sentence-transformers safetensors roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:5854451 loss:MaskedCachedMultipleNegativesRankingLoss arxiv:1908.10084 arxiv:2101.06983 base_model:sentence-transformers/all-distilroberta-v1 base_model:finetune:sentence-transformers/all-distilroberta-v1 autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/all-distilroberta-v1
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5854451
- loss:MaskedCachedMultipleNegativesRankingLoss
widget:
- source_sentence: Mathlib.Data.Finset.Option#52
  sentences:
  - neg_inj
  - CategoryTheory.Limits.HasCokernels.has_colimit
  - Finset.mem_image
- source_sentence: Mathlib.Analysis.Calculus.FDeriv.Mul#68
  sentences:
  - eq_of_heq
  - Option.some.injEq
  - Fin.le_last
- source_sentence: Mathlib.Data.Finset.Option#52
  sentences:
  - Set.biInter_and'
  - Int.natCast_dvd_natCast
  - Finset.mem_erase
- source_sentence: Mathlib.Algebra.GCDMonoid.Finset#74
  sentences:
  - gcd_zero_left
  - HasFDerivWithinAt.uniqueDiffWithinAt
  - Polynomial.Monic.map
- source_sentence: Mathlib.Algebra.Polynomial.HasseDeriv#31
  sentences:
  - Set.mem_inter_iff
  - Polynomial.hasseDeriv_coeff
  - HomologicalComplex.isZero_X_of_isStrictlySupported
---

# SentenceTransformer based on sentence-transformers/all-distilroberta-v1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1). It maps sentences & paragraphs to a 768-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-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1) <!-- at revision 8d88b92a34345fd6a139aa47768c9881720006ce -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, '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("hanwenzhu/all-distilroberta-v1-lr2e-4-bs1024-nneg3-mlbs-mar03")
# Run inference
sentences = [
    'Mathlib.Algebra.Polynomial.HasseDeriv#31',
    'Polynomial.hasseDeriv_coeff',
    'HomologicalComplex.isZero_X_of_isStrictlySupported',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 5,854,451 training samples
* Columns: <code>state_name</code> and <code>premise_name</code>
* Approximate statistics based on the first 1000 samples:
  |         | state_name                                                                         | premise_name                                                                      |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            |
  | details | <ul><li>min: 10 tokens</li><li>mean: 17.28 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 11.34 tokens</li><li>max: 38 tokens</li></ul> |
* Samples:
  | state_name                                            | premise_name                                         |
  |:------------------------------------------------------|:-----------------------------------------------------|
  | <code>Mathlib.RingTheory.Ideal.Norm.RelNorm#46</code> | <code>RingHomCompTriple.ids</code>                   |
  | <code>Mathlib.RingTheory.Ideal.Norm.RelNorm#46</code> | <code>MonoidWithZeroHomClass.toMonoidHomClass</code> |
  | <code>Mathlib.RingTheory.Ideal.Norm.RelNorm#46</code> | <code>Ideal.subset_span</code>                       |
* Loss: <code>loss.MaskedCachedMultipleNegativesRankingLoss</code> with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 1,959 evaluation samples
* Columns: <code>state_name</code> and <code>premise_name</code>
* Approximate statistics based on the first 1000 samples:
  |         | state_name                                                                         | premise_name                                                                      |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            |
  | details | <ul><li>min: 10 tokens</li><li>mean: 17.08 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.05 tokens</li><li>max: 31 tokens</li></ul> |
* Samples:
  | state_name                                                   | premise_name                                              |
  |:-------------------------------------------------------------|:----------------------------------------------------------|
  | <code>Mathlib.Algebra.Algebra.Hom#80</code>                  | <code>AlgHom.commutes</code>                              |
  | <code>Mathlib.Algebra.Algebra.NonUnitalSubalgebra#237</code> | <code>NonUnitalAlgHom.instNonUnitalAlgSemiHomClass</code> |
  | <code>Mathlib.Algebra.Algebra.NonUnitalSubalgebra#237</code> | <code>NonUnitalAlgebra.mem_top</code>                     |
* Loss: <code>loss.MaskedCachedMultipleNegativesRankingLoss</code> with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 1024
- `per_device_eval_batch_size`: 64
- `learning_rate`: 0.0002
- `num_train_epochs`: 1.0
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.03
- `bf16`: True
- `dataloader_num_workers`: 4

#### 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`: 1024
- `per_device_eval_batch_size`: 64
- `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`: 0.0002
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1.0
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.03
- `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`: 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`: False
- `dataloader_num_workers`: 4
- `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   |
|:------:|:----:|:-------------:|:------:|
| 0.0017 | 10   | 7.5842        | -      |
| 0.0035 | 20   | 6.4567        | -      |
| 0.0052 | 30   | 5.9408        | -      |
| 0.0070 | 40   | 5.7176        | -      |
| 0.0087 | 50   | 5.5353        | -      |
| 0.0101 | 58   | -             | 2.2337 |
| 0.0105 | 60   | 5.4044        | -      |
| 0.0122 | 70   | 5.3384        | -      |
| 0.0140 | 80   | 5.2395        | -      |
| 0.0157 | 90   | 5.1291        | -      |
| 0.0175 | 100  | 5.1093        | -      |
| 0.0192 | 110  | 5.0695        | -      |
| 0.0203 | 116  | -             | 1.9949 |
| 0.0210 | 120  | 4.9664        | -      |
| 0.0227 | 130  | 4.973         | -      |
| 0.0245 | 140  | 4.9065        | -      |
| 0.0262 | 150  | 4.8961        | -      |
| 0.0280 | 160  | 4.839         | -      |
| 0.0297 | 170  | 4.8513        | -      |
| 0.0304 | 174  | -             | 1.9119 |
| 0.0315 | 180  | 4.7662        | -      |
| 0.0332 | 190  | 4.7385        | -      |
| 0.0350 | 200  | 4.7036        | -      |
| 0.0367 | 210  | 4.7013        | -      |
| 0.0385 | 220  | 4.6837        | -      |
| 0.0402 | 230  | 4.6325        | -      |
| 0.0406 | 232  | -             | 1.7502 |
| 0.0420 | 240  | 4.5982        | -      |
| 0.0437 | 250  | 4.5526        | -      |
| 0.0455 | 260  | 4.5793        | -      |
| 0.0472 | 270  | 4.545         | -      |
| 0.0490 | 280  | 4.5173        | -      |
| 0.0507 | 290  | 4.4595        | 1.6955 |
| 0.0525 | 300  | 4.4772        | -      |
| 0.0542 | 310  | 4.4038        | -      |
| 0.0560 | 320  | 4.4132        | -      |
| 0.0577 | 330  | 4.4139        | -      |
| 0.0595 | 340  | 4.3585        | -      |
| 0.0609 | 348  | -             | 1.6316 |
| 0.0612 | 350  | 4.3314        | -      |
| 0.0630 | 360  | 4.3805        | -      |
| 0.0647 | 370  | 4.2791        | -      |
| 0.0665 | 380  | 4.2938        | -      |
| 0.0682 | 390  | 4.2591        | -      |
| 0.0700 | 400  | 4.262         | -      |
| 0.0710 | 406  | -             | 1.5723 |
| 0.0717 | 410  | 4.2108        | -      |
| 0.0735 | 420  | 4.1723        | -      |
| 0.0752 | 430  | 4.157         | -      |
| 0.0769 | 440  | 4.1878        | -      |
| 0.0787 | 450  | 4.1644        | -      |
| 0.0804 | 460  | 4.1569        | -      |
| 0.0811 | 464  | -             | 1.5368 |
| 0.0822 | 470  | 4.139         | -      |
| 0.0839 | 480  | 4.0872        | -      |
| 0.0857 | 490  | 4.1169        | -      |
| 0.0874 | 500  | 4.062         | -      |
| 0.0892 | 510  | 4.1138        | -      |
| 0.0909 | 520  | 4.1088        | -      |
| 0.0913 | 522  | -             | 1.5232 |
| 0.0927 | 530  | 4.0526        | -      |
| 0.0944 | 540  | 4.0355        | -      |
| 0.0962 | 550  | 3.9937        | -      |
| 0.0979 | 560  | 3.9647        | -      |
| 0.0997 | 570  | 3.9715        | -      |
| 0.1014 | 580  | 3.9524        | 1.4901 |
| 0.1032 | 590  | 3.945         | -      |
| 0.1049 | 600  | 3.9615        | -      |
| 0.1067 | 610  | 3.9713        | -      |
| 0.1084 | 620  | 3.9264        | -      |
| 0.1102 | 630  | 3.9036        | -      |
| 0.1116 | 638  | -             | 1.4411 |
| 0.1119 | 640  | 3.8909        | -      |
| 0.1137 | 650  | 3.901         | -      |
| 0.1154 | 660  | 3.879         | -      |
| 0.1172 | 670  | 3.8696        | -      |
| 0.1189 | 680  | 3.8678        | -      |
| 0.1207 | 690  | 3.8472        | -      |
| 0.1217 | 696  | -             | 1.4459 |
| 0.1224 | 700  | 3.8277        | -      |
| 0.1242 | 710  | 3.8321        | -      |
| 0.1259 | 720  | 3.812         | -      |
| 0.1277 | 730  | 3.8386        | -      |
| 0.1294 | 740  | 3.7583        | -      |
| 0.1312 | 750  | 3.8007        | -      |
| 0.1319 | 754  | -             | 1.3644 |
| 0.1329 | 760  | 3.7337        | -      |
| 0.1347 | 770  | 3.7554        | -      |
| 0.1364 | 780  | 3.7518        | -      |
| 0.1382 | 790  | 3.6993        | -      |
| 0.1399 | 800  | 3.7477        | -      |
| 0.1417 | 810  | 3.6979        | -      |
| 0.1420 | 812  | -             | 1.3702 |
| 0.1434 | 820  | 3.6651        | -      |
| 0.1452 | 830  | 3.7292        | -      |
| 0.1469 | 840  | 3.7005        | -      |
| 0.1487 | 850  | 3.6856        | -      |
| 0.1504 | 860  | 3.631         | -      |
| 0.1522 | 870  | 3.6459        | 1.3568 |
| 0.1539 | 880  | 3.6089        | -      |
| 0.1556 | 890  | 3.6134        | -      |
| 0.1574 | 900  | 3.6058        | -      |
| 0.1591 | 910  | 3.6193        | -      |
| 0.1609 | 920  | 3.627         | -      |
| 0.1623 | 928  | -             | 1.3072 |
| 0.1626 | 930  | 3.6202        | -      |
| 0.1644 | 940  | 3.5891        | -      |
| 0.1661 | 950  | 3.6185        | -      |
| 0.1679 | 960  | 3.5984        | -      |
| 0.1696 | 970  | 3.6258        | -      |
| 0.1714 | 980  | 3.5625        | -      |
| 0.1724 | 986  | -             | 1.2930 |
| 0.1731 | 990  | 3.5441        | -      |
| 0.1749 | 1000 | 3.5571        | -      |
| 0.1766 | 1010 | 3.5486        | -      |
| 0.1784 | 1020 | 3.5382        | -      |
| 0.1801 | 1030 | 3.4519        | -      |
| 0.1819 | 1040 | 3.5072        | -      |
| 0.1826 | 1044 | -             | 1.2823 |
| 0.1836 | 1050 | 3.5042        | -      |
| 0.1854 | 1060 | 3.5005        | -      |
| 0.1871 | 1070 | 3.455         | -      |
| 0.1889 | 1080 | 3.4727        | -      |
| 0.1906 | 1090 | 3.4473        | -      |
| 0.1924 | 1100 | 3.4296        | -      |
| 0.1927 | 1102 | -             | 1.2696 |
| 0.1941 | 1110 | 3.449         | -      |
| 0.1959 | 1120 | 3.4202        | -      |
| 0.1976 | 1130 | 3.4236        | -      |
| 0.1994 | 1140 | 3.414         | -      |
| 0.2011 | 1150 | 3.4264        | -      |
| 0.2029 | 1160 | 3.4005        | 1.2602 |
| 0.2046 | 1170 | 3.3801        | -      |
| 0.2064 | 1180 | 3.3543        | -      |
| 0.2081 | 1190 | 3.3866        | -      |
| 0.2099 | 1200 | 3.3831        | -      |
| 0.2116 | 1210 | 3.3691        | -      |
| 0.2130 | 1218 | -             | 1.2130 |
| 0.2134 | 1220 | 3.3607        | -      |
| 0.2151 | 1230 | 3.3659        | -      |
| 0.2169 | 1240 | 3.3538        | -      |
| 0.2186 | 1250 | 3.3336        | -      |
| 0.2204 | 1260 | 3.3403        | -      |
| 0.2221 | 1270 | 3.3062        | -      |
| 0.2232 | 1276 | -             | 1.2237 |
| 0.2239 | 1280 | 3.3251        | -      |
| 0.2256 | 1290 | 3.3475        | -      |
| 0.2274 | 1300 | 3.2729        | -      |
| 0.2291 | 1310 | 3.2872        | -      |
| 0.2308 | 1320 | 3.2778        | -      |
| 0.2326 | 1330 | 3.3147        | -      |
| 0.2333 | 1334 | -             | 1.2061 |
| 0.2343 | 1340 | 3.2477        | -      |
| 0.2361 | 1350 | 3.2871        | -      |
| 0.2378 | 1360 | 3.2458        | -      |
| 0.2396 | 1370 | 3.279         | -      |
| 0.2413 | 1380 | 3.2546        | -      |
| 0.2431 | 1390 | 3.2342        | -      |
| 0.2434 | 1392 | -             | 1.1854 |
| 0.2448 | 1400 | 3.2488        | -      |
| 0.2466 | 1410 | 3.2489        | -      |
| 0.2483 | 1420 | 3.2368        | -      |
| 0.2501 | 1430 | 3.2517        | -      |
| 0.2518 | 1440 | 3.2568        | -      |
| 0.2536 | 1450 | 3.21          | 1.1616 |
| 0.2553 | 1460 | 3.1891        | -      |
| 0.2571 | 1470 | 3.1739        | -      |
| 0.2588 | 1480 | 3.2004        | -      |
| 0.2606 | 1490 | 3.1988        | -      |
| 0.2623 | 1500 | 3.1892        | -      |
| 0.2637 | 1508 | -             | 1.1306 |
| 0.2641 | 1510 | 3.1967        | -      |
| 0.2658 | 1520 | 3.1331        | -      |
| 0.2676 | 1530 | 3.155         | -      |
| 0.2693 | 1540 | 3.1564        | -      |
| 0.2711 | 1550 | 3.1912        | -      |
| 0.2728 | 1560 | 3.1005        | -      |
| 0.2739 | 1566 | -             | 1.1026 |
| 0.2746 | 1570 | 3.1166        | -      |
| 0.2763 | 1580 | 3.1453        | -      |
| 0.2781 | 1590 | 3.116         | -      |
| 0.2798 | 1600 | 3.1465        | -      |
| 0.2816 | 1610 | 3.1325        | -      |
| 0.2833 | 1620 | 3.1022        | -      |
| 0.2840 | 1624 | -             | 1.1400 |
| 0.2851 | 1630 | 3.0703        | -      |
| 0.2868 | 1640 | 3.0999        | -      |
| 0.2886 | 1650 | 3.0957        | -      |
| 0.2903 | 1660 | 3.0886        | -      |
| 0.2921 | 1670 | 3.0471        | -      |
| 0.2938 | 1680 | 3.0845        | -      |
| 0.2942 | 1682 | -             | 1.1045 |
| 0.2956 | 1690 | 3.0513        | -      |
| 0.2973 | 1700 | 3.0621        | -      |
| 0.2991 | 1710 | 3.0473        | -      |
| 0.3008 | 1720 | 3.0486        | -      |
| 0.3026 | 1730 | 3.0189        | -      |
| 0.3043 | 1740 | 3.0675        | 1.1004 |
| 0.3061 | 1750 | 3.0592        | -      |
| 0.3078 | 1760 | 3.0663        | -      |
| 0.3095 | 1770 | 3.0879        | -      |
| 0.3113 | 1780 | 3.0167        | -      |
| 0.3130 | 1790 | 3.0356        | -      |
| 0.3144 | 1798 | -             | 1.0554 |
| 0.3148 | 1800 | 3.0294        | -      |
| 0.3165 | 1810 | 2.9956        | -      |
| 0.3183 | 1820 | 2.985         | -      |
| 0.3200 | 1830 | 2.9824        | -      |
| 0.3218 | 1840 | 2.9939        | -      |
| 0.3235 | 1850 | 2.9979        | -      |
| 0.3246 | 1856 | -             | 1.0561 |
| 0.3253 | 1860 | 2.9935        | -      |
| 0.3270 | 1870 | 3.0613        | -      |
| 0.3288 | 1880 | 2.9742        | -      |
| 0.3305 | 1890 | 2.9858        | -      |
| 0.3323 | 1900 | 2.9446        | -      |
| 0.3340 | 1910 | 2.9571        | -      |
| 0.3347 | 1914 | -             | 1.0333 |
| 0.3358 | 1920 | 2.9839        | -      |
| 0.3375 | 1930 | 2.9865        | -      |
| 0.3393 | 1940 | 2.9398        | -      |
| 0.3410 | 1950 | 2.9504        | -      |
| 0.3428 | 1960 | 2.9371        | -      |
| 0.3445 | 1970 | 2.9222        | -      |
| 0.3449 | 1972 | -             | 1.0322 |
| 0.3463 | 1980 | 2.8907        | -      |
| 0.3480 | 1990 | 2.9412        | -      |
| 0.3498 | 2000 | 2.944         | -      |
| 0.3515 | 2010 | 2.9168        | -      |
| 0.3533 | 2020 | 2.9076        | -      |
| 0.3550 | 2030 | 2.8967        | 1.0103 |
| 0.3568 | 2040 | 2.8569        | -      |
| 0.3585 | 2050 | 2.8602        | -      |
| 0.3603 | 2060 | 2.8984        | -      |
| 0.3620 | 2070 | 2.8782        | -      |
| 0.3638 | 2080 | 2.8649        | -      |
| 0.3652 | 2088 | -             | 1.0136 |
| 0.3655 | 2090 | 2.8388        | -      |
| 0.3673 | 2100 | 2.8845        | -      |
| 0.3690 | 2110 | 2.8749        | -      |
| 0.3708 | 2120 | 2.8439        | -      |
| 0.3725 | 2130 | 2.8693        | -      |
| 0.3743 | 2140 | 2.8342        | -      |
| 0.3753 | 2146 | -             | 0.9949 |
| 0.3760 | 2150 | 2.8696        | -      |
| 0.3778 | 2160 | 2.872         | -      |
| 0.3795 | 2170 | 2.828         | -      |
| 0.3813 | 2180 | 2.8338        | -      |
| 0.3830 | 2190 | 2.8716        | -      |
| 0.3847 | 2200 | 2.8798        | -      |
| 0.3854 | 2204 | -             | 1.0067 |
| 0.3865 | 2210 | 2.834         | -      |
| 0.3882 | 2220 | 2.7885        | -      |
| 0.3900 | 2230 | 2.8152        | -      |
| 0.3917 | 2240 | 2.8214        | -      |
| 0.3935 | 2250 | 2.8306        | -      |
| 0.3952 | 2260 | 2.8164        | -      |
| 0.3956 | 2262 | -             | 0.9845 |
| 0.3970 | 2270 | 2.8338        | -      |
| 0.3987 | 2280 | 2.8223        | -      |
| 0.4005 | 2290 | 2.8183        | -      |
| 0.4022 | 2300 | 2.7903        | -      |
| 0.4040 | 2310 | 2.7772        | -      |
| 0.4057 | 2320 | 2.7952        | 0.9900 |
| 0.4075 | 2330 | 2.7733        | -      |
| 0.4092 | 2340 | 2.8096        | -      |
| 0.4110 | 2350 | 2.771         | -      |
| 0.4127 | 2360 | 2.8178        | -      |
| 0.4145 | 2370 | 2.7539        | -      |
| 0.4159 | 2378 | -             | 0.9749 |
| 0.4162 | 2380 | 2.7488        | -      |
| 0.4180 | 2390 | 2.7592        | -      |
| 0.4197 | 2400 | 2.7385        | -      |
| 0.4215 | 2410 | 2.7564        | -      |
| 0.4232 | 2420 | 2.7573        | -      |
| 0.4250 | 2430 | 2.7686        | -      |
| 0.4260 | 2436 | -             | 0.9509 |
| 0.4267 | 2440 | 2.7147        | -      |
| 0.4285 | 2450 | 2.7375        | -      |
| 0.4302 | 2460 | 2.6995        | -      |
| 0.4320 | 2470 | 2.6888        | -      |
| 0.4337 | 2480 | 2.7171        | -      |
| 0.4355 | 2490 | 2.712         | -      |
| 0.4362 | 2494 | -             | 0.9311 |
| 0.4372 | 2500 | 2.729         | -      |
| 0.4390 | 2510 | 2.6974        | -      |
| 0.4407 | 2520 | 2.7056        | -      |
| 0.4425 | 2530 | 2.7123        | -      |
| 0.4442 | 2540 | 2.701         | -      |
| 0.4460 | 2550 | 2.7211        | -      |
| 0.4463 | 2552 | -             | 0.9259 |
| 0.4477 | 2560 | 2.6974        | -      |
| 0.4495 | 2570 | 2.6823        | -      |
| 0.4512 | 2580 | 2.6968        | -      |
| 0.4530 | 2590 | 2.7126        | -      |
| 0.4547 | 2600 | 2.693         | -      |
| 0.4565 | 2610 | 2.7164        | 0.9161 |
| 0.4582 | 2620 | 2.6558        | -      |
| 0.4600 | 2630 | 2.6972        | -      |
| 0.4617 | 2640 | 2.7116        | -      |
| 0.4634 | 2650 | 2.6398        | -      |
| 0.4652 | 2660 | 2.6645        | -      |
| 0.4666 | 2668 | -             | 0.8982 |
| 0.4669 | 2670 | 2.6646        | -      |
| 0.4687 | 2680 | 2.6828        | -      |
| 0.4704 | 2690 | 2.6502        | -      |
| 0.4722 | 2700 | 2.6605        | -      |
| 0.4739 | 2710 | 2.6224        | -      |
| 0.4757 | 2720 | 2.6753        | -      |
| 0.4767 | 2726 | -             | 0.8941 |
| 0.4774 | 2730 | 2.6478        | -      |
| 0.4792 | 2740 | 2.6688        | -      |
| 0.4809 | 2750 | 2.6674        | -      |
| 0.4827 | 2760 | 2.6132        | -      |
| 0.4844 | 2770 | 2.6286        | -      |
| 0.4862 | 2780 | 2.634         | -      |
| 0.4869 | 2784 | -             | 0.8756 |
| 0.4879 | 2790 | 2.6359        | -      |
| 0.4897 | 2800 | 2.6242        | -      |
| 0.4914 | 2810 | 2.6443        | -      |
| 0.4932 | 2820 | 2.59          | -      |
| 0.4949 | 2830 | 2.6166        | -      |
| 0.4967 | 2840 | 2.6249        | -      |
| 0.4970 | 2842 | -             | 0.8802 |
| 0.4984 | 2850 | 2.6257        | -      |
| 0.5002 | 2860 | 2.6286        | -      |
| 0.5019 | 2870 | 2.5671        | -      |
| 0.5037 | 2880 | 2.5959        | -      |
| 0.5054 | 2890 | 2.5962        | -      |
| 0.5072 | 2900 | 2.5521        | 0.8673 |
| 0.5089 | 2910 | 2.5833        | -      |
| 0.5107 | 2920 | 2.6015        | -      |
| 0.5124 | 2930 | 2.6446        | -      |
| 0.5142 | 2940 | 2.5655        | -      |
| 0.5159 | 2950 | 2.5802        | -      |
| 0.5173 | 2958 | -             | 0.8614 |
| 0.5177 | 2960 | 2.6124        | -      |
| 0.5194 | 2970 | 2.5372        | -      |
| 0.5212 | 2980 | 2.5108        | -      |
| 0.5229 | 2990 | 2.578         | -      |
| 0.5247 | 3000 | 2.5629        | -      |
| 0.5264 | 3010 | 2.5691        | -      |
| 0.5275 | 3016 | -             | 0.8418 |
| 0.5282 | 3020 | 2.5313        | -      |
| 0.5299 | 3030 | 2.5791        | -      |
| 0.5317 | 3040 | 2.5216        | -      |
| 0.5334 | 3050 | 2.5263        | -      |
| 0.5352 | 3060 | 2.5213        | -      |
| 0.5369 | 3070 | 2.5485        | -      |
| 0.5376 | 3074 | -             | 0.8546 |
| 0.5386 | 3080 | 2.5435        | -      |
| 0.5404 | 3090 | 2.5599        | -      |
| 0.5421 | 3100 | 2.5045        | -      |
| 0.5439 | 3110 | 2.5055        | -      |
| 0.5456 | 3120 | 2.54          | -      |
| 0.5474 | 3130 | 2.5134        | -      |
| 0.5477 | 3132 | -             | 0.8515 |
| 0.5491 | 3140 | 2.5053        | -      |
| 0.5509 | 3150 | 2.4578        | -      |
| 0.5526 | 3160 | 2.517         | -      |
| 0.5544 | 3170 | 2.5061        | -      |
| 0.5561 | 3180 | 2.5262        | -      |
| 0.5579 | 3190 | 2.5787        | 0.8376 |
| 0.5596 | 3200 | 2.4855        | -      |
| 0.5614 | 3210 | 2.5058        | -      |
| 0.5631 | 3220 | 2.5279        | -      |
| 0.5649 | 3230 | 2.498         | -      |
| 0.5666 | 3240 | 2.5045        | -      |
| 0.5680 | 3248 | -             | 0.8407 |
| 0.5684 | 3250 | 2.5129        | -      |
| 0.5701 | 3260 | 2.517         | -      |
| 0.5719 | 3270 | 2.4647        | -      |
| 0.5736 | 3280 | 2.4642        | -      |
| 0.5754 | 3290 | 2.4936        | -      |
| 0.5771 | 3300 | 2.4862        | -      |
| 0.5782 | 3306 | -             | 0.8310 |
| 0.5789 | 3310 | 2.4805        | -      |
| 0.5806 | 3320 | 2.4986        | -      |
| 0.5824 | 3330 | 2.481         | -      |
| 0.5841 | 3340 | 2.4747        | -      |
| 0.5859 | 3350 | 2.4939        | -      |
| 0.5876 | 3360 | 2.4691        | -      |
| 0.5883 | 3364 | -             | 0.8397 |
| 0.5894 | 3370 | 2.4798        | -      |
| 0.5911 | 3380 | 2.4439        | -      |
| 0.5929 | 3390 | 2.4849        | -      |
| 0.5946 | 3400 | 2.4653        | -      |
| 0.5964 | 3410 | 2.4795        | -      |
| 0.5981 | 3420 | 2.4681        | -      |
| 0.5985 | 3422 | -             | 0.8265 |
| 0.5999 | 3430 | 2.4671        | -      |
| 0.6016 | 3440 | 2.4579        | -      |
| 0.6034 | 3450 | 2.4319        | -      |
| 0.6051 | 3460 | 2.4235        | -      |
| 0.6069 | 3470 | 2.4447        | -      |
| 0.6086 | 3480 | 2.456         | 0.8104 |
| 0.6104 | 3490 | 2.4107        | -      |
| 0.6121 | 3500 | 2.49          | -      |
| 0.6139 | 3510 | 2.4511        | -      |
| 0.6156 | 3520 | 2.4446        | -      |
| 0.6173 | 3530 | 2.4159        | -      |
| 0.6187 | 3538 | -             | 0.8086 |
| 0.6191 | 3540 | 2.4135        | -      |
| 0.6208 | 3550 | 2.4147        | -      |
| 0.6226 | 3560 | 2.4458        | -      |
| 0.6243 | 3570 | 2.4207        | -      |
| 0.6261 | 3580 | 2.4333        | -      |
| 0.6278 | 3590 | 2.3931        | -      |
| 0.6289 | 3596 | -             | 0.8036 |
| 0.6296 | 3600 | 2.4695        | -      |
| 0.6313 | 3610 | 2.4285        | -      |
| 0.6331 | 3620 | 2.4066        | -      |
| 0.6348 | 3630 | 2.414         | -      |
| 0.6366 | 3640 | 2.4229        | -      |
| 0.6383 | 3650 | 2.3916        | -      |
| 0.6390 | 3654 | -             | 0.7960 |
| 0.6401 | 3660 | 2.4376        | -      |
| 0.6418 | 3670 | 2.4196        | -      |
| 0.6436 | 3680 | 2.4132        | -      |
| 0.6453 | 3690 | 2.4016        | -      |
| 0.6471 | 3700 | 2.3749        | -      |
| 0.6488 | 3710 | 2.3963        | -      |
| 0.6492 | 3712 | -             | 0.7895 |
| 0.6506 | 3720 | 2.4223        | -      |
| 0.6523 | 3730 | 2.3787        | -      |
| 0.6541 | 3740 | 2.368         | -      |
| 0.6558 | 3750 | 2.3526        | -      |
| 0.6576 | 3760 | 2.3883        | -      |
| 0.6593 | 3770 | 2.4286        | 0.7897 |
| 0.6611 | 3780 | 2.366         | -      |
| 0.6628 | 3790 | 2.3914        | -      |
| 0.6646 | 3800 | 2.416         | -      |
| 0.6663 | 3810 | 2.3731        | -      |
| 0.6681 | 3820 | 2.4097        | -      |
| 0.6695 | 3828 | -             | 0.7782 |
| 0.6698 | 3830 | 2.374         | -      |
| 0.6716 | 3840 | 2.3591        | -      |
| 0.6733 | 3850 | 2.384         | -      |
| 0.6751 | 3860 | 2.398         | -      |
| 0.6768 | 3870 | 2.3712        | -      |
| 0.6786 | 3880 | 2.3936        | -      |
| 0.6796 | 3886 | -             | 0.7725 |
| 0.6803 | 3890 | 2.3895        | -      |
| 0.6821 | 3900 | 2.359         | -      |
| 0.6838 | 3910 | 2.3901        | -      |
| 0.6856 | 3920 | 2.4           | -      |
| 0.6873 | 3930 | 2.3628        | -      |
| 0.6891 | 3940 | 2.3732        | -      |
| 0.6898 | 3944 | -             | 0.7658 |
| 0.6908 | 3950 | 2.3929        | -      |
| 0.6925 | 3960 | 2.3792        | -      |
| 0.6943 | 3970 | 2.3496        | -      |
| 0.6960 | 3980 | 2.3242        | -      |
| 0.6978 | 3990 | 2.3471        | -      |
| 0.6995 | 4000 | 2.3503        | -      |
| 0.6999 | 4002 | -             | 0.7617 |
| 0.7013 | 4010 | 2.3693        | -      |
| 0.7030 | 4020 | 2.3608        | -      |
| 0.7048 | 4030 | 2.3419        | -      |
| 0.7065 | 4040 | 2.3577        | -      |
| 0.7083 | 4050 | 2.3403        | -      |
| 0.7100 | 4060 | 2.3491        | 0.7549 |
| 0.7118 | 4070 | 2.3175        | -      |
| 0.7135 | 4080 | 2.3513        | -      |
| 0.7153 | 4090 | 2.3767        | -      |
| 0.7170 | 4100 | 2.371         | -      |
| 0.7188 | 4110 | 2.3103        | -      |
| 0.7202 | 4118 | -             | 0.7585 |
| 0.7205 | 4120 | 2.3048        | -      |
| 0.7223 | 4130 | 2.3406        | -      |
| 0.7240 | 4140 | 2.3551        | -      |
| 0.7258 | 4150 | 2.3309        | -      |
| 0.7275 | 4160 | 2.3565        | -      |
| 0.7293 | 4170 | 2.3111        | -      |
| 0.7303 | 4176 | -             | 0.7527 |
| 0.7310 | 4180 | 2.2925        | -      |
| 0.7328 | 4190 | 2.281         | -      |
| 0.7345 | 4200 | 2.3131        | -      |
| 0.7363 | 4210 | 2.3568        | -      |
| 0.7380 | 4220 | 2.3645        | -      |
| 0.7398 | 4230 | 2.3283        | -      |
| 0.7405 | 4234 | -             | 0.7497 |
| 0.7415 | 4240 | 2.3098        | -      |
| 0.7433 | 4250 | 2.3136        | -      |
| 0.7450 | 4260 | 2.3141        | -      |
| 0.7468 | 4270 | 2.2717        | -      |
| 0.7485 | 4280 | 2.325         | -      |
| 0.7503 | 4290 | 2.3358        | -      |
| 0.7506 | 4292 | -             | 0.7449 |
| 0.7520 | 4300 | 2.296         | -      |
| 0.7538 | 4310 | 2.3211        | -      |
| 0.7555 | 4320 | 2.3035        | -      |
| 0.7573 | 4330 | 2.3114        | -      |
| 0.7590 | 4340 | 2.3076        | -      |
| 0.7608 | 4350 | 2.334         | 0.7416 |
| 0.7625 | 4360 | 2.2805        | -      |
| 0.7643 | 4370 | 2.3302        | -      |
| 0.7660 | 4380 | 2.2753        | -      |
| 0.7678 | 4390 | 2.3084        | -      |
| 0.7695 | 4400 | 2.308         | -      |
| 0.7709 | 4408 | -             | 0.7463 |
| 0.7712 | 4410 | 2.2909        | -      |
| 0.7730 | 4420 | 2.2796        | -      |
| 0.7747 | 4430 | 2.2868        | -      |
| 0.7765 | 4440 | 2.3021        | -      |
| 0.7782 | 4450 | 2.2977        | -      |
| 0.7800 | 4460 | 2.2885        | -      |
| 0.7810 | 4466 | -             | 0.7391 |
| 0.7817 | 4470 | 2.2967        | -      |
| 0.7835 | 4480 | 2.2774        | -      |
| 0.7852 | 4490 | 2.3178        | -      |
| 0.7870 | 4500 | 2.2785        | -      |
| 0.7887 | 4510 | 2.2493        | -      |
| 0.7905 | 4520 | 2.2866        | -      |
| 0.7912 | 4524 | -             | 0.7325 |
| 0.7922 | 4530 | 2.2632        | -      |
| 0.7940 | 4540 | 2.289         | -      |
| 0.7957 | 4550 | 2.2782        | -      |
| 0.7975 | 4560 | 2.2607        | -      |
| 0.7992 | 4570 | 2.2914        | -      |
| 0.8010 | 4580 | 2.2593        | -      |
| 0.8013 | 4582 | -             | 0.7318 |
| 0.8027 | 4590 | 2.3077        | -      |
| 0.8045 | 4600 | 2.2793        | -      |
| 0.8062 | 4610 | 2.3051        | -      |
| 0.8080 | 4620 | 2.2914        | -      |
| 0.8097 | 4630 | 2.2646        | -      |
| 0.8115 | 4640 | 2.2574        | 0.7308 |
| 0.8132 | 4650 | 2.2654        | -      |
| 0.8150 | 4660 | 2.235         | -      |
| 0.8167 | 4670 | 2.258         | -      |
| 0.8185 | 4680 | 2.2935        | -      |
| 0.8202 | 4690 | 2.281         | -      |
| 0.8216 | 4698 | -             | 0.7281 |
| 0.8220 | 4700 | 2.295         | -      |
| 0.8237 | 4710 | 2.3095        | -      |
| 0.8255 | 4720 | 2.2516        | -      |
| 0.8272 | 4730 | 2.2292        | -      |
| 0.8290 | 4740 | 2.2635        | -      |
| 0.8307 | 4750 | 2.2522        | -      |
| 0.8318 | 4756 | -             | 0.7330 |
| 0.8325 | 4760 | 2.248         | -      |
| 0.8342 | 4770 | 2.3082        | -      |
| 0.8360 | 4780 | 2.2447        | -      |
| 0.8377 | 4790 | 2.2596        | -      |
| 0.8395 | 4800 | 2.2747        | -      |
| 0.8412 | 4810 | 2.2343        | -      |
| 0.8419 | 4814 | -             | 0.7319 |
| 0.8430 | 4820 | 2.2521        | -      |
| 0.8447 | 4830 | 2.2642        | -      |
| 0.8464 | 4840 | 2.2492        | -      |
| 0.8482 | 4850 | 2.2788        | -      |
| 0.8499 | 4860 | 2.2925        | -      |
| 0.8517 | 4870 | 2.2491        | -      |
| 0.8520 | 4872 | -             | 0.7304 |
| 0.8534 | 4880 | 2.2666        | -      |
| 0.8552 | 4890 | 2.2261        | -      |
| 0.8569 | 4900 | 2.2504        | -      |
| 0.8587 | 4910 | 2.2567        | -      |
| 0.8604 | 4920 | 2.2813        | -      |
| 0.8622 | 4930 | 2.244         | 0.7277 |
| 0.8639 | 4940 | 2.2645        | -      |
| 0.8657 | 4950 | 2.228         | -      |
| 0.8674 | 4960 | 2.2322        | -      |
| 0.8692 | 4970 | 2.2547        | -      |
| 0.8709 | 4980 | 2.2722        | -      |
| 0.8723 | 4988 | -             | 0.7272 |
| 0.8727 | 4990 | 2.227         | -      |
| 0.8744 | 5000 | 2.2407        | -      |
| 0.8762 | 5010 | 2.2269        | -      |
| 0.8779 | 5020 | 2.2428        | -      |
| 0.8797 | 5030 | 2.2448        | -      |
| 0.8814 | 5040 | 2.2562        | -      |
| 0.8825 | 5046 | -             | 0.7256 |
| 0.8832 | 5050 | 2.2364        | -      |
| 0.8849 | 5060 | 2.2445        | -      |
| 0.8867 | 5070 | 2.2409        | -      |
| 0.8884 | 5080 | 2.2261        | -      |
| 0.8902 | 5090 | 2.2613        | -      |
| 0.8919 | 5100 | 2.2718        | -      |
| 0.8926 | 5104 | -             | 0.7233 |
| 0.8937 | 5110 | 2.2544        | -      |
| 0.8954 | 5120 | 2.2276        | -      |
| 0.8972 | 5130 | 2.2385        | -      |
| 0.8989 | 5140 | 2.2401        | -      |
| 0.9007 | 5150 | 2.2769        | -      |
| 0.9024 | 5160 | 2.2399        | -      |
| 0.9028 | 5162 | -             | 0.7231 |
| 0.9042 | 5170 | 2.2205        | -      |
| 0.9059 | 5180 | 2.2303        | -      |
| 0.9077 | 5190 | 2.231         | -      |
| 0.9094 | 5200 | 2.2356        | -      |
| 0.9112 | 5210 | 2.2386        | -      |
| 0.9129 | 5220 | 2.2233        | 0.7233 |
| 0.9147 | 5230 | 2.2509        | -      |
| 0.9164 | 5240 | 2.2201        | -      |
| 0.9182 | 5250 | 2.2189        | -      |
| 0.9199 | 5260 | 2.1992        | -      |
| 0.9217 | 5270 | 2.2362        | -      |
| 0.9231 | 5278 | -             | 0.7221 |
| 0.9234 | 5280 | 2.2293        | -      |
| 0.9251 | 5290 | 2.2302        | -      |
| 0.9269 | 5300 | 2.2216        | -      |
| 0.9286 | 5310 | 2.2191        | -      |
| 0.9304 | 5320 | 2.2504        | -      |
| 0.9321 | 5330 | 2.2447        | -      |
| 0.9332 | 5336 | -             | 0.7221 |
| 0.9339 | 5340 | 2.2326        | -      |
| 0.9356 | 5350 | 2.2315        | -      |
| 0.9374 | 5360 | 2.244         | -      |
| 0.9391 | 5370 | 2.2369        | -      |
| 0.9409 | 5380 | 2.2312        | -      |
| 0.9426 | 5390 | 2.2739        | -      |
| 0.9433 | 5394 | -             | 0.7206 |
| 0.9444 | 5400 | 2.2598        | -      |
| 0.9461 | 5410 | 2.2319        | -      |
| 0.9479 | 5420 | 2.2312        | -      |
| 0.9496 | 5430 | 2.2592        | -      |
| 0.9514 | 5440 | 2.2503        | -      |
| 0.9531 | 5450 | 2.232         | -      |
| 0.9535 | 5452 | -             | 0.7208 |
| 0.9549 | 5460 | 2.2341        | -      |
| 0.9566 | 5470 | 2.2564        | -      |
| 0.9584 | 5480 | 2.2087        | -      |
| 0.9601 | 5490 | 2.257         | -      |
| 0.9619 | 5500 | 2.2524        | -      |
| 0.9636 | 5510 | 2.253         | 0.7204 |
| 0.9654 | 5520 | 2.2424        | -      |
| 0.9671 | 5530 | 2.2459        | -      |
| 0.9689 | 5540 | 2.2387        | -      |
| 0.9706 | 5550 | 2.2482        | -      |
| 0.9724 | 5560 | 2.2156        | -      |
| 0.9738 | 5568 | -             | 0.7200 |
| 0.9741 | 5570 | 2.2343        | -      |
| 0.9759 | 5580 | 2.2426        | -      |
| 0.9776 | 5590 | 2.2154        | -      |
| 0.9794 | 5600 | 2.2365        | -      |
| 0.9811 | 5610 | 2.275         | -      |
| 0.9829 | 5620 | 2.2689        | -      |
| 0.9839 | 5626 | -             | 0.7200 |
| 0.9846 | 5630 | 2.2356        | -      |
| 0.9864 | 5640 | 2.2517        | -      |
| 0.9881 | 5650 | 2.2436        | -      |
| 0.9899 | 5660 | 2.2229        | -      |
| 0.9916 | 5670 | 2.2617        | -      |
| 0.9934 | 5680 | 2.2359        | -      |
| 0.9941 | 5684 | -             | 0.7201 |
| 0.9951 | 5690 | 2.2444        | -      |
| 0.9969 | 5700 | 2.2505        | -      |
| 0.9986 | 5710 | 2.2713        | -      |

</details>

### Framework Versions
- Python: 3.11.8
- Sentence Transformers: 3.1.1
- Transformers: 4.45.1
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.0
- 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",
}
```

#### MaskedCachedMultipleNegativesRankingLoss
```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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