sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:510287 loss:CoSENTLoss en arxiv:1908.10084 base_model:sentence-transformers/all-MiniLM-L12-v2 base_model:finetune:sentence-transformers/all-MiniLM-L12-v2 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/all-MiniLM-L12-v2
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:510287
- loss:CoSENTLoss
widget:
- source_sentence: bag
  sentences:
  - bag
  - summer colors bag
  - carry all bag
- source_sentence: bean bag
  sentences:
  - bag
  - havan bag
  - black yellow shoes
- source_sentence: pyramid shaped cushion mattress
  sentences:
  - dress
  - silver bag
  - women shoes
- source_sentence: handcrafted rug
  sentences:
  - amaga  cross bag - white
  - handcrafted boots
  - polyester top
- source_sentence: bean bag
  sentences:
  - bag
  - v-neck dress
  - bag
model-index:
- name: all-MiniLM-L12-v2-pair_score
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: -0.10403022864037037
      name: Pearson Cosine
    - type: spearman_cosine
      value: -0.1437799564130218
      name: Spearman Cosine
    - type: pearson_manhattan
      value: -0.10847915569723102
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: -0.14274368509273366
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: -0.11064121359722408
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: -0.14377964610318103
      name: Spearman Euclidean
    - type: pearson_dot
      value: -0.10403015819885228
      name: Pearson Dot
    - type: spearman_dot
      value: -0.14377961300118045
      name: Spearman Dot
    - type: pearson_max
      value: -0.10403015819885228
      name: Pearson Max
    - type: spearman_max
      value: -0.14274368509273366
      name: Spearman Max
---

# all-MiniLM-L12-v2-pair_score

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2). 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:** [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) <!-- at revision 30ce63ae64e71b9199b3d2eae9de99f64a26eedc -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0

### 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': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'bean bag',
    'bag',
    'v-neck dress',
]
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.*
-->

## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value       |
|:--------------------|:------------|
| pearson_cosine      | -0.104      |
| **spearman_cosine** | **-0.1438** |
| pearson_manhattan   | -0.1085     |
| spearman_manhattan  | -0.1427     |
| pearson_euclidean   | -0.1106     |
| spearman_euclidean  | -0.1438     |
| pearson_dot         | -0.104      |
| spearman_dot        | -0.1438     |
| pearson_max         | -0.104      |
| spearman_max        | -0.1427     |

<!--
## 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 Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `fp16`: True

#### 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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 4
- `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 | loss   | sts-dev_spearman_cosine |
|:------:|:-----:|:-------------:|:------:|:-----------------------:|
| 0      | 0     | -             | -      | -0.1438                 |
| 0.0063 | 100   | 11.9171       | -      | -                       |
| 0.0125 | 200   | 11.0074       | -      | -                       |
| 0.0188 | 300   | 10.1073       | -      | -                       |
| 0.0251 | 400   | 8.6232        | -      | -                       |
| 0.0314 | 500   | 7.5947        | 7.2720 | -                       |
| 0.0376 | 600   | 6.3883        | -      | -                       |
| 0.0439 | 700   | 5.6165        | -      | -                       |
| 0.0502 | 800   | 4.8254        | -      | -                       |
| 0.0564 | 900   | 4.5595        | -      | -                       |
| 0.0627 | 1000  | 4.2965        | 4.1720 | -                       |
| 0.0690 | 1100  | 4.063         | -      | -                       |
| 0.0752 | 1200  | 4.0861        | -      | -                       |
| 0.0815 | 1300  | 3.9703        | -      | -                       |
| 0.0878 | 1400  | 3.8222        | -      | -                       |
| 0.0941 | 1500  | 3.927         | 3.6404 | -                       |
| 0.1003 | 1600  | 3.6892        | -      | -                       |
| 0.1066 | 1700  | 3.9166        | -      | -                       |
| 0.1129 | 1800  | 3.7162        | -      | -                       |
| 0.1191 | 1900  | 3.4866        | -      | -                       |
| 0.1254 | 2000  | 3.5202        | 3.4226 | -                       |
| 0.1317 | 2100  | 3.6876        | -      | -                       |
| 0.1380 | 2200  | 3.4884        | -      | -                       |
| 0.1442 | 2300  | 3.4407        | -      | -                       |
| 0.1505 | 2400  | 3.2658        | -      | -                       |
| 0.1568 | 2500  | 3.2973        | 3.0777 | -                       |
| 0.1630 | 2600  | 3.2087        | -      | -                       |
| 0.1693 | 2700  | 3.4316        | -      | -                       |
| 0.1756 | 2800  | 3.3372        | -      | -                       |
| 0.1819 | 2900  | 3.161         | -      | -                       |
| 0.1881 | 3000  | 3.0232        | 2.8805 | -                       |
| 0.1944 | 3100  | 3.2897        | -      | -                       |
| 0.2007 | 3200  | 3.2576        | -      | -                       |
| 0.2069 | 3300  | 2.7636        | -      | -                       |
| 0.2132 | 3400  | 3.1788        | -      | -                       |
| 0.2195 | 3500  | 2.6269        | 2.6237 | -                       |
| 0.2257 | 3600  | 2.9352        | -      | -                       |
| 0.2320 | 3700  | 2.847         | -      | -                       |
| 0.2383 | 3800  | 2.8001        | -      | -                       |
| 0.2446 | 3900  | 2.6048        | -      | -                       |
| 0.2508 | 4000  | 2.5976        | 2.5250 | -                       |
| 0.2571 | 4100  | 2.5211        | -      | -                       |
| 0.2634 | 4200  | 2.7812        | -      | -                       |
| 0.2696 | 4300  | 2.6822        | -      | -                       |
| 0.2759 | 4400  | 2.4779        | -      | -                       |
| 0.2822 | 4500  | 2.6242        | 2.6365 | -                       |
| 0.2885 | 4600  | 2.5655        | -      | -                       |
| 0.2947 | 4700  | 2.9998        | -      | -                       |
| 0.3010 | 4800  | 2.679         | -      | -                       |
| 0.3073 | 4900  | 2.5719        | -      | -                       |
| 0.3135 | 5000  | 2.6913        | 2.6934 | -                       |
| 0.3198 | 5100  | 2.8346        | -      | -                       |
| 0.3261 | 5200  | 2.7453        | -      | -                       |
| 0.3324 | 5300  | 2.4492        | -      | -                       |
| 0.3386 | 5400  | 2.9389        | -      | -                       |
| 0.3449 | 5500  | 2.6002        | 2.6182 | -                       |
| 0.3512 | 5600  | 2.2592        | -      | -                       |
| 0.3574 | 5700  | 2.3822        | -      | -                       |
| 0.3637 | 5800  | 2.4771        | -      | -                       |
| 0.3700 | 5900  | 3.5914        | -      | -                       |
| 0.3762 | 6000  | 2.3525        | 2.5605 | -                       |
| 0.3825 | 6100  | 2.2667        | -      | -                       |
| 0.3888 | 6200  | 2.4671        | -      | -                       |
| 0.3951 | 6300  | 2.6816        | -      | -                       |
| 0.4013 | 6400  | 2.2303        | -      | -                       |
| 0.4076 | 6500  | 2.3153        | 2.4245 | -                       |
| 0.4139 | 6600  | 2.7969        | -      | -                       |
| 0.4201 | 6700  | 2.61          | -      | -                       |
| 0.4264 | 6800  | 2.5267        | -      | -                       |
| 0.4327 | 6900  | 2.532         | -      | -                       |
| 0.4390 | 7000  | 2.6088        | 2.4666 | -                       |
| 0.4452 | 7100  | 1.848         | -      | -                       |
| 0.4515 | 7200  | 2.1369        | -      | -                       |
| 0.4578 | 7300  | 2.185         | -      | -                       |
| 0.4640 | 7400  | 2.0279        | -      | -                       |
| 0.4703 | 7500  | 2.5593        | 2.3958 | -                       |
| 0.4766 | 7600  | 2.339         | -      | -                       |
| 0.4828 | 7700  | 2.2122        | -      | -                       |
| 0.4891 | 7800  | 2.7878        | -      | -                       |
| 0.4954 | 7900  | 2.3005        | -      | -                       |
| 0.5017 | 8000  | 2.2922        | 2.5408 | -                       |
| 0.5079 | 8100  | 2.3731        | -      | -                       |
| 0.5142 | 8200  | 2.1879        | -      | -                       |
| 0.5205 | 8300  | 2.1598        | -      | -                       |
| 0.5267 | 8400  | 2.2292        | -      | -                       |
| 0.5330 | 8500  | 1.958         | 2.0935 | -                       |
| 0.5393 | 8600  | 2.1152        | -      | -                       |
| 0.5456 | 8700  | 1.9725        | -      | -                       |
| 0.5518 | 8800  | 2.1106        | -      | -                       |
| 0.5581 | 8900  | 2.06          | -      | -                       |
| 0.5644 | 9000  | 1.7624        | 2.1509 | -                       |
| 0.5706 | 9100  | 2.3793        | -      | -                       |
| 0.5769 | 9200  | 1.9322        | -      | -                       |
| 0.5832 | 9300  | 1.8355        | -      | -                       |
| 0.5895 | 9400  | 2.1425        | -      | -                       |
| 0.5957 | 9500  | 2.2191        | 1.9984 | -                       |
| 0.6020 | 9600  | 2.3245        | -      | -                       |
| 0.6083 | 9700  | 2.1206        | -      | -                       |
| 0.6145 | 9800  | 2.0957        | -      | -                       |
| 0.6208 | 9900  | 2.5276        | -      | -                       |
| 0.6271 | 10000 | 1.5383        | 1.9509 | -                       |
| 0.6333 | 10100 | 2.111         | -      | -                       |
| 0.6396 | 10200 | 1.893         | -      | -                       |
| 0.6459 | 10300 | 1.8961        | -      | -                       |
| 0.6522 | 10400 | 1.6599        | -      | -                       |
| 0.6584 | 10500 | 2.3409        | 1.8286 | -                       |
| 0.6647 | 10600 | 1.9741        | -      | -                       |
| 0.6710 | 10700 | 2.0438        | -      | -                       |
| 0.6772 | 10800 | 1.814         | -      | -                       |
| 0.6835 | 10900 | 2.1819        | -      | -                       |
| 0.6898 | 11000 | 1.8547        | 1.9461 | -                       |
| 0.6961 | 11100 | 2.5979        | -      | -                       |
| 0.7023 | 11200 | 1.9309        | -      | -                       |
| 0.7086 | 11300 | 1.6247        | -      | -                       |
| 0.7149 | 11400 | 2.1107        | -      | -                       |
| 0.7211 | 11500 | 2.1264        | 1.8004 | -                       |
| 0.7274 | 11600 | 1.7397        | -      | -                       |
| 0.7337 | 11700 | 1.9569        | -      | -                       |
| 0.7400 | 11800 | 1.4769        | -      | -                       |
| 0.7462 | 11900 | 1.6222        | -      | -                       |
| 0.7525 | 12000 | 1.5354        | 1.6811 | -                       |
| 0.7588 | 12100 | 2.2645        | -      | -                       |
| 0.7650 | 12200 | 1.8662        | -      | -                       |
| 0.7713 | 12300 | 1.5327        | -      | -                       |
| 0.7776 | 12400 | 1.9501        | -      | -                       |
| 0.7838 | 12500 | 2.0923        | 1.6134 | -                       |
| 0.7901 | 12600 | 1.8887        | -      | -                       |
| 0.7964 | 12700 | 1.7207        | -      | -                       |
| 0.8027 | 12800 | 1.8589        | -      | -                       |
| 0.8089 | 12900 | 1.7602        | -      | -                       |
| 0.8152 | 13000 | 2.2405        | 1.5030 | -                       |
| 0.8215 | 13100 | 1.6249        | -      | -                       |
| 0.8277 | 13200 | 1.6814        | -      | -                       |
| 0.8340 | 13300 | 1.4072        | -      | -                       |
| 0.8403 | 13400 | 1.6286        | -      | -                       |
| 0.8466 | 13500 | 2.2081        | 1.6078 | -                       |
| 0.8528 | 13600 | 1.7387        | -      | -                       |
| 0.8591 | 13700 | 1.5268        | -      | -                       |
| 0.8654 | 13800 | 1.5693        | -      | -                       |
| 0.8716 | 13900 | 1.2473        | -      | -                       |
| 0.8779 | 14000 | 1.361         | 1.7168 | -                       |
| 0.8842 | 14100 | 1.5246        | -      | -                       |
| 0.8904 | 14200 | 1.7266        | -      | -                       |
| 0.8967 | 14300 | 0.9221        | -      | -                       |
| 0.9030 | 14400 | 1.6397        | -      | -                       |
| 0.9093 | 14500 | 1.3139        | 1.5492 | -                       |
| 0.9155 | 14600 | 1.7942        | -      | -                       |
| 0.9218 | 14700 | 1.5206        | -      | -                       |
| 0.9281 | 14800 | 1.5868        | -      | -                       |
| 0.9343 | 14900 | 1.2131        | -      | -                       |
| 0.9406 | 15000 | 1.8765        | 1.4192 | -                       |
| 0.9469 | 15100 | 1.624         | -      | -                       |
| 0.9532 | 15200 | 1.4692        | -      | -                       |
| 0.9594 | 15300 | 1.5426        | -      | -                       |
| 0.9657 | 15400 | 1.3668        | -      | -                       |
| 0.9720 | 15500 | 1.3951        | 1.6835 | -                       |
| 0.9782 | 15600 | 1.1567        | -      | -                       |
| 0.9845 | 15700 | 1.8634        | -      | -                       |
| 0.9908 | 15800 | 1.641         | -      | -                       |
| 0.9971 | 15900 | 1.6458        | -      | -                       |
| 1.0033 | 16000 | 1.1369        | 1.5746 | -                       |
| 1.0096 | 16100 | 1.1913        | -      | -                       |
| 1.0159 | 16200 | 1.5563        | -      | -                       |
| 1.0221 | 16300 | 1.4081        | -      | -                       |
| 1.0284 | 16400 | 1.8157        | -      | -                       |
| 1.0347 | 16500 | 1.6405        | 1.5235 | -                       |
| 1.0409 | 16600 | 0.9207        | -      | -                       |
| 1.0472 | 16700 | 1.4301        | -      | -                       |
| 1.0535 | 16800 | 1.4566        | -      | -                       |
| 1.0598 | 16900 | 1.5397        | -      | -                       |
| 1.0660 | 17000 | 1.3417        | 1.3883 | -                       |
| 1.0723 | 17100 | 0.9769        | -      | -                       |
| 1.0786 | 17200 | 1.3734        | -      | -                       |
| 1.0848 | 17300 | 1.0874        | -      | -                       |
| 1.0911 | 17400 | 1.2601        | -      | -                       |
| 1.0974 | 17500 | 1.4799        | 1.4361 | -                       |
| 1.1037 | 17600 | 1.1086        | -      | -                       |
| 1.1099 | 17700 | 1.3731        | -      | -                       |
| 1.1162 | 17800 | 1.0515        | -      | -                       |
| 1.1225 | 17900 | 1.7916        | -      | -                       |
| 1.1287 | 18000 | 1.7606        | 1.3792 | -                       |
| 1.1350 | 18100 | 1.3844        | -      | -                       |
| 1.1413 | 18200 | 1.3567        | -      | -                       |
| 1.1476 | 18300 | 1.4322        | -      | -                       |
| 1.1538 | 18400 | 1.9509        | -      | -                       |
| 1.1601 | 18500 | 1.0303        | 1.3425 | -                       |
| 1.1664 | 18600 | 1.6484        | -      | -                       |
| 1.1726 | 18700 | 1.1177        | -      | -                       |
| 1.1789 | 18800 | 1.0295        | -      | -                       |
| 1.1852 | 18900 | 1.4364        | -      | -                       |
| 1.1914 | 19000 | 1.1954        | 1.3385 | -                       |
| 1.1977 | 19100 | 1.1944        | -      | -                       |
| 1.2040 | 19200 | 0.9109        | -      | -                       |
| 1.2103 | 19300 | 1.4191        | -      | -                       |
| 1.2165 | 19400 | 1.5755        | -      | -                       |
| 1.2228 | 19500 | 1.0958        | 1.2872 | -                       |
| 1.2291 | 19600 | 0.9054        | -      | -                       |
| 1.2353 | 19700 | 1.0892        | -      | -                       |
| 1.2416 | 19800 | 1.4455        | -      | -                       |
| 1.2479 | 19900 | 1.3273        | -      | -                       |
| 1.2542 | 20000 | 1.6442        | 1.2880 | -                       |
| 1.2604 | 20100 | 1.1901        | -      | -                       |
| 1.2667 | 20200 | 0.9871        | -      | -                       |
| 1.2730 | 20300 | 1.6448        | -      | -                       |
| 1.2792 | 20400 | 1.1899        | -      | -                       |
| 1.2855 | 20500 | 1.3454        | 1.3303 | -                       |
| 1.2918 | 20600 | 1.4376        | -      | -                       |
| 1.2980 | 20700 | 1.0356        | -      | -                       |
| 1.3043 | 20800 | 1.7588        | -      | -                       |
| 1.3106 | 20900 | 1.0993        | -      | -                       |
| 1.3169 | 21000 | 1.3673        | 1.2607 | -                       |
| 1.3231 | 21100 | 1.3326        | -      | -                       |
| 1.3294 | 21200 | 1.3618        | -      | -                       |
| 1.3357 | 21300 | 1.3123        | -      | -                       |
| 1.3419 | 21400 | 0.9771        | -      | -                       |
| 1.3482 | 21500 | 1.1626        | 1.2873 | -                       |
| 1.3545 | 21600 | 1.41          | -      | -                       |
| 1.3608 | 21700 | 1.6998        | -      | -                       |
| 1.3670 | 21800 | 0.8335        | -      | -                       |
| 1.3733 | 21900 | 1.579         | -      | -                       |
| 1.3796 | 22000 | 1.6073        | 1.2164 | -                       |
| 1.3858 | 22100 | 1.0534        | -      | -                       |
| 1.3921 | 22200 | 1.0045        | -      | -                       |
| 1.3984 | 22300 | 1.4195        | -      | -                       |
| 1.4047 | 22400 | 1.4409        | -      | -                       |
| 1.4109 | 22500 | 1.3942        | 1.2018 | -                       |
| 1.4172 | 22600 | 1.6013        | -      | -                       |
| 1.4235 | 22700 | 1.139         | -      | -                       |
| 1.4297 | 22800 | 0.7062        | -      | -                       |
| 1.4360 | 22900 | 1.1948        | -      | -                       |
| 1.4423 | 23000 | 1.6784        | 1.1736 | -                       |
| 1.4485 | 23100 | 1.1618        | -      | -                       |
| 1.4548 | 23200 | 0.827         | -      | -                       |
| 1.4611 | 23300 | 1.0041        | -      | -                       |
| 1.4674 | 23400 | 0.7447        | -      | -                       |
| 1.4736 | 23500 | 1.1531        | 1.0797 | -                       |
| 1.4799 | 23600 | 1.0904        | -      | -                       |
| 1.4862 | 23700 | 1.0648        | -      | -                       |
| 1.4924 | 23800 | 1.1863        | -      | -                       |
| 1.4987 | 23900 | 0.893         | -      | -                       |
| 1.5050 | 24000 | 1.2528        | 1.0737 | -                       |
| 1.5113 | 24100 | 0.9333        | -      | -                       |
| 1.5175 | 24200 | 1.3404        | -      | -                       |
| 1.5238 | 24300 | 0.8959        | -      | -                       |
| 1.5301 | 24400 | 0.6898        | -      | -                       |
| 1.5363 | 24500 | 0.9896        | 1.1813 | -                       |
| 1.5426 | 24600 | 0.7928        | -      | -                       |
| 1.5489 | 24700 | 1.4153        | -      | -                       |
| 1.5552 | 24800 | 1.2393        | -      | -                       |
| 1.5614 | 24900 | 0.744         | -      | -                       |
| 1.5677 | 25000 | 0.7545        | 1.0823 | -                       |
| 1.5740 | 25100 | 1.1936        | -      | -                       |
| 1.5802 | 25200 | 0.8755        | -      | -                       |
| 1.5865 | 25300 | 1.063         | -      | -                       |
| 1.5928 | 25400 | 0.8634        | -      | -                       |
| 1.5990 | 25500 | 1.2905        | 1.0718 | -                       |
| 1.6053 | 25600 | 1.0906        | -      | -                       |
| 1.6116 | 25700 | 1.1594        | -      | -                       |
| 1.6179 | 25800 | 1.108         | -      | -                       |
| 1.6241 | 25900 | 1.2538        | -      | -                       |
| 1.6304 | 26000 | 1.3377        | 1.1370 | -                       |
| 1.6367 | 26100 | 0.8156        | -      | -                       |
| 1.6429 | 26200 | 0.9753        | -      | -                       |
| 1.6492 | 26300 | 1.0909        | -      | -                       |
| 1.6555 | 26400 | 1.0029        | -      | -                       |
| 1.6618 | 26500 | 0.6841        | 1.0385 | -                       |
| 1.6680 | 26600 | 1.1673        | -      | -                       |
| 1.6743 | 26700 | 1.3606        | -      | -                       |
| 1.6806 | 26800 | 0.4306        | -      | -                       |
| 1.6868 | 26900 | 1.0989        | -      | -                       |
| 1.6931 | 27000 | 1.3283        | 1.0136 | -                       |
| 1.6994 | 27100 | 1.0206        | -      | -                       |
| 1.7056 | 27200 | 0.6866        | -      | -                       |
| 1.7119 | 27300 | 0.9168        | -      | -                       |
| 1.7182 | 27400 | 0.9472        | -      | -                       |
| 1.7245 | 27500 | 0.7866        | 1.0890 | -                       |
| 1.7307 | 27600 | 1.481         | -      | -                       |
| 1.7370 | 27700 | 1.0311        | -      | -                       |
| 1.7433 | 27800 | 1.3346        | -      | -                       |
| 1.7495 | 27900 | 0.8331        | -      | -                       |
| 1.7558 | 28000 | 1.3056        | 0.9919 | -                       |
| 1.7621 | 28100 | 0.9692        | -      | -                       |
| 1.7684 | 28200 | 0.9337        | -      | -                       |
| 1.7746 | 28300 | 1.1588        | -      | -                       |
| 1.7809 | 28400 | 1.0859        | -      | -                       |
| 1.7872 | 28500 | 0.9939        | 1.0109 | -                       |
| 1.7934 | 28600 | 1.4019        | -      | -                       |
| 1.7997 | 28700 | 0.9404        | -      | -                       |
| 1.8060 | 28800 | 0.7085        | -      | -                       |
| 1.8123 | 28900 | 1.1423        | -      | -                       |
| 1.8185 | 29000 | 0.8389        | 0.9510 | -                       |
| 1.8248 | 29100 | 1.3947        | -      | -                       |
| 1.8311 | 29200 | 0.8909        | -      | -                       |
| 1.8373 | 29300 | 1.3824        | -      | -                       |
| 1.8436 | 29400 | 0.6364        | -      | -                       |
| 1.8499 | 29500 | 1.2197        | 0.9501 | -                       |
| 1.8561 | 29600 | 0.6353        | -      | -                       |
| 1.8624 | 29700 | 1.3453        | -      | -                       |
| 1.8687 | 29800 | 1.1069        | -      | -                       |
| 1.8750 | 29900 | 0.9873        | -      | -                       |
| 1.8812 | 30000 | 0.9291        | 1.0391 | -                       |
| 1.8875 | 30100 | 1.3971        | -      | -                       |
| 1.8938 | 30200 | 1.0569        | -      | -                       |
| 1.9000 | 30300 | 0.6731        | -      | -                       |
| 1.9063 | 30400 | 1.0216        | -      | -                       |
| 1.9126 | 30500 | 1.295         | 0.9819 | -                       |
| 1.9189 | 30600 | 1.1641        | -      | -                       |
| 1.9251 | 30700 | 0.9199        | -      | -                       |
| 1.9314 | 30800 | 0.9774        | -      | -                       |
| 1.9377 | 30900 | 0.8242        | -      | -                       |
| 1.9439 | 31000 | 1.4039        | 0.9666 | -                       |
| 1.9502 | 31100 | 0.7112        | -      | -                       |
| 1.9565 | 31200 | 0.846         | -      | -                       |
| 1.9628 | 31300 | 1.0952        | -      | -                       |
| 1.9690 | 31400 | 1.0372        | -      | -                       |
| 1.9753 | 31500 | 0.9585        | 0.8983 | -                       |
| 1.9816 | 31600 | 1.1527        | -      | -                       |
| 1.9878 | 31700 | 0.7675        | -      | -                       |
| 1.9941 | 31800 | 0.8359        | -      | -                       |
| 2.0004 | 31900 | 1.1224        | -      | -                       |
| 2.0066 | 32000 | 1.3421        | 0.9575 | -                       |
| 2.0129 | 32100 | 0.9171        | -      | -                       |
| 2.0192 | 32200 | 0.5865        | -      | -                       |
| 2.0255 | 32300 | 0.9239        | -      | -                       |
| 2.0317 | 32400 | 0.7426        | -      | -                       |
| 2.0380 | 32500 | 0.8965        | 0.9158 | -                       |
| 2.0443 | 32600 | 0.6605        | -      | -                       |
| 2.0505 | 32700 | 0.8507        | -      | -                       |
| 2.0568 | 32800 | 0.7288        | -      | -                       |
| 2.0631 | 32900 | 0.6888        | -      | -                       |
| 2.0694 | 33000 | 0.8745        | 0.9568 | -                       |
| 2.0756 | 33100 | 0.7972        | -      | -                       |
| 2.0819 | 33200 | 0.6211        | -      | -                       |
| 2.0882 | 33300 | 1.0126        | -      | -                       |
| 2.0944 | 33400 | 0.8268        | -      | -                       |
| 2.1007 | 33500 | 0.9723        | 0.8551 | -                       |
| 2.1070 | 33600 | 0.6366        | -      | -                       |
| 2.1133 | 33700 | 0.6773        | -      | -                       |
| 2.1195 | 33800 | 0.7676        | -      | -                       |
| 2.1258 | 33900 | 0.9192        | -      | -                       |
| 2.1321 | 34000 | 0.7054        | 0.8941 | -                       |
| 2.1383 | 34100 | 0.7349        | -      | -                       |
| 2.1446 | 34200 | 0.6288        | -      | -                       |
| 2.1509 | 34300 | 0.799         | -      | -                       |
| 2.1571 | 34400 | 0.7492        | -      | -                       |
| 2.1634 | 34500 | 1.0967        | 0.8746 | -                       |
| 2.1697 | 34600 | 0.7628        | -      | -                       |
| 2.1760 | 34700 | 0.7697        | -      | -                       |
| 2.1822 | 34800 | 0.7458        | -      | -                       |
| 2.1885 | 34900 | 0.7868        | -      | -                       |
| 2.1948 | 35000 | 0.9526        | 0.8620 | -                       |
| 2.2010 | 35100 | 0.6087        | -      | -                       |
| 2.2073 | 35200 | 0.8602        | -      | -                       |
| 2.2136 | 35300 | 0.8906        | -      | -                       |
| 2.2199 | 35400 | 0.6012        | -      | -                       |
| 2.2261 | 35500 | 0.9625        | 0.9094 | -                       |
| 2.2324 | 35600 | 0.8622        | -      | -                       |
| 2.2387 | 35700 | 0.9015        | -      | -                       |
| 2.2449 | 35800 | 1.0395        | -      | -                       |
| 2.2512 | 35900 | 0.5582        | -      | -                       |
| 2.2575 | 36000 | 0.7266        | 0.8666 | -                       |
| 2.2637 | 36100 | 0.6806        | -      | -                       |
| 2.2700 | 36200 | 0.9246        | -      | -                       |
| 2.2763 | 36300 | 0.7452        | -      | -                       |
| 2.2826 | 36400 | 0.7886        | -      | -                       |
| 2.2888 | 36500 | 0.9288        | 0.8529 | -                       |
| 2.2951 | 36600 | 1.2166        | -      | -                       |
| 2.3014 | 36700 | 0.9566        | -      | -                       |
| 2.3076 | 36800 | 0.7842        | -      | -                       |
| 2.3139 | 36900 | 0.6815        | -      | -                       |
| 2.3202 | 37000 | 0.78          | 0.8212 | -                       |
| 2.3265 | 37100 | 0.8306        | -      | -                       |
| 2.3327 | 37200 | 0.8073        | -      | -                       |
| 2.3390 | 37300 | 0.7565        | -      | -                       |
| 2.3453 | 37400 | 0.8478        | -      | -                       |
| 2.3515 | 37500 | 1.0159        | 0.8735 | -                       |
| 2.3578 | 37600 | 0.8126        | -      | -                       |
| 2.3641 | 37700 | 0.751         | -      | -                       |
| 2.3704 | 37800 | 0.7185        | -      | -                       |
| 2.3766 | 37900 | 0.7429        | -      | -                       |
| 2.3829 | 38000 | 0.7149        | 0.7997 | -                       |
| 2.3892 | 38100 | 0.6867        | -      | -                       |
| 2.3954 | 38200 | 0.608         | -      | -                       |
| 2.4017 | 38300 | 0.5687        | -      | -                       |
| 2.4080 | 38400 | 0.6623        | -      | -                       |
| 2.4142 | 38500 | 0.7751        | 0.7834 | -                       |
| 2.4205 | 38600 | 0.6537        | -      | -                       |
| 2.4268 | 38700 | 0.7121        | -      | -                       |
| 2.4331 | 38800 | 0.7864        | -      | -                       |
| 2.4393 | 38900 | 0.296         | -      | -                       |
| 2.4456 | 39000 | 0.4544        | 0.8051 | -                       |
| 2.4519 | 39100 | 0.4543        | -      | -                       |
| 2.4581 | 39200 | 0.9965        | -      | -                       |
| 2.4644 | 39300 | 0.4595        | -      | -                       |
| 2.4707 | 39400 | 0.7557        | -      | -                       |
| 2.4770 | 39500 | 0.6006        | 0.8437 | -                       |
| 2.4832 | 39600 | 0.695         | -      | -                       |
| 2.4895 | 39700 | 0.6292        | -      | -                       |
| 2.4958 | 39800 | 0.7392        | -      | -                       |
| 2.5020 | 39900 | 0.6547        | -      | -                       |
| 2.5083 | 40000 | 0.739         | 0.8443 | -                       |
| 2.5146 | 40100 | 0.5618        | -      | -                       |
| 2.5209 | 40200 | 0.861         | -      | -                       |
| 2.5271 | 40300 | 0.7318        | -      | -                       |
| 2.5334 | 40400 | 0.9021        | -      | -                       |
| 2.5397 | 40500 | 0.7329        | 0.8595 | -                       |
| 2.5459 | 40600 | 0.9691        | -      | -                       |
| 2.5522 | 40700 | 1.0524        | -      | -                       |
| 2.5585 | 40800 | 0.4546        | -      | -                       |
| 2.5647 | 40900 | 0.8917        | -      | -                       |
| 2.5710 | 41000 | 0.6644        | 0.8664 | -                       |
| 2.5773 | 41100 | 0.5167        | -      | -                       |
| 2.5836 | 41200 | 0.6499        | -      | -                       |
| 2.5898 | 41300 | 0.8096        | -      | -                       |
| 2.5961 | 41400 | 0.7269        | -      | -                       |
| 2.6024 | 41500 | 0.8561        | 0.8173 | -                       |
| 2.6086 | 41600 | 0.761         | -      | -                       |
| 2.6149 | 41700 | 1.0167        | -      | -                       |
| 2.6212 | 41800 | 0.763         | -      | -                       |
| 2.6275 | 41900 | 0.6659        | -      | -                       |
| 2.6337 | 42000 | 0.7299        | 0.8343 | -                       |
| 2.6400 | 42100 | 0.7045        | -      | -                       |
| 2.6463 | 42200 | 0.9054        | -      | -                       |
| 2.6525 | 42300 | 0.3002        | -      | -                       |
| 2.6588 | 42400 | 0.7728        | -      | -                       |
| 2.6651 | 42500 | 0.8214        | 0.8112 | -                       |
| 2.6713 | 42600 | 0.6762        | -      | -                       |
| 2.6776 | 42700 | 0.8863        | -      | -                       |
| 2.6839 | 42800 | 0.7438        | -      | -                       |
| 2.6902 | 42900 | 0.5968        | -      | -                       |
| 2.6964 | 43000 | 0.5292        | 0.7920 | -                       |
| 2.7027 | 43100 | 0.429         | -      | -                       |
| 2.7090 | 43200 | 0.6001        | -      | -                       |
| 2.7152 | 43300 | 0.7253        | -      | -                       |
| 2.7215 | 43400 | 0.9268        | -      | -                       |
| 2.7278 | 43500 | 0.9536        | 0.8434 | -                       |
| 2.7341 | 43600 | 0.6164        | -      | -                       |
| 2.7403 | 43700 | 0.8411        | -      | -                       |
| 2.7466 | 43800 | 1.0441        | -      | -                       |
| 2.7529 | 43900 | 0.6473        | -      | -                       |
| 2.7591 | 44000 | 0.8697        | 0.8089 | -                       |
| 2.7654 | 44100 | 0.7743        | -      | -                       |
| 2.7717 | 44200 | 0.9118        | -      | -                       |
| 2.7780 | 44300 | 0.7464        | -      | -                       |
| 2.7842 | 44400 | 0.7195        | -      | -                       |
| 2.7905 | 44500 | 0.9814        | 0.8122 | -                       |
| 2.7968 | 44600 | 0.5812        | -      | -                       |
| 2.8030 | 44700 | 0.5095        | -      | -                       |
| 2.8093 | 44800 | 0.7771        | -      | -                       |
| 2.8156 | 44900 | 0.6714        | -      | -                       |
| 2.8218 | 45000 | 0.5836        | 0.7786 | -                       |
| 2.8281 | 45100 | 1.0708        | -      | -                       |
| 2.8344 | 45200 | 0.576         | -      | -                       |
| 2.8407 | 45300 | 0.9657        | -      | -                       |
| 2.8469 | 45400 | 0.8103        | -      | -                       |
| 2.8532 | 45500 | 0.4644        | 0.7895 | -                       |
| 2.8595 | 45600 | 0.7485        | -      | -                       |
| 2.8657 | 45700 | 0.9843        | -      | -                       |
| 2.8720 | 45800 | 0.8462        | -      | -                       |
| 2.8783 | 45900 | 0.9025        | -      | -                       |
| 2.8846 | 46000 | 0.7014        | 0.8031 | -                       |
| 2.8908 | 46100 | 0.5638        | -      | -                       |
| 2.8971 | 46200 | 0.6016        | -      | -                       |
| 2.9034 | 46300 | 0.7257        | -      | -                       |
| 2.9096 | 46400 | 1.1182        | -      | -                       |
| 2.9159 | 46500 | 1.0352        | 0.8031 | -                       |
| 2.9222 | 46600 | 0.8413        | -      | -                       |
| 2.9285 | 46700 | 0.7341        | -      | -                       |
| 2.9347 | 46800 | 0.7115        | -      | -                       |
| 2.9410 | 46900 | 0.9124        | -      | -                       |
| 2.9473 | 47000 | 0.7988        | 0.7591 | -                       |
| 2.9535 | 47100 | 0.8373        | -      | -                       |
| 2.9598 | 47200 | 0.8587        | -      | -                       |
| 2.9661 | 47300 | 0.4961        | -      | -                       |
| 2.9723 | 47400 | 0.7349        | -      | -                       |
| 2.9786 | 47500 | 0.5285        | 0.7255 | -                       |
| 2.9849 | 47600 | 0.3715        | -      | -                       |
| 2.9912 | 47700 | 0.811         | -      | -                       |
| 2.9974 | 47800 | 0.6716        | -      | -                       |
| 3.0037 | 47900 | 0.4408        | -      | -                       |
| 3.0100 | 48000 | 0.7449        | 0.7503 | -                       |
| 3.0162 | 48100 | 0.4491        | -      | -                       |
| 3.0225 | 48200 | 0.5995        | -      | -                       |
| 3.0288 | 48300 | 0.6073        | -      | -                       |
| 3.0351 | 48400 | 0.5753        | -      | -                       |
| 3.0413 | 48500 | 0.6204        | 0.7650 | -                       |
| 3.0476 | 48600 | 0.9864        | -      | -                       |
| 3.0539 | 48700 | 0.6648        | -      | -                       |
| 3.0601 | 48800 | 0.4662        | -      | -                       |
| 3.0664 | 48900 | 0.5638        | -      | -                       |
| 3.0727 | 49000 | 0.6692        | 0.7381 | -                       |
| 3.0789 | 49100 | 0.6403        | -      | -                       |
| 3.0852 | 49200 | 0.5042        | -      | -                       |
| 3.0915 | 49300 | 0.4447        | -      | -                       |
| 3.0978 | 49400 | 0.5983        | -      | -                       |
| 3.1040 | 49500 | 0.6961        | 0.7289 | -                       |
| 3.1103 | 49600 | 0.8092        | -      | -                       |
| 3.1166 | 49700 | 0.4172        | -      | -                       |
| 3.1228 | 49800 | 0.6542        | -      | -                       |
| 3.1291 | 49900 | 0.8016        | -      | -                       |
| 3.1354 | 50000 | 0.3927        | 0.7370 | -                       |
| 3.1417 | 50100 | 0.4724        | -      | -                       |
| 3.1479 | 50200 | 0.46          | -      | -                       |
| 3.1542 | 50300 | 0.4258        | -      | -                       |
| 3.1605 | 50400 | 0.5053        | -      | -                       |
| 3.1667 | 50500 | 0.3406        | 0.7210 | -                       |
| 3.1730 | 50600 | 0.6276        | -      | -                       |
| 3.1793 | 50700 | 0.5913        | -      | -                       |
| 3.1856 | 50800 | 0.3902        | -      | -                       |
| 3.1918 | 50900 | 0.5063        | -      | -                       |
| 3.1981 | 51000 | 0.7909        | 0.7442 | -                       |
| 3.2044 | 51100 | 0.5071        | -      | -                       |
| 3.2106 | 51200 | 0.5611        | -      | -                       |
| 3.2169 | 51300 | 0.545         | -      | -                       |
| 3.2232 | 51400 | 0.4359        | -      | -                       |
| 3.2294 | 51500 | 0.5249        | 0.7148 | -                       |
| 3.2357 | 51600 | 0.6759        | -      | -                       |
| 3.2420 | 51700 | 0.5458        | -      | -                       |
| 3.2483 | 51800 | 0.5195        | -      | -                       |
| 3.2545 | 51900 | 0.292         | -      | -                       |
| 3.2608 | 52000 | 0.4826        | 0.7129 | -                       |
| 3.2671 | 52100 | 0.2496        | -      | -                       |
| 3.2733 | 52200 | 0.6702        | -      | -                       |
| 3.2796 | 52300 | 0.3192        | -      | -                       |
| 3.2859 | 52400 | 0.66          | -      | -                       |
| 3.2922 | 52500 | 0.6472        | 0.7146 | -                       |
| 3.2984 | 52600 | 0.4482        | -      | -                       |
| 3.3047 | 52700 | 0.6618        | -      | -                       |
| 3.3110 | 52800 | 0.4424        | -      | -                       |
| 3.3172 | 52900 | 0.6157        | -      | -                       |
| 3.3235 | 53000 | 0.5087        | 0.7036 | -                       |
| 3.3298 | 53100 | 0.5148        | -      | -                       |
| 3.3361 | 53200 | 0.386         | -      | -                       |
| 3.3423 | 53300 | 0.3552        | -      | -                       |
| 3.3486 | 53400 | 0.5609        | -      | -                       |
| 3.3549 | 53500 | 0.3549        | 0.7148 | -                       |
| 3.3611 | 53600 | 0.3099        | -      | -                       |
| 3.3674 | 53700 | 0.2903        | -      | -                       |
| 3.3737 | 53800 | 0.7385        | -      | -                       |
| 3.3799 | 53900 | 0.7025        | -      | -                       |
| 3.3862 | 54000 | 0.5625        | 0.7014 | -                       |
| 3.3925 | 54100 | 0.7545        | -      | -                       |
| 3.3988 | 54200 | 0.4371        | -      | -                       |
| 3.4050 | 54300 | 0.4588        | -      | -                       |
| 3.4113 | 54400 | 0.4973        | -      | -                       |
| 3.4176 | 54500 | 0.4534        | 0.7010 | -                       |
| 3.4238 | 54600 | 0.6761        | -      | -                       |
| 3.4301 | 54700 | 0.6559        | -      | -                       |
| 3.4364 | 54800 | 0.6087        | -      | -                       |
| 3.4427 | 54900 | 0.601         | -      | -                       |
| 3.4489 | 55000 | 0.4894        | 0.6706 | -                       |
| 3.4552 | 55100 | 0.6524        | -      | -                       |
| 3.4615 | 55200 | 0.8268        | -      | -                       |
| 3.4677 | 55300 | 0.1795        | -      | -                       |
| 3.4740 | 55400 | 0.5667        | -      | -                       |
| 3.4803 | 55500 | 0.4185        | 0.6823 | -                       |
| 3.4865 | 55600 | 0.615         | -      | -                       |
| 3.4928 | 55700 | 0.6231        | -      | -                       |
| 3.4991 | 55800 | 0.3809        | -      | -                       |
| 3.5054 | 55900 | 0.6747        | -      | -                       |
| 3.5116 | 56000 | 0.6484        | 0.6736 | -                       |
| 3.5179 | 56100 | 0.6208        | -      | -                       |
| 3.5242 | 56200 | 0.2345        | -      | -                       |
| 3.5304 | 56300 | 0.4494        | -      | -                       |
| 3.5367 | 56400 | 0.327         | -      | -                       |
| 3.5430 | 56500 | 0.5614        | 0.6762 | -                       |
| 3.5493 | 56600 | 0.8796        | -      | -                       |
| 3.5555 | 56700 | 0.6068        | -      | -                       |
| 3.5618 | 56800 | 0.4918        | -      | -                       |
| 3.5681 | 56900 | 0.7352        | -      | -                       |
| 3.5743 | 57000 | 0.4149        | 0.6881 | -                       |
| 3.5806 | 57100 | 0.3746        | -      | -                       |
| 3.5869 | 57200 | 0.7055        | -      | -                       |
| 3.5932 | 57300 | 0.5557        | -      | -                       |
| 3.5994 | 57400 | 0.7734        | -      | -                       |
| 3.6057 | 57500 | 0.5263        | 0.6800 | -                       |
| 3.6120 | 57600 | 0.4527        | -      | -                       |
| 3.6182 | 57700 | 0.8339        | -      | -                       |
| 3.6245 | 57800 | 0.7004        | -      | -                       |
| 3.6308 | 57900 | 0.5068        | -      | -                       |
| 3.6370 | 58000 | 0.6601        | 0.6667 | -                       |
| 3.6433 | 58100 | 0.8452        | -      | -                       |
| 3.6496 | 58200 | 0.2345        | -      | -                       |
| 3.6559 | 58300 | 0.6034        | -      | -                       |
| 3.6621 | 58400 | 0.8962        | -      | -                       |
| 3.6684 | 58500 | 0.5844        | 0.6755 | -                       |
| 3.6747 | 58600 | 0.6827        | -      | -                       |
| 3.6809 | 58700 | 0.4087        | -      | -                       |
| 3.6872 | 58800 | 0.6221        | -      | -                       |
| 3.6935 | 58900 | 0.777         | -      | -                       |
| 3.6998 | 59000 | 0.572         | 0.6737 | -                       |
| 3.7060 | 59100 | 0.5479        | -      | -                       |
| 3.7123 | 59200 | 0.5078        | -      | -                       |
| 3.7186 | 59300 | 0.6982        | -      | -                       |
| 3.7248 | 59400 | 0.2223        | -      | -                       |
| 3.7311 | 59500 | 0.5361        | 0.6709 | -                       |
| 3.7374 | 59600 | 0.6072        | -      | -                       |
| 3.7437 | 59700 | 0.35          | -      | -                       |
| 3.7499 | 59800 | 0.8802        | -      | -                       |
| 3.7562 | 59900 | 0.6216        | -      | -                       |
| 3.7625 | 60000 | 0.2514        | 0.6836 | -                       |
| 3.7687 | 60100 | 0.6285        | -      | -                       |
| 3.7750 | 60200 | 0.9845        | -      | -                       |
| 3.7813 | 60300 | 0.5355        | -      | -                       |
| 3.7875 | 60400 | 0.495         | -      | -                       |
| 3.7938 | 60500 | 0.6905        | 0.6725 | -                       |
| 3.8001 | 60600 | 0.563         | -      | -                       |
| 3.8064 | 60700 | 0.6067        | -      | -                       |
| 3.8126 | 60800 | 0.7585        | -      | -                       |
| 3.8189 | 60900 | 0.4283        | -      | -                       |
| 3.8252 | 61000 | 0.4758        | 0.6600 | -                       |
| 3.8314 | 61100 | 0.5462        | -      | -                       |
| 3.8377 | 61200 | 0.649         | -      | -                       |
| 3.8440 | 61300 | 0.5576        | -      | -                       |
| 3.8503 | 61400 | 0.6717        | -      | -                       |
| 3.8565 | 61500 | 0.2951        | 0.6613 | -                       |
| 3.8628 | 61600 | 0.457         | -      | -                       |
| 3.8691 | 61700 | 0.473         | -      | -                       |
| 3.8753 | 61800 | 0.5181        | -      | -                       |
| 3.8816 | 61900 | 0.4581        | -      | -                       |
| 3.8879 | 62000 | 0.6875        | 0.6669 | -                       |
| 3.8941 | 62100 | 0.3821        | -      | -                       |
| 3.9004 | 62200 | 0.5039        | -      | -                       |
| 3.9067 | 62300 | 0.6809        | -      | -                       |
| 3.9130 | 62400 | 0.3591        | -      | -                       |
| 3.9192 | 62500 | 0.6695        | 0.6654 | -                       |
| 3.9255 | 62600 | 0.5352        | -      | -                       |
| 3.9318 | 62700 | 0.8635        | -      | -                       |
| 3.9380 | 62800 | 0.73          | -      | -                       |
| 3.9443 | 62900 | 0.4138        | -      | -                       |
| 3.9506 | 63000 | 0.3704        | 0.6620 | -                       |
| 3.9569 | 63100 | 0.4831        | -      | -                       |
| 3.9631 | 63200 | 0.5405        | -      | -                       |
| 3.9694 | 63300 | 0.6123        | -      | -                       |
| 3.9757 | 63400 | 0.5167        | -      | -                       |
| 3.9819 | 63500 | 0.6967        | 0.6613 | -                       |
| 3.9882 | 63600 | 0.338         | -      | -                       |
| 3.9945 | 63700 | 0.515         | -      | -                       |

</details>

### Framework Versions
- Python: 3.8.10
- Sentence Transformers: 3.1.1
- Transformers: 4.45.1
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.20.0

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}
```

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