40 | 2.3874 | - |
| 0.0005 | 50 | 2.3478 | - |
| 0.0006 | 60 | 2.2947 | - |
| 0.0007 | 70 | 2.2224 | - |
| 0.0008 | 80 | 2.1957 | - |
| 0.0009 | 90 | 2.1906 | - |
| 0.001 | 100 | 2.1638 | 0.4390 |
| 0.0011 | 110 | 2.141 | - |
| 0.0012 | 120 | 2.1424 | - |
| 0.0013 | 130 | 2.1075 | - |
| 0.0014 | 140 | 2.1 | - |
| 0.0015 | 150 | 2.087 | - |
| 0.0016 | 160 | 2.0809 | - |
| 0.0017 | 170 | 2.1087 | - |
| 0.0018 | 180 | 2.0668 | - |
| 0.0019 | 190 | 2.057 | - |
| 0.002 | 200 | 2.0647 | 0.4512 |
| 0.0021 | 210 | 2.0259 | - |
| 0.0022 | 220 | 2.0266 | - |
| 0.0023 | 230 | 2.0173 | - |
| 0.0024 | 240 | 2.0347 | - |
| 0.0025 | 250 | 2.0364 | - |
| 0.0026 | 260 | 1.9999 | - |
| 0.0027 | 270 | 2.0162 | - |
| 0.0028 | 280 | 1.9844 | - |
| 0.0029 | 290 | 1.9897 | - |
| 0.003 | 300 | 1.9756 | 0.4665 |
| 0.0031 | 310 | 1.9482 | - |
| 0.0032 | 320 | 1.97 | - |
| 0.0033 | 330 | 1.9571 | - |
| 0.0034 | 340 | 1.9638 | - |
| 0.0035 | 350 | 1.9598 | - |
| 0.0036 | 360 | 1.9482 | - |
| 0.0037 | 370 | 1.9507 | - |
| 0.0038 | 380 | 1.9339 | - |
| 0.0039 | 390 | 1.9431 | - |
| 0.004 | 400 | 1.9562 | 0.4728 |
| 0.0041 | 410 | 1.9217 | - |
| 0.0042 | 420 | 1.9493 | - |
| 0.0043 | 430 | 1.9322 | - |
| 0.0044 | 440 | 1.9046 | - |
| 0.0045 | 450 | 1.9142 | - |
| 0.0046 | 460 | 1.9117 | - |
| 0.0047 | 470 | 1.9063 | - |
| 0.0048 | 480 | 1.8879 | - |
| 0.0049 | 490 | 1.8921 | - |
| 0.005 | 500 | 1.9033 | 0.4813 |
| 0.0051 | 510 | 1.8904 | - |
| 0.0052 | 520 | 1.8941 | - |
| 0.0053 | 530 | 1.8901 | - |
| 0.0054 | 540 | 1.88 | - |
| 0.0055 | 550 | 1.8974 | - |
| 0.0056 | 560 | 1.8875 | - |
| 0.0057 | 570 | 1.8962 | - |
| 0.0058 | 580 | 1.9163 | - |
| 0.0059 | 590 | 1.8463 | - |
| 0.006 | 600 | 1.8859 | 0.4808 |
| 0.0061 | 610 | 1.8772 | - |
| 0.0062 | 620 | 1.8664 | - |
| 0.0063 | 630 | 1.8553 | - |
| 0.0064 | 640 | 1.8674 | - |
| 0.0065 | 650 | 1.8326 | - |
| 0.0066 | 660 | 1.8411 | - |
| 0.0067 | 670 | 1.8764 | - |
| 0.0068 | 680 | 1.8544 | - |
| 0.0069 | 690 | 1.8489 | - |
| 0.007 | 700 | 1.8488 | 0.4785 |
| 0.0071 | 710 | 1.8558 | - |
| 0.0072 | 720 | 1.8389 | - |
| 0.0073 | 730 | 1.8367 | - |
| 0.0074 | 740 | 1.8525 | - |
| 0.0075 | 750 | 1.8368 | - |
| 0.0076 | 760 | 1.8561 | - |
| 0.0077 | 770 | 1.8255 | - |
| 0.0078 | 780 | 1.8169 | - |
| 0.0079 | 790 | 1.8036 | - |
| 0.008 | 800 | 1.8187 | 0.4950 |
| 0.0081 | 810 | 1.835 | - |
| 0.0082 | 820 | 1.8459 | - |
| 0.0083 | 830 | 1.7894 | - |
| 0.0084 | 840 | 1.8323 | - |
| 0.0085 | 850 | 1.8184 | - |
| 0.0086 | 860 | 1.8148 | - |
| 0.0087 | 870 | 1.8335 | - |
| 0.0088 | 880 | 1.8051 | - |
| 0.0089 | 890 | 1.796 | - |
| 0.009 | 900 | 1.8298 | 0.4931 |
| 0.0091 | 910 | 1.8048 | - |
| 0.0092 | 920 | 1.7841 | - |
| 0.0093 | 930 | 1.8022 | - |
| 0.0094 | 940 | 1.7822 | - |
| 0.0095 | 950 | 1.8062 | - |
| 0.0096 | 960 | 1.8197 | - |
| 0.0097 | 970 | 1.7851 | - |
| 0.0098 | 980 | 1.7836 | - |
| 0.0099 | 990 | 1.7629 | - |
| 0.01 | 1000 | 1.8104 | 0.4907 |
| 0.0101 | 1010 | 1.7941 | - |
| 0.0102 | 1020 | 1.8084 | - |
| 0.0103 | 1030 | 1.801 | - |
| 0.0104 | 1040 | 1.782 | - |
| 0.0105 | 1050 | 1.8032 | - |
| 0.0106 | 1060 | 1.7978 | - |
| 0.0107 | 1070 | 1.758 | - |
| 0.0108 | 1080 | 1.7695 | - |
| 0.0109 | 1090 | 1.7961 | - |
| 0.011 | 1100 | 1.7667 | 0.4844 |
| 0.0111 | 1110 | 1.7644 | - |
| 0.0112 | 1120 | 1.7974 | - |
| 0.0113 | 1130 | 1.7672 | - |
| 0.0114 | 1140 | 1.7671 | - |
| 0.0115 | 1150 | 1.7945 | - |
| 0.0116 | 1160 | 1.7863 | - |
| 0.0117 | 1170 | 1.7613 | - |
| 0.0118 | 1180 | 1.7739 | - |
| 0.0119 | 1190 | 1.7759 | - |
| 0.012 | 1200 | 1.7736 | 0.4868 |
| 0.0121 | 1210 | 1.7507 | - |
| 0.0122 | 1220 | 1.7628 | - |
| 0.0123 | 1230 | 1.7723 | - |
| 0.0124 | 1240 | 1.7536 | - |
| 0.0125 | 1250 | 1.727 | - |
| 0.0126 | 1260 | 1.7445 | - |
| 0.0127 | 1270 | 1.7529 | - |
| 0.0128 | 1280 | 1.7623 | - |
| 0.0129 | 1290 | 1.7321 | - |
| 0.013 | 1300 | 1.7748 | 0.4892 |
| 0.0131 | 1310 | 1.7211 | - |
| 0.0132 | 1320 | 1.7745 | - |
| 0.0133 | 1330 | 1.7305 | - |
| 0.0134 | 1340 | 1.767 | - |
| 0.0135 | 1350 | 1.7551 | - |
| 0.0136 | 1360 | 1.7329 | - |
| 0.0137 | 1370 | 1.78 | - |
| 0.0138 | 1380 | 1.7495 | - |
| 0.0139 | 1390 | 1.724 | - |
| 0.014 | 1400 | 1.7436 | 0.4939 |
| 0.0141 | 1410 | 1.7756 | - |
| 0.0142 | 1420 | 1.7748 | - |
| 0.0143 | 1430 | 1.7508 | - |
| 0.0144 | 1440 | 1.7301 | - |
| 0.0145 | 1450 | 1.746 | - |
| 0.0146 | 1460 | 1.7387 | - |
| 0.0147 | 1470 | 1.7368 | - |
| 0.0148 | 1480 | 1.7422 | - |
| 0.0149 | 1490 | 1.7335 | - |
| 0.015 | 1500 | 1.7129 | 0.4923 |
| 0.0151 | 1510 | 1.731 | - |
| 0.0152 | 1520 | 1.7307 | - |
| 0.0153 | 1530 | 1.7322 | - |
| 0.0154 | 1540 | 1.7554 | - |
| 0.0155 | 1550 | 1.7125 | - |
| 0.0156 | 1560 | 1.7327 | - |
| 0.0157 | 1570 | 1.7223 | - |
| 0.0158 | 1580 | 1.7136 | - |
| 0.0159 | 1590 | 1.7267 | - |
| 0.016 | 1600 | 1.7625 | 0.4914 |
| 0.0161 | 1610 | 1.7267 | - |
| 0.0162 | 1620 | 1.7038 | - |
| 0.0163 | 1630 | 1.7063 | - |
| 0.0164 | 1640 | 1.7057 | - |
| 0.0165 | 1650 | 1.7284 | - |
| 0.0166 | 1660 | 1.6998 | - |
| 0.0167 | 1670 | 1.7115 | - |
| 0.0168 | 1680 | 1.7356 | - |
| 0.0169 | 1690 | 1.6929 | - |
| 0.017 | 1700 | 1.7292 | 0.4985 |
| 0.0171 | 1710 | 1.7109 | - |
| 0.0172 | 1720 | 1.7494 | - |
| 0.0173 | 1730 | 1.7181 | - |
| 0.0174 | 1740 | 1.712 | - |
| 0.0175 | 1750 | 1.708 | - |
| 0.0176 | 1760 | 1.7056 | - |
| 0.0177 | 1770 | 1.7039 | - |
| 0.0178 | 1780 | 1.6837 | - |
| 0.0179 | 1790 | 1.7071 | - |
| 0.018 | 1800 | 1.7121 | 0.4972 |
| 0.0181 | 1810 | 1.7147 | - |
| 0.0182 | 1820 | 1.7203 | - |
| 0.0183 | 1830 | 1.7023 | - |
| 0.0184 | 1840 | 1.7278 | - |
| 0.0185 | 1850 | 1.7129 | - |
| 0.0186 | 1860 | 1.7454 | - |
| 0.0187 | 1870 | 1.7011 | - |
| 0.0188 | 1880 | 1.6996 | - |
| 0.0189 | 1890 | 1.7046 | - |
| 0.019 | 1900 | 1.6877 | 0.4984 |
| 0.0191 | 1910 | 1.6962 | - |
| 0.0192 | 1920 | 1.7057 | - |
| 0.0193 | 1930 | 1.6968 | - |
| 0.0194 | 1940 | 1.6994 | - |
| 0.0195 | 1950 | 1.7087 | - |
| 0.0196 | 1960 | 1.6832 | - |
| 0.0197 | 1970 | 1.686 | - |
| 0.0198 | 1980 | 1.7101 | - |
| 0.0199 | 1990 | 1.7024 | - |
| 0.02 | 2000 | 1.6875 | 0.4987 |
| 0.0201 | 2010 | 1.6872 | - |
| 0.0202 | 2020 | 1.6918 | - |
| 0.0203 | 2030 | 1.6735 | - |
| 0.0204 | 2040 | 1.6869 | - |
| 0.0205 | 2050 | 1.7082 | - |
| 0.0206 | 2060 | 1.6991 | - |
| 0.0207 | 2070 | 1.6973 | - |
| 0.0208 | 2080 | 1.6754 | - |
| 0.0209 | 2090 | 1.6953 | - |
| 0.021 | 2100 | 1.7065 | 0.4954 |
| 0.0211 | 2110 | 1.6804 | - |
| 0.0212 | 2120 | 1.6705 | - |
| 0.0213 | 2130 | 1.673 | - |
| 0.0214 | 2140 | 1.6997 | - |
| 0.0215 | 2150 | 1.6774 | - |
| 0.0216 | 2160 | 1.7124 | - |
| 0.0217 | 2170 | 1.6749 | - |
| 0.0218 | 2180 | 1.6661 | - |
| 0.0219 | 2190 | 1.6782 | - |
| 0.022 | 2200 | 1.6742 | 0.4922 |
| 0.0221 | 2210 | 1.7204 | - |
| 0.0222 | 2220 | 1.7081 | - |
| 0.0223 | 2230 | 1.681 | - |
| 0.0224 | 2240 | 1.6775 | - |
| 0.0225 | 2250 | 1.665 | - |
| 0.0226 | 2260 | 1.6992 | - |
| 0.0227 | 2270 | 1.6531 | - |
| 0.0228 | 2280 | 1.6656 | - |
| 0.0229 | 2290 | 1.6717 | - |
| 0.023 | 2300 | 1.6922 | 0.4982 |
| 0.0231 | 2310 | 1.6765 | - |
| 0.0232 | 2320 | 1.6687 | - |
| 0.0233 | 2330 | 1.6682 | - |
| 0.0234 | 2340 | 1.6734 | - |
| 0.0235 | 2350 | 1.6735 | - |
| 0.0236 | 2360 | 1.6935 | - |
| 0.0237 | 2370 | 1.6753 | - |
| 0.0238 | 2380 | 1.6571 | - |
| 0.0239 | 2390 | 1.6834 | - |
| 0.024 | 2400 | 1.6964 | 0.4986 |
| 0.0241 | 2410 | 1.6722 | - |
| 0.0242 | 2420 | 1.655 | - |
| 0.0243 | 2430 | 1.6599 | - |
| 0.0244 | 2440 | 1.6362 | - |
| 0.0245 | 2450 | 1.6715 | - |
| 0.0246 | 2460 | 1.6889 | - |
| 0.0247 | 2470 | 1.6778 | - |
| 0.0248 | 2480 | 1.6746 | - |
| 0.0249 | 2490 | 1.6739 | - |
| 0.025 | 2500 | 1.6478 | 0.4960 |
| 0.0251 | 2510 | 1.6609 | - |
| 0.0252 | 2520 | 1.6575 | - |
| 0.0253 | 2530 | 1.6393 | - |
| 0.0254 | 2540 | 1.6665 | - |
| 0.0255 | 2550 | 1.637 | - |
| 0.0256 | 2560 | 1.6294 | - |
| 0.0257 | 2570 | 1.6586 | - |
| 0.0258 | 2580 | 1.6625 | - |
| 0.0259 | 2590 | 1.6465 | - |
| 0.026 | 2600 | 1.6576 | 0.4993 |
| 0.0261 | 2610 | 1.6683 | - |
| 0.0262 | 2620 | 1.6447 | - |
| 0.0263 | 2630 | 1.6647 | - |
| 0.0264 | 2640 | 1.641 | - |
| 0.0265 | 2650 | 1.6544 | - |
| 0.0266 | 2660 | 1.6366 | - |
| 0.0267 | 2670 | 1.6368 | - |
| 0.0268 | 2680 | 1.668 | - |
| 0.0269 | 2690 | 1.6355 | - |
| 0.027 | 2700 | 1.6621 | 0.5026 |
| 0.0271 | 2710 | 1.6472 | - |
| 0.0272 | 2720 | 1.6579 | - |
| 0.0273 | 2730 | 1.6631 | - |
| 0.0274 | 2740 | 1.6627 | - |
| 0.0275 | 2750 | 1.6485 | - |
| 0.0276 | 2760 | 1.655 | - |
| 0.0277 | 2770 | 1.656 | - |
| 0.0278 | 2780 | 1.6425 | - |
| 0.0279 | 2790 | 1.6207 | - |
| 0.028 | 2800 | 1.6438 | 0.5061 |
| 0.0281 | 2810 | 1.6466 | - |
| 0.0282 | 2820 | 1.625 | - |
| 0.0283 | 2830 | 1.6672 | - |
| 0.0284 | 2840 | 1.6154 | - |
| 0.0285 | 2850 | 1.6581 | - |
| 0.0286 | 2860 | 1.638 | - |
| 0.0287 | 2870 | 1.6252 | - |
| 0.0288 | 2880 | 1.6468 | - |
| 0.0289 | 2890 | 1.638 | - |
| 0.029 | 2900 | 1.67 | 0.4955 |
| 0.0291 | 2910 | 1.6236 | - |
| 0.0292 | 2920 | 1.6583 | - |
| 0.0293 | 2930 | 1.6596 | - |
| 0.0294 | 2940 | 1.6437 | - |
| 0.0295 | 2950 | 1.6362 | - |
| 0.0296 | 2960 | 1.6505 | - |
| 0.0297 | 2970 | 1.6299 | - |
| 0.0298 | 2980 | 1.6276 | - |
| 0.0299 | 2990 | 1.6274 | - |
| 0.03 | 3000 | 1.6666 | 0.5002 |
| 0.0301 | 3010 | 1.6358 | - |
| 0.0302 | 3020 | 1.6166 | - |
| 0.0303 | 3030 | 1.6491 | - |
| 0.0304 | 3040 | 1.6289 | - |
| 0.0305 | 3050 | 1.6544 | - |
| 0.0306 | 3060 | 1.6237 | - |
| 0.0307 | 3070 | 1.6131 | - |
| 0.0308 | 3080 | 1.6332 | - |
| 0.0309 | 3090 | 1.6182 | - |
| 0.031 | 3100 | 1.6344 | 0.5085 |
| 0.0311 | 3110 | 1.6217 | - |
| 0.0312 | 3120 | 1.6532 | - |
| 0.0313 | 3130 | 1.6315 | - |
| 0.0314 | 3140 | 1.6342 | - |
| 0.0315 | 3150 | 1.6281 | - |
| 0.0316 | 3160 | 1.6277 | - |
| 0.0317 | 3170 | 1.6527 | - |
| 0.0318 | 3180 | 1.6129 | - |
| 0.0319 | 3190 | 1.6247 | - |
| 0.032 | 3200 | 1.6439 | 0.5018 |
| 0.0321 | 3210 | 1.6422 | - |
| 0.0322 | 3220 | 1.6442 | - |
| 0.0323 | 3230 | 1.6632 | - |
| 0.0324 | 3240 | 1.6302 | - |
| 0.0325 | 3250 | 1.6162 | - |
| 0.0326 | 3260 | 1.6381 | - |
| 0.0327 | 3270 | 1.6137 | - |
| 0.0328 | 3280 | 1.6122 | - |
| 0.0329 | 3290 | 1.6224 | - |
| 0.033 | 3300 | 1.612 | 0.4993 |
| 0.0331 | 3310 | 1.6095 | - |
| 0.0332 | 3320 | 1.6206 | - |
| 0.0333 | 3330 | 1.6262 | - |
| 0.0334 | 3340 | 1.6052 | - |
| 0.0335 | 3350 | 1.6187 | - |
| 0.0336 | 3360 | 1.6145 | - |
| 0.0337 | 3370 | 1.6275 | - |
| 0.0338 | 3380 | 1.6093 | - |
| 0.0339 | 3390 | 1.6284 | - |
| 0.034 | 3400 | 1.6184 | 0.5079 |
| 0.0341 | 3410 | 1.6359 | - |
| 0.0342 | 3420 | 1.6208 | - |
| 0.0343 | 3430 | 1.6208 | - |
| 0.0344 | 3440 | 1.6341 | - |
| 0.0345 | 3450 | 1.6171 | - |
| 0.0346 | 3460 | 1.6122 | - |
| 0.0347 | 3470 | 1.6302 | - |
| 0.0348 | 3480 | 1.6214 | - |
| 0.0349 | 3490 | 1.6299 | - |
| 0.035 | 3500 | 1.6201 | 0.5005 |
| 0.0351 | 3510 | 1.6033 | - |
| 0.0352 | 3520 | 1.6202 | - |
| 0.0353 | 3530 | 1.6198 | - |
| 0.0354 | 3540 | 1.6207 | - |
| 0.0355 | 3550 | 1.6111 | - |
| 0.0356 | 3560 | 1.6196 | - |
| 0.0357 | 3570 | 1.6341 | - |
| 0.0358 | 3580 | 1.6086 | - |
| 0.0359 | 3590 | 1.6021 | - |
| 0.036 | 3600 | 1.6294 | 0.5161 |
| 0.0361 | 3610 | 1.6117 | - |
| 0.0362 | 3620 | 1.6368 | - |
| 0.0363 | 3630 | 1.6009 | - |
| 0.0364 | 3640 | 1.5983 | - |
| 0.0365 | 3650 | 1.6248 | - |
| 0.0366 | 3660 | 1.609 | - |
| 0.0367 | 3670 | 1.5975 | - |
| 0.0368 | 3680 | 1.6043 | - |
| 0.0369 | 3690 | 1.5989 | - |
| 0.037 | 3700 | 1.6164 | 0.5117 |
| 0.0371 | 3710 | 1.6283 | - |
| 0.0372 | 3720 | 1.5928 | - |
| 0.0373 | 3730 | 1.6104 | - |
| 0.0374 | 3740 | 1.6264 | - |
| 0.0375 | 3750 | 1.5989 | - |
| 0.0376 | 3760 | 1.5975 | - |
| 0.0377 | 3770 | 1.6011 | - |
| 0.0378 | 3780 | 1.6054 | - |
| 0.0379 | 3790 | 1.6129 | - |
| 0.038 | 3800 | 1.616 | 0.5119 |
| 0.0381 | 3810 | 1.618 | - |
| 0.0382 | 3820 | 1.6236 | - |
| 0.0383 | 3830 | 1.6032 | - |
| 0.0384 | 3840 | 1.6236 | - |
| 0.0385 | 3850 | 1.6003 | - |
| 0.0386 | 3860 | 1.6025 | - |
| 0.0387 | 3870 | 1.6034 | - |
| 0.0388 | 3880 | 1.599 | - |
| 0.0389 | 3890 | 1.6065 | - |
| 0.039 | 3900 | 1.6161 | 0.5097 |
| 0.0391 | 3910 | 1.6093 | - |
| 0.0392 | 3920 | 1.5912 | - |
| 0.0393 | 3930 | 1.5893 | - |
| 0.0394 | 3940 | 1.602 | - |
| 0.0395 | 3950 | 1.6023 | - |
| 0.0396 | 3960 | 1.6072 | - |
| 0.0397 | 3970 | 1.599 | - |
| 0.0398 | 3980 | 1.6083 | - |
| 0.0399 | 3990 | 1.5991 | - |
| 0.04 | 4000 | 1.6085 | 0.5089 |
| 0.0401 | 4010 | 1.5917 | - |
| 0.0402 | 4020 | 1.5934 | - |
| 0.0403 | 4030 | 1.5862 | - |
| 0.0404 | 4040 | 1.6041 | - |
| 0.0405 | 4050 | 1.6048 | - |
| 0.0406 | 4060 | 1.6145 | - |
| 0.0407 | 4070 | 1.5817 | - |
| 0.0408 | 4080 | 1.5848 | - |
| 0.0409 | 4090 | 1.6079 | - |
| 0.041 | 4100 | 1.5973 | 0.5101 |
| 0.0411 | 4110 | 1.6045 | - |
| 0.0412 | 4120 | 1.6083 | - |
| 0.0413 | 4130 | 1.5871 | - |
| 0.0414 | 4140 | 1.5891 | - |
| 0.0415 | 4150 | 1.5788 | - |
| 0.0416 | 4160 | 1.5859 | - |
| 0.0417 | 4170 | 1.6094 | - |
| 0.0418 | 4180 | 1.5684 | - |
| 0.0419 | 4190 | 1.5735 | - |
| 0.042 | 4200 | 1.5902 | 0.5126 |
| 0.0421 | 4210 | 1.578 | - |
| 0.0422 | 4220 | 1.5966 | - |
| 0.0423 | 4230 | 1.6026 | - |
| 0.0424 | 4240 | 1.6029 | - |
| 0.0425 | 4250 | 1.6013 | - |
| 0.0426 | 4260 | 1.5849 | - |
| 0.0427 | 4270 | 1.6059 | - |
| 0.0428 | 4280 | 1.6116 | - |
| 0.0429 | 4290 | 1.5928 | - |
| 0.043 | 4300 | 1.5911 | 0.5132 |
| 0.0431 | 4310 | 1.5873 | - |
| 0.0432 | 4320 | 1.611 | - |
| 0.0433 | 4330 | 1.5959 | - |
| 0.0434 | 4340 | 1.61 | - |
| 0.0435 | 4350 | 1.5948 | - |
| 0.0436 | 4360 | 1.5947 | - |
| 0.0437 | 4370 | 1.5969 | - |
| 0.0438 | 4380 | 1.5894 | - |
| 0.0439 | 4390 | 1.5905 | - |
| 0.044 | 4400 | 1.5826 | 0.5057 |
| 0.0441 | 4410 | 1.5852 | - |
| 0.0442 | 4420 | 1.6171 | - |
| 0.0443 | 4430 | 1.5904 | - |
| 0.0444 | 4440 | 1.5873 | - |
| 0.0445 | 4450 | 1.5963 | - |
| 0.0446 | 4460 | 1.6266 | - |
| 0.0447 | 4470 | 1.559 | - |
| 0.0448 | 4480 | 1.6168 | - |
| 0.0449 | 4490 | 1.599 | - |
| 0.045 | 4500 | 1.5974 | 0.5169 |
| 0.0451 | 4510 | 1.5921 | - |
| 0.0452 | 4520 | 1.5812 | - |
| 0.0453 | 4530 | 1.6036 | - |
| 0.0454 | 4540 | 1.5925 | - |
| 0.0455 | 4550 | 1.5967 | - |
| 0.0456 | 4560 | 1.5747 | - |
| 0.0457 | 4570 | 1.6012 | - |
| 0.0458 | 4580 | 1.6041 | - |
| 0.0459 | 4590 | 1.5917 | - |
| 0.046 | 4600 | 1.5615 | 0.5125 |
| 0.0461 | 4610 | 1.5794 | - |
| 0.0462 | 4620 | 1.5635 | - |
| 0.0463 | 4630 | 1.5968 | - |
| 0.0464 | 4640 | 1.6121 | - |
| 0.0465 | 4650 | 1.595 | - |
| 0.0466 | 4660 | 1.5875 | - |
| 0.0467 | 4670 | 1.5933 | - |
| 0.0468 | 4680 | 1.6045 | - |
| 0.0469 | 4690 | 1.5688 | - |
| 0.047 | 4700 | 1.5843 | 0.5133 |
| 0.0471 | 4710 | 1.5679 | - |
| 0.0472 | 4720 | 1.5736 | - |
| 0.0473 | 4730 | 1.5801 | - |
| 0.0474 | 4740 | 1.5753 | - |
| 0.0475 | 4750 | 1.5563 | - |
| 0.0476 | 4760 | 1.5565 | - |
| 0.0477 | 4770 | 1.5823 | - |
| 0.0478 | 4780 | 1.5866 | - |
| 0.0479 | 4790 | 1.5576 | - |
| 0.048 | 4800 | 1.5866 | 0.5147 |
| 0.0481 | 4810 | 1.5836 | - |
| 0.0482 | 4820 | 1.5993 | - |
| 0.0483 | 4830 | 1.5777 | - |
| 0.0484 | 4840 | 1.5642 | - |
| 0.0485 | 4850 | 1.5727 | - |
| 0.0486 | 4860 | 1.5621 | - |
| 0.0487 | 4870 | 1.5808 | - |
| 0.0488 | 4880 | 1.5604 | - |
| 0.0489 | 4890 | 1.5805 | - |
| 0.049 | 4900 | 1.5658 | 0.5227 |
| 0.0491 | 4910 | 1.5873 | - |
| 0.0492 | 4920 | 1.5984 | - |
| 0.0493 | 4930 | 1.5765 | - |
| 0.0494 | 4940 | 1.5659 | - |
| 0.0495 | 4950 | 1.5616 | - |
| 0.0496 | 4960 | 1.5851 | - |
| 0.0497 | 4970 | 1.5687 | - |
| 0.0498 | 4980 | 1.5758 | - |
| 0.0499 | 4990 | 1.588 | - |
| 0.05 | 5000 | 1.5564 | 0.5119 |
| 0.0501 | 5010 | 1.5673 | - |
| 0.0502 | 5020 | 1.5774 | - |
| 0.0503 | 5030 | 1.5763 | - |
| 0.0504 | 5040 | 1.572 | - |
| 0.0505 | 5050 | 1.5716 | - |
| 0.0506 | 5060 | 1.5634 | - |
| 0.0507 | 5070 | 1.5205 | - |
| 0.0508 | 5080 | 1.556 | - |
| 0.0509 | 5090 | 1.5651 | - |
| 0.051 | 5100 | 1.5684 | 0.5085 |
| 0.0511 | 5110 | 1.5629 | - |
| 0.0512 | 5120 | 1.5434 | - |
| 0.0513 | 5130 | 1.5796 | - |
| 0.0514 | 5140 | 1.574 | - |
| 0.0515 | 5150 | 1.5571 | - |
| 0.0516 | 5160 | 1.5433 | - |
| 0.0517 | 5170 | 1.5617 | - |
| 0.0518 | 5180 | 1.6022 | - |
| 0.0519 | 5190 | 1.5453 | - |
| 0.052 | 5200 | 1.5533 | 0.5091 |
| 0.0521 | 5210 | 1.5588 | - |
| 0.0522 | 5220 | 1.5756 | - |
| 0.0523 | 5230 | 1.5698 | - |
| 0.0524 | 5240 | 1.5916 | - |
| 0.0525 | 5250 | 1.5669 | - |
| 0.0526 | 5260 | 1.5595 | - |
| 0.0527 | 5270 | 1.5896 | - |
| 0.0528 | 5280 | 1.564 | - |
| 0.0529 | 5290 | 1.5819 | - |
| 0.053 | 5300 | 1.5674 | 0.5046 |
| 0.0531 | 5310 | 1.5725 | - |
| 0.0532 | 5320 | 1.5897 | - |
| 0.0533 | 5330 | 1.5674 | - |
| 0.0534 | 5340 | 1.5562 | - |
| 0.0535 | 5350 | 1.5582 | - |
| 0.0536 | 5360 | 1.5712 | - |
| 0.0537 | 5370 | 1.5384 | - |
| 0.0538 | 5380 | 1.5717 | - |
| 0.0539 | 5390 | 1.5793 | - |
| 0.054 | 5400 | 1.5731 | 0.5094 |
| 0.0541 | 5410 | 1.5597 | - |
| 0.0542 | 5420 | 1.5852 | - |
| 0.0543 | 5430 | 1.5932 | - |
| 0.0544 | 5440 | 1.5545 | - |
| 0.0545 | 5450 | 1.5954 | - |
| 0.0546 | 5460 | 1.5711 | - |
| 0.0547 | 5470 | 1.5441 | - |
| 0.0548 | 5480 | 1.5649 | - |
| 0.0549 | 5490 | 1.5662 | - |
| 0.055 | 5500 | 1.5433 | 0.5161 |
| 0.0551 | 5510 | 1.5714 | - |
| 0.0552 | 5520 | 1.5406 | - |
| 0.0553 | 5530 | 1.5391 | - |
| 0.0554 | 5540 | 1.5676 | - |
| 0.0555 | 5550 | 1.5568 | - |
| 0.0556 | 5560 | 1.5784 | - |
| 0.0557 | 5570 | 1.5715 | - |
| 0.0558 | 5580 | 1.5635 | - |
| 0.0559 | 5590 | 1.5659 | - |
| 0.056 | 5600 | 1.59 | 0.5109 |
| 0.0561 | 5610 | 1.5562 | - |
| 0.0562 | 5620 | 1.554 | - |
| 0.0563 | 5630 | 1.5524 | - |
| 0.0564 | 5640 | 1.5544 | - |
| 0.0565 | 5650 | 1.5805 | - |
| 0.0566 | 5660 | 1.5431 | - |
| 0.0567 | 5670 | 1.5627 | - |
| 0.0568 | 5680 | 1.5731 | - |
| 0.0569 | 5690 | 1.5795 | - |
| 0.057 | 5700 | 1.5558 | 0.5133 |
| 0.0571 | 5710 | 1.5365 | - |
| 0.0572 | 5720 | 1.5882 | - |
| 0.0573 | 5730 | 1.5812 | - |
| 0.0574 | 5740 | 1.5553 | - |
| 0.0575 | 5750 | 1.5516 | - |
| 0.0576 | 5760 | 1.5523 | - |
| 0.0577 | 5770 | 1.5522 | - |
| 0.0578 | 5780 | 1.5634 | - |
| 0.0579 | 5790 | 1.5467 | - |
| 0.058 | 5800 | 1.561 | 0.5116 |
| 0.0581 | 5810 | 1.5614 | - |
| 0.0582 | 5820 | 1.5595 | - |
| 0.0583 | 5830 | 1.5655 | - |
| 0.0584 | 5840 | 1.5842 | - |
| 0.0585 | 5850 | 1.556 | - |
| 0.0586 | 5860 | 1.5526 | - |
| 0.0587 | 5870 | 1.5637 | - |
| 0.0588 | 5880 | 1.5612 | - |
| 0.0589 | 5890 | 1.5815 | - |
| 0.059 | 5900 | 1.5407 | 0.5109 |
| 0.0591 | 5910 | 1.5594 | - |
| 0.0592 | 5920 | 1.5551 | - |
| 0.0593 | 5930 | 1.5485 | - |
| 0.0594 | 5940 | 1.5832 | - |
| 0.0595 | 5950 | 1.5729 | - |
| 0.0596 | 5960 | 1.567 | - |
| 0.0597 | 5970 | 1.5743 | - |
| 0.0598 | 5980 | 1.5456 | - |
| 0.0599 | 5990 | 1.5446 | - |
| 0.06 | 6000 | 1.5754 | 0.5088 |
| 0.0601 | 6010 | 1.5551 | - |
| 0.0602 | 6020 | 1.5345 | - |
| 0.0603 | 6030 | 1.5446 | - |
| 0.0604 | 6040 | 1.569 | - |
| 0.0605 | 6050 | 1.5573 | - |
| 0.0606 | 6060 | 1.5332 | - |
| 0.0607 | 6070 | 1.5851 | - |
| 0.0608 | 6080 | 1.5464 | - |
| 0.0609 | 6090 | 1.5552 | - |
| 0.061 | 6100 | 1.5519 | 0.5106 |
| 0.0611 | 6110 | 1.55 | - |
| 0.0612 | 6120 | 1.5674 | - |
| 0.0613 | 6130 | 1.5552 | - |
| 0.0614 | 6140 | 1.5582 | - |
| 0.0615 | 6150 | 1.5509 | - |
| 0.0616 | 6160 | 1.5531 | - |
| 0.0617 | 6170 | 1.5411 | - |
| 0.0618 | 6180 | 1.5569 | - |
| 0.0619 | 6190 | 1.5531 | - |
| 0.062 | 6200 | 1.5636 | 0.5095 |
| 0.0621 | 6210 | 1.5427 | - |
| 0.0622 | 6220 | 1.5796 | - |
| 0.0623 | 6230 | 1.5355 | - |
| 0.0624 | 6240 | 1.5428 | - |
| 0.0625 | 6250 | 1.5297 | - |
| 0.0626 | 6260 | 1.5397 | - |
| 0.0627 | 6270 | 1.5606 | - |
| 0.0628 | 6280 | 1.5366 | - |
| 0.0629 | 6290 | 1.5391 | - |
| 0.063 | 6300 | 1.5649 | 0.5146 |
| 0.0631 | 6310 | 1.5612 | - |
| 0.0632 | 6320 | 1.5357 | - |
| 0.0633 | 6330 | 1.5406 | - |
| 0.0634 | 6340 | 1.5636 | - |
| 0.0635 | 6350 | 1.5423 | - |
| 0.0636 | 6360 | 1.5265 | - |
| 0.0637 | 6370 | 1.5507 | - |
| 0.0638 | 6380 | 1.5481 | - |
| 0.0639 | 6390 | 1.5464 | - |
| 0.064 | 6400 | 1.5459 | 0.5177 |
| 0.0641 | 6410 | 1.5542 | - |
| 0.0642 | 6420 | 1.5515 | - |
| 0.0643 | 6430 | 1.5741 | - |
| 0.0644 | 6440 | 1.5338 | - |
| 0.0645 | 6450 | 1.5527 | - |
| 0.0646 | 6460 | 1.5624 | - |
| 0.0647 | 6470 | 1.5469 | - |
| 0.0648 | 6480 | 1.5486 | - |
| 0.0649 | 6490 | 1.5582 | - |
| 0.065 | 6500 | 1.536 | 0.5100 |
| 0.0651 | 6510 | 1.5565 | - |
| 0.0652 | 6520 | 1.5353 | - |
| 0.0653 | 6530 | 1.5236 | - |
| 0.0654 | 6540 | 1.54 | - |
| 0.0655 | 6550 | 1.5448 | - |
| 0.0656 | 6560 | 1.5495 | - |
| 0.0657 | 6570 | 1.5394 | - |
| 0.0658 | 6580 | 1.5737 | - |
| 0.0659 | 6590 | 1.5201 | - |
| 0.066 | 6600 | 1.5509 | 0.5127 |
| 0.0661 | 6610 | 1.5549 | - |
| 0.0662 | 6620 | 1.5545 | - |
| 0.0663 | 6630 | 1.5601 | - |
| 0.0664 | 6640 | 1.537 | - |
| 0.0665 | 6650 | 1.5451 | - |
| 0.0666 | 6660 | 1.5412 | - |
| 1.0000 | 6670 | 1.5383 | - |
| 1.0001 | 6680 | 1.5399 | - |
| 1.0002 | 6690 | 1.5336 | - |
| 1.0003 | 6700 | 1.5344 | 0.5156 |
| 1.0004 | 6710 | 1.5272 | - |
| 1.0005 | 6720 | 1.5436 | - |
| 1.0006 | 6730 | 1.5438 | - |
| 1.0007 | 6740 | 1.5238 | - |
| 1.0008 | 6750 | 1.527 | - |
| 1.0009 | 6760 | 1.5379 | - |
| 1.0010 | 6770 | 1.5227 | - |
| 1.0011 | 6780 | 1.5424 | - |
| 1.0012 | 6790 | 1.5154 | - |
| 1.0013 | 6800 | 1.5199 | 0.5088 |
| 1.0014 | 6810 | 1.5277 | - |
| 1.0015 | 6820 | 1.5371 | - |
| 1.0016 | 6830 | 1.5455 | - |
| 1.0017 | 6840 | 1.5624 | - |
| 1.0018 | 6850 | 1.5471 | - |
| 1.0019 | 6860 | 1.5287 | - |
| 1.0020 | 6870 | 1.5495 | - |
| 1.0021 | 6880 | 1.5094 | - |
| 1.0022 | 6890 | 1.5313 | - |
| 1.0023 | 6900 | 1.5386 | 0.5092 |
| 1.0024 | 6910 | 1.5389 | - |
| 1.0025 | 6920 | 1.5424 | - |
| 1.0026 | 6930 | 1.5342 | - |
| 1.0027 | 6940 | 1.5497 | - |
| 1.0028 | 6950 | 1.5228 | - |
| 1.0029 | 6960 | 1.5294 | - |
| 1.0030 | 6970 | 1.5264 | - |
| 1.0031 | 6980 | 1.508 | - |
| 1.0032 | 6990 | 1.5189 | - |
| 1.0033 | 7000 | 1.5118 | 0.5125 |
| 1.0034 | 7010 | 1.5243 | - |
| 1.0035 | 7020 | 1.5297 | - |
| 1.0036 | 7030 | 1.5139 | - |
| 1.0037 | 7040 | 1.5347 | - |
| 1.0038 | 7050 | 1.5236 | - |
| 1.0039 | 7060 | 1.538 | - |
| 1.0040 | 7070 | 1.5402 | - |
| 1.0041 | 7080 | 1.5136 | - |
| 1.0042 | 7090 | 1.5556 | - |
| 1.0043 | 7100 | 1.5203 | 0.5082 |
| 1.0044 | 7110 | 1.5284 | - |
| 1.0045 | 7120 | 1.5455 | - |
| 1.0046 | 7130 | 1.5158 | - |
| 1.0047 | 7140 | 1.5284 | - |
| 1.0048 | 7150 | 1.5064 | - |
| 1.0049 | 7160 | 1.5362 | - |
| 1.0050 | 7170 | 1.5289 | - |
| 1.0051 | 7180 | 1.5309 | - |
| 1.0052 | 7190 | 1.5189 | - |
| 1.0053 | 7200 | 1.4976 | 0.5141 |
| 1.0054 | 7210 | 1.5245 | - |
| 1.0055 | 7220 | 1.5435 | - |
| 1.0056 | 7230 | 1.5165 | - |
| 1.0057 | 7240 | 1.5469 | - |
| 1.0058 | 7250 | 1.5298 | - |
| 1.0059 | 7260 | 1.5141 | - |
| 1.0060 | 7270 | 1.5313 | - |
| 1.0061 | 7280 | 1.5166 | - |
| 1.0062 | 7290 | 1.5311 | - |
| 1.0063 | 7300 | 1.5188 | 0.5248 |
| 1.0064 | 7310 | 1.5217 | - |
| 1.0065 | 7320 | 1.4895 | - |
| 1.0066 | 7330 | 1.5336 | - |
| 1.0067 | 7340 | 1.5275 | - |
| 1.0068 | 7350 | 1.5246 | - |
| 1.0069 | 7360 | 1.5264 | - |
| 1.0070 | 7370 | 1.5325 | - |
| 1.0071 | 7380 | 1.5262 | - |
| 1.0072 | 7390 | 1.5147 | - |
| 1.0073 | 7400 | 1.515 | 0.5183 |
| 1.0074 | 7410 | 1.5398 | - |
| 1.0075 | 7420 | 1.5269 | - |
| 1.0076 | 7430 | 1.5146 | - |
| 1.0077 | 7440 | 1.5092 | - |
| 1.0078 | 7450 | 1.5034 | - |
| 1.0079 | 7460 | 1.5042 | - |
| 1.0080 | 7470 | 1.5185 | - |
| 1.0081 | 7480 | 1.537 | - |
| 1.0082 | 7490 | 1.521 | - |
| 1.0083 | 7500 | 1.4988 | 0.5193 |
| 1.0084 | 7510 | 1.5177 | - |
| 1.0085 | 7520 | 1.5335 | - |
| 1.0086 | 7530 | 1.4924 | - |
| 1.0087 | 7540 | 1.5419 | - |
| 1.0088 | 7550 | 1.4974 | - |
| 1.0089 | 7560 | 1.5227 | - |
| 1.0090 | 7570 | 1.5242 | - |
| 1.0091 | 7580 | 1.5008 | - |
| 1.0092 | 7590 | 1.4978 | - |
| 1.0093 | 7600 | 1.5043 | 0.5133 |
| 1.0094 | 7610 | 1.5041 | - |
| 1.0095 | 7620 | 1.5295 | - |
| 1.0096 | 7630 | 1.5221 | - |
| 1.0097 | 7640 | 1.502 | - |
| 1.0098 | 7650 | 1.4861 | - |
| 1.0099 | 7660 | 1.5108 | - |
| 1.0100 | 7670 | 1.5257 | - |
| 1.0101 | 7680 | 1.5215 | - |
| 1.0102 | 7690 | 1.5413 | - |
| 1.0103 | 7700 | 1.5071 | 0.5195 |
| 1.0104 | 7710 | 1.5172 | - |
| 1.0105 | 7720 | 1.531 | - |
| 1.0106 | 7730 | 1.5104 | - |
| 1.0107 | 7740 | 1.5009 | - |
| 1.0108 | 7750 | 1.5046 | - |
| 1.0109 | 7760 | 1.5339 | - |
| 1.0110 | 7770 | 1.4935 | - |
| 1.0111 | 7780 | 1.5211 | - |
| 1.0112 | 7790 | 1.5203 | - |
| 1.0113 | 7800 | 1.5066 | 0.5172 |
| 1.0114 | 7810 | 1.5154 | - |
| 1.0115 | 7820 | 1.5233 | - |
| 1.0116 | 7830 | 1.5256 | - |
| 1.0117 | 7840 | 1.5082 | - |
| 1.0118 | 7850 | 1.5092 | - |
| 1.0119 | 7860 | 1.5279 | - |
| 1.0120 | 7870 | 1.5133 | - |
| 1.0121 | 7880 | 1.5168 | - |
| 1.0122 | 7890 | 1.4994 | - |
| 1.0123 | 7900 | 1.5309 | 0.5175 |
| 1.0124 | 7910 | 1.503 | - |
| 1.0125 | 7920 | 1.4806 | - |
| 1.0126 | 7930 | 1.5077 | - |
| 1.0127 | 7940 | 1.4981 | - |
| 1.0128 | 7950 | 1.5086 | - |
| 1.0129 | 7960 | 1.5003 | - |
| 1.0130 | 7970 | 1.5208 | - |
| 1.0131 | 7980 | 1.4941 | - |
| 1.0132 | 7990 | 1.5226 | - |
| 1.0133 | 8000 | 1.5023 | 0.5128 |
| 1.0134 | 8010 | 1.5249 | - |
| 1.0135 | 8020 | 1.4965 | - |
| 1.0136 | 8030 | 1.5012 | - |
| 1.0137 | 8040 | 1.5477 | - |
| 1.0138 | 8050 | 1.5128 | - |
| 1.0139 | 8060 | 1.4758 | - |
| 1.0140 | 8070 | 1.5248 | - |
| 1.0141 | 8080 | 1.5497 | - |
| 1.0142 | 8090 | 1.5281 | - |
| 1.0143 | 8100 | 1.5217 | 0.5182 |
| 1.0144 | 8110 | 1.4934 | - |
| 1.0145 | 8120 | 1.527 | - |
| 1.0146 | 8130 | 1.522 | - |
| 1.0147 | 8140 | 1.4958 | - |
| 1.0148 | 8150 | 1.5086 | - |
| 1.0149 | 8160 | 1.5038 | - |
| 1.0150 | 8170 | 1.4971 | - |
| 1.0151 | 8180 | 1.5217 | - |
| 1.0152 | 8190 | 1.4871 | - |
| 1.0153 | 8200 | 1.525 | 0.5223 |
| 1.0154 | 8210 | 1.5179 | - |
| 1.0155 | 8220 | 1.4958 | - |
| 1.0156 | 8230 | 1.5119 | - |
| 1.0157 | 8240 | 1.5116 | - |
| 1.0158 | 8250 | 1.4997 | - |
| 1.0159 | 8260 | 1.5089 | - |
| 1.0160 | 8270 | 1.5377 | - |
| 1.0161 | 8280 | 1.502 | - |
| 1.0162 | 8290 | 1.486 | - |
| 1.0163 | 8300 | 1.5065 | 0.5142 |
| 1.0164 | 8310 | 1.5047 | - |
| 1.0165 | 8320 | 1.5028 | - |
| 1.0166 | 8330 | 1.4898 | - |
| 1.0167 | 8340 | 1.5046 | - |
| 1.0168 | 8350 | 1.5195 | - |
| 1.0169 | 8360 | 1.4848 | - |
| 1.0170 | 8370 | 1.5242 | - |
| 1.0171 | 8380 | 1.4994 | - |
| 1.0172 | 8390 | 1.5306 | - |
| 1.0173 | 8400 | 1.5079 | 0.5113 |
| 1.0174 | 8410 | 1.5143 | - |
| 1.0175 | 8420 | 1.495 | - |
| 1.0176 | 8430 | 1.492 | - |
| 1.0177 | 8440 | 1.5087 | - |
| 1.0178 | 8450 | 1.4765 | - |
| 1.0179 | 8460 | 1.5043 | - |
| 1.0180 | 8470 | 1.5014 | - |
| 1.0181 | 8480 | 1.5275 | - |
| 1.0182 | 8490 | 1.5101 | - |
| 1.0183 | 8500 | 1.5117 | 0.5129 |
| 1.0184 | 8510 | 1.5254 | - |
| 1.0185 | 8520 | 1.5261 | - |
| 1.0186 | 8530 | 1.5111 | - |
| 1.0187 | 8540 | 1.5176 | - |
| 1.0188 | 8550 | 1.4906 | - |
| 1.0189 | 8560 | 1.5139 | - |
| 1.0190 | 8570 | 1.4893 | - |
| 1.0191 | 8580 | 1.5022 | - |
| 1.0192 | 8590 | 1.5134 | - |
| 1.0193 | 8600 | 1.5087 | 0.5143 |
| 1.0194 | 8610 | 1.512 | - |
| 1.0195 | 8620 | 1.495 | - |
| 1.0196 | 8630 | 1.4875 | - |
| 1.0197 | 8640 | 1.5239 | - |
| 1.0198 | 8650 | 1.513 | - |
| 1.0199 | 8660 | 1.5124 | - |
| 1.0200 | 8670 | 1.4855 | - |
| 1.0201 | 8680 | 1.5028 | - |
| 1.0202 | 8690 | 1.4998 | - |
| 1.0203 | 8700 | 1.4937 | 0.5149 |
| 1.0204 | 8710 | 1.5103 | - |
| 1.0205 | 8720 | 1.5105 | - |
| 1.0206 | 8730 | 1.5062 | - |
| 1.0207 | 8740 | 1.5228 | - |
| 1.0208 | 8750 | 1.4866 | - |
| 1.0209 | 8760 | 1.5092 | - |
| 1.0210 | 8770 | 1.5088 | - |
| 1.0211 | 8780 | 1.488 | - |
| 1.0212 | 8790 | 1.5022 | - |
| 1.0213 | 8800 | 1.504 | 0.5097 |
| 1.0214 | 8810 | 1.5051 | - |
| 1.0215 | 8820 | 1.5027 | - |
| 1.0216 | 8830 | 1.5273 | - |
| 1.0217 | 8840 | 1.4884 | - |
| 1.0218 | 8850 | 1.4908 | - |
| 1.0219 | 8860 | 1.5074 | - |
| 1.0220 | 8870 | 1.516 | - |
| 1.0221 | 8880 | 1.5422 | - |
| 1.0222 | 8890 | 1.5174 | - |
| 1.0223 | 8900 | 1.5009 | 0.5129 |
| 1.0224 | 8910 | 1.4936 | - |
| 1.0225 | 8920 | 1.5076 | - |
| 1.0226 | 8930 | 1.5093 | - |
| 1.0227 | 8940 | 1.4848 | - |
| 1.0228 | 8950 | 1.4958 | - |
| 1.0229 | 8960 | 1.5092 | - |
| 1.0230 | 8970 | 1.5048 | - |
| 1.0231 | 8980 | 1.4895 | - |
| 1.0232 | 8990 | 1.4949 | - |
| 1.0233 | 9000 | 1.4988 | 0.5131 |
| 1.0234 | 9010 | 1.5205 | - |
| 1.0235 | 9020 | 1.4961 | - |
| 1.0236 | 9030 | 1.5235 | - |
| 1.0237 | 9040 | 1.4945 | - |
| 1.0238 | 9050 | 1.504 | - |
| 1.0239 | 9060 | 1.5147 | - |
| 1.0240 | 9070 | 1.5104 | - |
| 1.0241 | 9080 | 1.4981 | - |
| 1.0242 | 9090 | 1.5019 | - |
| 1.0243 | 9100 | 1.4843 | 0.5163 |
| 1.0244 | 9110 | 1.4691 | - |
| 1.0245 | 9120 | 1.5097 | - |
| 1.0246 | 9130 | 1.524 | - |
| 1.0247 | 9140 | 1.5055 | - |
| 1.0248 | 9150 | 1.5212 | - |
| 1.0249 | 9160 | 1.4888 | - |
| 1.0250 | 9170 | 1.4887 | - |
| 1.0251 | 9180 | 1.5051 | - |
| 1.0252 | 9190 | 1.491 | - |
| 1.0253 | 9200 | 1.492 | 0.5140 |
| 1.0254 | 9210 | 1.4771 | - |
| 1.0255 | 9220 | 1.4683 | - |
| 1.0256 | 9230 | 1.4824 | - |
| 1.0257 | 9240 | 1.4967 | - |
| 1.0258 | 9250 | 1.5054 | - |
| 1.0259 | 9260 | 1.4884 | - |
| 1.0260 | 9270 | 1.4975 | - |
| 1.0261 | 9280 | 1.4918 | - |
| 1.0262 | 9290 | 1.5143 | - |
| 1.0263 | 9300 | 1.4946 | 0.5168 |
| 1.0264 | 9310 | 1.4905 | - |
| 1.0265 | 9320 | 1.4981 | - |
| 1.0266 | 9330 | 1.4591 | - |
| 1.0267 | 9340 | 1.4933 | - |
| 1.0268 | 9350 | 1.5028 | - |
| 1.0269 | 9360 | 1.5035 | - |
| 1.0270 | 9370 | 1.4996 | - |
| 1.0271 | 9380 | 1.4939 | - |
| 1.0272 | 9390 | 1.505 | - |
| 1.0273 | 9400 | 1.496 | 0.5120 |
| 1.0274 | 9410 | 1.5085 | - |
| 1.0275 | 9420 | 1.5057 | - |
| 1.0276 | 9430 | 1.5065 | - |
| 1.0277 | 9440 | 1.4898 | - |
| 1.0278 | 9450 | 1.5031 | - |
| 1.0279 | 9460 | 1.4784 | - |
| 1.0280 | 9470 | 1.4906 | - |
| 1.0281 | 9480 | 1.4878 | - |
| 1.0282 | 9490 | 1.4931 | - |
| 1.0283 | 9500 | 1.5035 | 0.5159 |
| 1.0284 | 9510 | 1.4766 | - |
| 1.0285 | 9520 | 1.497 | - |
| 1.0286 | 9530 | 1.4965 | - |
| 1.0287 | 9540 | 1.4743 | - |
| 1.0288 | 9550 | 1.4836 | - |
| 1.0289 | 9560 | 1.5031 | - |
| 1.0290 | 9570 | 1.5287 | - |
| 1.0291 | 9580 | 1.4851 | - |
| 1.0292 | 9590 | 1.5061 | - |
| 1.0293 | 9600 | 1.5071 | 0.5191 |
| 1.0294 | 9610 | 1.4943 | - |
| 1.0295 | 9620 | 1.502 | - |
| 1.0296 | 9630 | 1.4953 | - |
| 1.0297 | 9640 | 1.4923 | - |
| 1.0298 | 9650 | 1.489 | - |
| 1.0299 | 9660 | 1.4968 | - |
| 1.0300 | 9670 | 1.5051 | - |
| 1.0301 | 9680 | 1.4736 | - |
| 1.0302 | 9690 | 1.4883 | - |
| 1.0303 | 9700 | 1.5062 | 0.5145 |
| 1.0304 | 9710 | 1.4961 | - |
| 1.0305 | 9720 | 1.5107 | - |
| 1.0306 | 9730 | 1.4852 | - |
| 1.0307 | 9740 | 1.4771 | - |
| 1.0308 | 9750 | 1.4911 | - |
| 1.0309 | 9760 | 1.4781 | - |
| 1.0310 | 9770 | 1.5064 | - |
| 1.0311 | 9780 | 1.4871 | - |
| 1.0312 | 9790 | 1.5042 | - |
| 1.0313 | 9800 | 1.4922 | 0.5190 |
| 1.0314 | 9810 | 1.4952 | - |
| 1.0315 | 9820 | 1.4875 | - |
| 1.0316 | 9830 | 1.4974 | - |
| 1.0317 | 9840 | 1.5013 | - |
| 1.0318 | 9850 | 1.4966 | - |
| 1.0319 | 9860 | 1.4861 | - |
| 1.0320 | 9870 | 1.5135 | - |
| 1.0321 | 9880 | 1.5038 | - |
| 1.0322 | 9890 | 1.5148 | - |
| 1.0323 | 9900 | 1.5164 | 0.5195 |
| 1.0324 | 9910 | 1.494 | - |
| 1.0325 | 9920 | 1.4848 | - |
| 1.0326 | 9930 | 1.5015 | - |
| 1.0327 | 9940 | 1.4868 | - |
| 1.0328 | 9950 | 1.4728 | - |
| 1.0329 | 9960 | 1.4989 | - |
| 1.0330 | 9970 | 1.4742 | - |
| 1.0331 | 9980 | 1.4743 | - |
| 1.0332 | 9990 | 1.4996 | - |
| 1.0333 | 10000 | 1.4748 | 0.5134 |
</details>
### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.2.0
- Transformers: 4.57.3
- PyTorch: 2.9.0+cu128
- Accelerate: 1.12.0
- Datasets: 4.4.2
- Tokenizers: 0.22.1
## 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",
}
```
#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
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