sentence-transformers safetensors t5 sentence-similarity feature-extraction generated_from_trainer dataset_size:557850 loss:MultipleNegativesRankingLoss en dataset:sentence-transformers/all-nli arxiv:1908.10084 arxiv:1705.00652 base_model:google-t5/t5-base base_model:finetune:google-t5/t5-base autotrain_compatible endpoints_compatible region:us
---
base_model: google-t5/t5-base
datasets:
- sentence-transformers/all-nli
language:
- en
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: A man is jumping unto his filthy bed.
  sentences:
  - A young male is looking at a newspaper while 2 females walks past him.
  - The bed is dirty.
  - The man is on the moon.
- source_sentence: A carefully balanced male stands on one foot near a clean ocean
    beach area.
  sentences:
  - A man is ouside near the beach.
  - Three policemen patrol the streets on bikes
  - A man is sitting on his couch.
- source_sentence: The man is wearing a blue shirt.
  sentences:
  - Near the trashcan the man stood and smoked
  - A man in a blue shirt leans on a wall beside a road with a blue van and red car
    with water in the background.
  - A man in a black shirt is playing a guitar.
- source_sentence: The girls are outdoors.
  sentences:
  - Two girls riding on an amusement part ride.
  - a guy laughs while doing laundry
  - Three girls are standing together in a room, one is listening, one is writing
    on a wall and the third is talking to them.
- source_sentence: A construction worker peeking out of a manhole while his coworker
    sits on the sidewalk smiling.
  sentences:
  - A worker is looking out of a manhole.
  - A man is giving a presentation.
  - The workers are both inside the manhole.
---

# SentenceTransformer based on google-t5/t5-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. 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:** [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) <!-- at revision a9723ea7f1b39c1eae772870f3b547bf6ef7e6c1 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- **Language:** en
<!-- - **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': 256, 'do_lower_case': False}) with Transformer model: T5EncoderModel 
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
    'A worker is looking out of a manhole.',
    'The workers are both inside the manhole.',
]
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

#### all-nli

* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 557,850 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                          | negative                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 9.96 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.79 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.02 tokens</li><li>max: 57 tokens</li></ul> |
* Samples:
  | anchor                                                                     | positive                                         | negative                                                   |
  |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   | <code>A person is at a diner, ordering an omelette.</code> |
  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>The kids are frowning</code>                         |
  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code>             |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### all-nli

* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 6,584 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                         | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                            |
  | details | <ul><li>min: 5 tokens</li><li>mean: 19.41 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.69 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.35 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                         | positive                                                    | negative                                                |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
  | <code>Two women are embracing while holding to go packages.</code>                                                                                                             | <code>Two woman are holding packages.</code>                | <code>The men are fighting outside a deli.</code>       |
  | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code>        |
  | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    | <code>A man selling donuts to a customer.</code>            | <code>A woman drinks her coffee in a small cafe.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 1e-05
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates

#### 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`: 64
- `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`: 1e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `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`: 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`: 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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0011 | 10   | -             | 1.8733          |
| 0.0023 | 20   | -             | 1.8726          |
| 0.0034 | 30   | -             | 1.8714          |
| 0.0046 | 40   | -             | 1.8697          |
| 0.0057 | 50   | -             | 1.8675          |
| 0.0069 | 60   | -             | 1.8649          |
| 0.0080 | 70   | -             | 1.8619          |
| 0.0092 | 80   | -             | 1.8584          |
| 0.0103 | 90   | -             | 1.8544          |
| 0.0115 | 100  | 3.1046        | 1.8499          |
| 0.0126 | 110  | -             | 1.8451          |
| 0.0138 | 120  | -             | 1.8399          |
| 0.0149 | 130  | -             | 1.8343          |
| 0.0161 | 140  | -             | 1.8283          |
| 0.0172 | 150  | -             | 1.8223          |
| 0.0184 | 160  | -             | 1.8159          |
| 0.0195 | 170  | -             | 1.8091          |
| 0.0206 | 180  | -             | 1.8016          |
| 0.0218 | 190  | -             | 1.7938          |
| 0.0229 | 200  | 3.0303        | 1.7858          |
| 0.0241 | 210  | -             | 1.7775          |
| 0.0252 | 220  | -             | 1.7693          |
| 0.0264 | 230  | -             | 1.7605          |
| 0.0275 | 240  | -             | 1.7514          |
| 0.0287 | 250  | -             | 1.7417          |
| 0.0298 | 260  | -             | 1.7320          |
| 0.0310 | 270  | -             | 1.7227          |
| 0.0321 | 280  | -             | 1.7134          |
| 0.0333 | 290  | -             | 1.7040          |
| 0.0344 | 300  | 2.9459        | 1.6941          |
| 0.0356 | 310  | -             | 1.6833          |
| 0.0367 | 320  | -             | 1.6725          |
| 0.0379 | 330  | -             | 1.6614          |
| 0.0390 | 340  | -             | 1.6510          |
| 0.0402 | 350  | -             | 1.6402          |
| 0.0413 | 360  | -             | 1.6296          |
| 0.0424 | 370  | -             | 1.6187          |
| 0.0436 | 380  | -             | 1.6073          |
| 0.0447 | 390  | -             | 1.5962          |
| 0.0459 | 400  | 2.7813        | 1.5848          |
| 0.0470 | 410  | -             | 1.5735          |
| 0.0482 | 420  | -             | 1.5620          |
| 0.0493 | 430  | -             | 1.5495          |
| 0.0505 | 440  | -             | 1.5375          |
| 0.0516 | 450  | -             | 1.5256          |
| 0.0528 | 460  | -             | 1.5133          |
| 0.0539 | 470  | -             | 1.5012          |
| 0.0551 | 480  | -             | 1.4892          |
| 0.0562 | 490  | -             | 1.4769          |
| 0.0574 | 500  | 2.6308        | 1.4640          |
| 0.0585 | 510  | -             | 1.4513          |
| 0.0597 | 520  | -             | 1.4391          |
| 0.0608 | 530  | -             | 1.4262          |
| 0.0619 | 540  | -             | 1.4130          |
| 0.0631 | 550  | -             | 1.3998          |
| 0.0642 | 560  | -             | 1.3874          |
| 0.0654 | 570  | -             | 1.3752          |
| 0.0665 | 580  | -             | 1.3620          |
| 0.0677 | 590  | -             | 1.3485          |
| 0.0688 | 600  | 2.4452        | 1.3350          |
| 0.0700 | 610  | -             | 1.3213          |
| 0.0711 | 620  | -             | 1.3088          |
| 0.0723 | 630  | -             | 1.2965          |
| 0.0734 | 640  | -             | 1.2839          |
| 0.0746 | 650  | -             | 1.2713          |
| 0.0757 | 660  | -             | 1.2592          |
| 0.0769 | 670  | -             | 1.2466          |
| 0.0780 | 680  | -             | 1.2332          |
| 0.0792 | 690  | -             | 1.2203          |
| 0.0803 | 700  | 2.2626        | 1.2077          |
| 0.0815 | 710  | -             | 1.1959          |
| 0.0826 | 720  | -             | 1.1841          |
| 0.0837 | 730  | -             | 1.1725          |
| 0.0849 | 740  | -             | 1.1619          |
| 0.0860 | 750  | -             | 1.1516          |
| 0.0872 | 760  | -             | 1.1416          |
| 0.0883 | 770  | -             | 1.1320          |
| 0.0895 | 780  | -             | 1.1227          |
| 0.0906 | 790  | -             | 1.1138          |
| 0.0918 | 800  | 2.0044        | 1.1053          |
| 0.0929 | 810  | -             | 1.0965          |
| 0.0941 | 820  | -             | 1.0879          |
| 0.0952 | 830  | -             | 1.0796          |
| 0.0964 | 840  | -             | 1.0718          |
| 0.0975 | 850  | -             | 1.0644          |
| 0.0987 | 860  | -             | 1.0564          |
| 0.0998 | 870  | -             | 1.0490          |
| 0.1010 | 880  | -             | 1.0417          |
| 0.1021 | 890  | -             | 1.0354          |
| 0.1032 | 900  | 1.8763        | 1.0296          |
| 0.1044 | 910  | -             | 1.0239          |
| 0.1055 | 920  | -             | 1.0180          |
| 0.1067 | 930  | -             | 1.0123          |
| 0.1078 | 940  | -             | 1.0065          |
| 0.1090 | 950  | -             | 1.0008          |
| 0.1101 | 960  | -             | 0.9950          |
| 0.1113 | 970  | -             | 0.9894          |
| 0.1124 | 980  | -             | 0.9840          |
| 0.1136 | 990  | -             | 0.9793          |
| 0.1147 | 1000 | 1.7287        | 0.9752          |
| 0.1159 | 1010 | -             | 0.9706          |
| 0.1170 | 1020 | -             | 0.9659          |
| 0.1182 | 1030 | -             | 0.9615          |
| 0.1193 | 1040 | -             | 0.9572          |
| 0.1205 | 1050 | -             | 0.9531          |
| 0.1216 | 1060 | -             | 0.9494          |
| 0.1227 | 1070 | -             | 0.9456          |
| 0.1239 | 1080 | -             | 0.9415          |
| 0.1250 | 1090 | -             | 0.9377          |
| 0.1262 | 1100 | 1.6312        | 0.9339          |
| 0.1273 | 1110 | -             | 0.9303          |
| 0.1285 | 1120 | -             | 0.9267          |
| 0.1296 | 1130 | -             | 0.9232          |
| 0.1308 | 1140 | -             | 0.9197          |
| 0.1319 | 1150 | -             | 0.9162          |
| 0.1331 | 1160 | -             | 0.9128          |
| 0.1342 | 1170 | -             | 0.9097          |
| 0.1354 | 1180 | -             | 0.9069          |
| 0.1365 | 1190 | -             | 0.9040          |
| 0.1377 | 1200 | 1.5316        | 0.9010          |
| 0.1388 | 1210 | -             | 0.8979          |
| 0.1400 | 1220 | -             | 0.8947          |
| 0.1411 | 1230 | -             | 0.8915          |
| 0.1423 | 1240 | -             | 0.8888          |
| 0.1434 | 1250 | -             | 0.8861          |
| 0.1445 | 1260 | -             | 0.8833          |
| 0.1457 | 1270 | -             | 0.8806          |
| 0.1468 | 1280 | -             | 0.8779          |
| 0.1480 | 1290 | -             | 0.8748          |
| 0.1491 | 1300 | 1.4961        | 0.8718          |
| 0.1503 | 1310 | -             | 0.8690          |
| 0.1514 | 1320 | -             | 0.8664          |
| 0.1526 | 1330 | -             | 0.8635          |
| 0.1537 | 1340 | -             | 0.8603          |
| 0.1549 | 1350 | -             | 0.8574          |
| 0.1560 | 1360 | -             | 0.8545          |
| 0.1572 | 1370 | -             | 0.8521          |
| 0.1583 | 1380 | -             | 0.8497          |
| 0.1595 | 1390 | -             | 0.8474          |
| 0.1606 | 1400 | 1.451         | 0.8453          |
| 0.1618 | 1410 | -             | 0.8429          |
| 0.1629 | 1420 | -             | 0.8404          |
| 0.1640 | 1430 | -             | 0.8380          |
| 0.1652 | 1440 | -             | 0.8357          |
| 0.1663 | 1450 | -             | 0.8336          |
| 0.1675 | 1460 | -             | 0.8312          |
| 0.1686 | 1470 | -             | 0.8289          |
| 0.1698 | 1480 | -             | 0.8262          |
| 0.1709 | 1490 | -             | 0.8236          |
| 0.1721 | 1500 | 1.4177        | 0.8213          |
| 0.1732 | 1510 | -             | 0.8189          |
| 0.1744 | 1520 | -             | 0.8168          |
| 0.1755 | 1530 | -             | 0.8147          |
| 0.1767 | 1540 | -             | 0.8127          |
| 0.1778 | 1550 | -             | 0.8107          |
| 0.1790 | 1560 | -             | 0.8082          |
| 0.1801 | 1570 | -             | 0.8059          |
| 0.1813 | 1580 | -             | 0.8036          |
| 0.1824 | 1590 | -             | 0.8015          |
| 0.1835 | 1600 | 1.3734        | 0.7993          |
| 0.1847 | 1610 | -             | 0.7970          |
| 0.1858 | 1620 | -             | 0.7948          |
| 0.1870 | 1630 | -             | 0.7922          |
| 0.1881 | 1640 | -             | 0.7900          |
| 0.1893 | 1650 | -             | 0.7877          |
| 0.1904 | 1660 | -             | 0.7852          |
| 0.1916 | 1670 | -             | 0.7829          |
| 0.1927 | 1680 | -             | 0.7804          |
| 0.1939 | 1690 | -             | 0.7779          |
| 0.1950 | 1700 | 1.3327        | 0.7757          |
| 0.1962 | 1710 | -             | 0.7738          |
| 0.1973 | 1720 | -             | 0.7719          |
| 0.1985 | 1730 | -             | 0.7700          |
| 0.1996 | 1740 | -             | 0.7679          |
| 0.2008 | 1750 | -             | 0.7658          |
| 0.2019 | 1760 | -             | 0.7641          |
| 0.2031 | 1770 | -             | 0.7621          |
| 0.2042 | 1780 | -             | 0.7601          |
| 0.2053 | 1790 | -             | 0.7580          |
| 0.2065 | 1800 | 1.2804        | 0.7558          |
| 0.2076 | 1810 | -             | 0.7536          |
| 0.2088 | 1820 | -             | 0.7514          |
| 0.2099 | 1830 | -             | 0.7493          |
| 0.2111 | 1840 | -             | 0.7473          |
| 0.2122 | 1850 | -             | 0.7451          |
| 0.2134 | 1860 | -             | 0.7429          |
| 0.2145 | 1870 | -             | 0.7408          |
| 0.2157 | 1880 | -             | 0.7389          |
| 0.2168 | 1890 | -             | 0.7368          |
| 0.2180 | 1900 | 1.2255        | 0.7349          |
| 0.2191 | 1910 | -             | 0.7328          |
| 0.2203 | 1920 | -             | 0.7310          |
| 0.2214 | 1930 | -             | 0.7293          |
| 0.2226 | 1940 | -             | 0.7277          |
| 0.2237 | 1950 | -             | 0.7259          |
| 0.2248 | 1960 | -             | 0.7240          |
| 0.2260 | 1970 | -             | 0.7221          |
| 0.2271 | 1980 | -             | 0.7203          |
| 0.2283 | 1990 | -             | 0.7184          |
| 0.2294 | 2000 | 1.2635        | 0.7165          |
| 0.2306 | 2010 | -             | 0.7150          |
| 0.2317 | 2020 | -             | 0.7135          |
| 0.2329 | 2030 | -             | 0.7117          |
| 0.2340 | 2040 | -             | 0.7099          |
| 0.2352 | 2050 | -             | 0.7084          |
| 0.2363 | 2060 | -             | 0.7068          |
| 0.2375 | 2070 | -             | 0.7054          |
| 0.2386 | 2080 | -             | 0.7037          |
| 0.2398 | 2090 | -             | 0.7023          |
| 0.2409 | 2100 | 1.1912        | 0.7009          |
| 0.2421 | 2110 | -             | 0.6991          |
| 0.2432 | 2120 | -             | 0.6974          |
| 0.2444 | 2130 | -             | 0.6962          |
| 0.2455 | 2140 | -             | 0.6950          |
| 0.2466 | 2150 | -             | 0.6938          |
| 0.2478 | 2160 | -             | 0.6922          |
| 0.2489 | 2170 | -             | 0.6909          |
| 0.2501 | 2180 | -             | 0.6897          |
| 0.2512 | 2190 | -             | 0.6884          |
| 0.2524 | 2200 | 1.2144        | 0.6868          |
| 0.2535 | 2210 | -             | 0.6856          |
| 0.2547 | 2220 | -             | 0.6843          |
| 0.2558 | 2230 | -             | 0.6829          |
| 0.2570 | 2240 | -             | 0.6817          |
| 0.2581 | 2250 | -             | 0.6804          |
| 0.2593 | 2260 | -             | 0.6789          |
| 0.2604 | 2270 | -             | 0.6775          |
| 0.2616 | 2280 | -             | 0.6763          |
| 0.2627 | 2290 | -             | 0.6751          |
| 0.2639 | 2300 | 1.1498        | 0.6739          |
| 0.2650 | 2310 | -             | 0.6725          |
| 0.2661 | 2320 | -             | 0.6711          |
| 0.2673 | 2330 | -             | 0.6698          |
| 0.2684 | 2340 | -             | 0.6684          |
| 0.2696 | 2350 | -             | 0.6666          |
| 0.2707 | 2360 | -             | 0.6653          |
| 0.2719 | 2370 | -             | 0.6638          |
| 0.2730 | 2380 | -             | 0.6621          |
| 0.2742 | 2390 | -             | 0.6609          |
| 0.2753 | 2400 | 1.1446        | 0.6596          |
| 0.2765 | 2410 | -             | 0.6582          |
| 0.2776 | 2420 | -             | 0.6568          |
| 0.2788 | 2430 | -             | 0.6553          |
| 0.2799 | 2440 | -             | 0.6541          |
| 0.2811 | 2450 | -             | 0.6527          |
| 0.2822 | 2460 | -             | 0.6513          |
| 0.2834 | 2470 | -             | 0.6496          |
| 0.2845 | 2480 | -             | 0.6483          |
| 0.2856 | 2490 | -             | 0.6475          |
| 0.2868 | 2500 | 1.1309        | 0.6465          |
| 0.2879 | 2510 | -             | 0.6455          |
| 0.2891 | 2520 | -             | 0.6447          |
| 0.2902 | 2530 | -             | 0.6437          |
| 0.2914 | 2540 | -             | 0.6428          |
| 0.2925 | 2550 | -             | 0.6415          |
| 0.2937 | 2560 | -             | 0.6403          |
| 0.2948 | 2570 | -             | 0.6392          |
| 0.2960 | 2580 | -             | 0.6381          |
| 0.2971 | 2590 | -             | 0.6371          |
| 0.2983 | 2600 | 1.1006        | 0.6358          |
| 0.2994 | 2610 | -             | 0.6348          |
| 0.3006 | 2620 | -             | 0.6340          |
| 0.3017 | 2630 | -             | 0.6330          |
| 0.3029 | 2640 | -             | 0.6319          |
| 0.3040 | 2650 | -             | 0.6308          |
| 0.3052 | 2660 | -             | 0.6300          |
| 0.3063 | 2670 | -             | 0.6291          |
| 0.3074 | 2680 | -             | 0.6280          |
| 0.3086 | 2690 | -             | 0.6268          |
| 0.3097 | 2700 | 1.0772        | 0.6254          |
| 0.3109 | 2710 | -             | 0.6243          |
| 0.3120 | 2720 | -             | 0.6232          |
| 0.3132 | 2730 | -             | 0.6224          |
| 0.3143 | 2740 | -             | 0.6215          |
| 0.3155 | 2750 | -             | 0.6205          |
| 0.3166 | 2760 | -             | 0.6194          |
| 0.3178 | 2770 | -             | 0.6183          |
| 0.3189 | 2780 | -             | 0.6171          |
| 0.3201 | 2790 | -             | 0.6160          |
| 0.3212 | 2800 | 1.0648        | 0.6153          |
| 0.3224 | 2810 | -             | 0.6141          |
| 0.3235 | 2820 | -             | 0.6129          |
| 0.3247 | 2830 | -             | 0.6119          |
| 0.3258 | 2840 | -             | 0.6109          |
| 0.3269 | 2850 | -             | 0.6099          |
| 0.3281 | 2860 | -             | 0.6088          |
| 0.3292 | 2870 | -             | 0.6079          |
| 0.3304 | 2880 | -             | 0.6073          |
| 0.3315 | 2890 | -             | 0.6063          |
| 0.3327 | 2900 | 1.0398        | 0.6054          |
| 0.3338 | 2910 | -             | 0.6044          |
| 0.3350 | 2920 | -             | 0.6033          |
| 0.3361 | 2930 | -             | 0.6022          |
| 0.3373 | 2940 | -             | 0.6012          |
| 0.3384 | 2950 | -             | 0.6003          |
| 0.3396 | 2960 | -             | 0.5993          |
| 0.3407 | 2970 | -             | 0.5986          |
| 0.3419 | 2980 | -             | 0.5978          |
| 0.3430 | 2990 | -             | 0.5967          |
| 0.3442 | 3000 | 1.0256        | 0.5959          |
| 0.3453 | 3010 | -             | 0.5947          |
| 0.3464 | 3020 | -             | 0.5937          |
| 0.3476 | 3030 | -             | 0.5929          |
| 0.3487 | 3040 | -             | 0.5920          |
| 0.3499 | 3050 | -             | 0.5908          |
| 0.3510 | 3060 | -             | 0.5897          |
| 0.3522 | 3070 | -             | 0.5888          |
| 0.3533 | 3080 | -             | 0.5882          |
| 0.3545 | 3090 | -             | 0.5874          |
| 0.3556 | 3100 | 1.0489        | 0.5868          |
| 0.3568 | 3110 | -             | 0.5860          |
| 0.3579 | 3120 | -             | 0.5854          |
| 0.3591 | 3130 | -             | 0.5839          |
| 0.3602 | 3140 | -             | 0.5830          |
| 0.3614 | 3150 | -             | 0.5822          |
| 0.3625 | 3160 | -             | 0.5814          |
| 0.3637 | 3170 | -             | 0.5808          |
| 0.3648 | 3180 | -             | 0.5802          |
| 0.3660 | 3190 | -             | 0.5794          |
| 0.3671 | 3200 | 1.038         | 0.5788          |
| 0.3682 | 3210 | -             | 0.5778          |
| 0.3694 | 3220 | -             | 0.5770          |
| 0.3705 | 3230 | -             | 0.5763          |
| 0.3717 | 3240 | -             | 0.5752          |
| 0.3728 | 3250 | -             | 0.5745          |
| 0.3740 | 3260 | -             | 0.5737          |
| 0.3751 | 3270 | -             | 0.5728          |
| 0.3763 | 3280 | -             | 0.5720          |
| 0.3774 | 3290 | -             | 0.5713          |
| 0.3786 | 3300 | 1.0058        | 0.5707          |
| 0.3797 | 3310 | -             | 0.5700          |
| 0.3809 | 3320 | -             | 0.5690          |
| 0.3820 | 3330 | -             | 0.5681          |
| 0.3832 | 3340 | -             | 0.5673          |
| 0.3843 | 3350 | -             | 0.5669          |
| 0.3855 | 3360 | -             | 0.5667          |
| 0.3866 | 3370 | -             | 0.5665          |
| 0.3877 | 3380 | -             | 0.5659          |
| 0.3889 | 3390 | -             | 0.5650          |
| 0.3900 | 3400 | 1.0413        | 0.5645          |
| 0.3912 | 3410 | -             | 0.5641          |
| 0.3923 | 3420 | -             | 0.5635          |
| 0.3935 | 3430 | -             | 0.5629          |
| 0.3946 | 3440 | -             | 0.5622          |
| 0.3958 | 3450 | -             | 0.5617          |
| 0.3969 | 3460 | -             | 0.5614          |
| 0.3981 | 3470 | -             | 0.5607          |
| 0.3992 | 3480 | -             | 0.5603          |
| 0.4004 | 3490 | -             | 0.5598          |
| 0.4015 | 3500 | 0.938         | 0.5596          |
| 0.4027 | 3510 | -             | 0.5589          |
| 0.4038 | 3520 | -             | 0.5581          |
| 0.4050 | 3530 | -             | 0.5571          |
| 0.4061 | 3540 | -             | 0.5563          |
| 0.4073 | 3550 | -             | 0.5557          |
| 0.4084 | 3560 | -             | 0.5551          |
| 0.4095 | 3570 | -             | 0.5546          |
| 0.4107 | 3580 | -             | 0.5541          |
| 0.4118 | 3590 | -             | 0.5535          |
| 0.4130 | 3600 | 0.955         | 0.5528          |
| 0.4141 | 3610 | -             | 0.5522          |
| 0.4153 | 3620 | -             | 0.5516          |
| 0.4164 | 3630 | -             | 0.5509          |
| 0.4176 | 3640 | -             | 0.5503          |
| 0.4187 | 3650 | -             | 0.5495          |
| 0.4199 | 3660 | -             | 0.5490          |
| 0.4210 | 3670 | -             | 0.5481          |
| 0.4222 | 3680 | -             | 0.5475          |
| 0.4233 | 3690 | -             | 0.5467          |
| 0.4245 | 3700 | 0.9387        | 0.5463          |
| 0.4256 | 3710 | -             | 0.5459          |
| 0.4268 | 3720 | -             | 0.5452          |
| 0.4279 | 3730 | -             | 0.5448          |
| 0.4290 | 3740 | -             | 0.5443          |
| 0.4302 | 3750 | -             | 0.5440          |
| 0.4313 | 3760 | -             | 0.5435          |
| 0.4325 | 3770 | -             | 0.5430          |
| 0.4336 | 3780 | -             | 0.5423          |
| 0.4348 | 3790 | -             | 0.5418          |
| 0.4359 | 3800 | 0.9672        | 0.5415          |
| 0.4371 | 3810 | -             | 0.5413          |
| 0.4382 | 3820 | -             | 0.5410          |
| 0.4394 | 3830 | -             | 0.5406          |
| 0.4405 | 3840 | -             | 0.5403          |
| 0.4417 | 3850 | -             | 0.5397          |
| 0.4428 | 3860 | -             | 0.5394          |
| 0.4440 | 3870 | -             | 0.5386          |
| 0.4451 | 3880 | -             | 0.5378          |
| 0.4463 | 3890 | -             | 0.5370          |
| 0.4474 | 3900 | 0.926         | 0.5360          |
| 0.4485 | 3910 | -             | 0.5351          |
| 0.4497 | 3920 | -             | 0.5346          |
| 0.4508 | 3930 | -             | 0.5343          |
| 0.4520 | 3940 | -             | 0.5339          |
| 0.4531 | 3950 | -             | 0.5337          |
| 0.4543 | 3960 | -             | 0.5334          |
| 0.4554 | 3970 | -             | 0.5330          |
| 0.4566 | 3980 | -             | 0.5327          |
| 0.4577 | 3990 | -             | 0.5324          |
| 0.4589 | 4000 | 0.867         | 0.5319          |
| 0.4600 | 4010 | -             | 0.5313          |
| 0.4612 | 4020 | -             | 0.5308          |
| 0.4623 | 4030 | -             | 0.5300          |
| 0.4635 | 4040 | -             | 0.5293          |
| 0.4646 | 4050 | -             | 0.5287          |
| 0.4658 | 4060 | -             | 0.5284          |
| 0.4669 | 4070 | -             | 0.5281          |
| 0.4681 | 4080 | -             | 0.5277          |
| 0.4692 | 4090 | -             | 0.5272          |
| 0.4703 | 4100 | 0.916         | 0.5267          |
| 0.4715 | 4110 | -             | 0.5260          |
| 0.4726 | 4120 | -             | 0.5252          |
| 0.4738 | 4130 | -             | 0.5246          |
| 0.4749 | 4140 | -             | 0.5239          |
| 0.4761 | 4150 | -             | 0.5232          |
| 0.4772 | 4160 | -             | 0.5225          |
| 0.4784 | 4170 | -             | 0.5221          |
| 0.4795 | 4180 | -             | 0.5216          |
| 0.4807 | 4190 | -             | 0.5211          |
| 0.4818 | 4200 | 0.9667        | 0.5206          |
| 0.4830 | 4210 | -             | 0.5204          |
| 0.4841 | 4220 | -             | 0.5200          |
| 0.4853 | 4230 | -             | 0.5192          |
| 0.4864 | 4240 | -             | 0.5187          |
| 0.4876 | 4250 | -             | 0.5185          |
| 0.4887 | 4260 | -             | 0.5179          |
| 0.4898 | 4270 | -             | 0.5173          |
| 0.4910 | 4280 | -             | 0.5170          |
| 0.4921 | 4290 | -             | 0.5165          |
| 0.4933 | 4300 | 0.9276        | 0.5160          |
| 0.4944 | 4310 | -             | 0.5154          |
| 0.4956 | 4320 | -             | 0.5150          |
| 0.4967 | 4330 | -             | 0.5144          |
| 0.4979 | 4340 | -             | 0.5141          |
| 0.4990 | 4350 | -             | 0.5139          |
| 0.5002 | 4360 | -             | 0.5138          |
| 0.5013 | 4370 | -             | 0.5136          |
| 0.5025 | 4380 | -             | 0.5133          |
| 0.5036 | 4390 | -             | 0.5129          |
| 0.5048 | 4400 | 0.9331        | 0.5126          |
| 0.5059 | 4410 | -             | 0.5123          |
| 0.5071 | 4420 | -             | 0.5117          |
| 0.5082 | 4430 | -             | 0.5113          |
| 0.5093 | 4440 | -             | 0.5108          |
| 0.5105 | 4450 | -             | 0.5106          |
| 0.5116 | 4460 | -             | 0.5106          |
| 0.5128 | 4470 | -             | 0.5106          |
| 0.5139 | 4480 | -             | 0.5104          |
| 0.5151 | 4490 | -             | 0.5102          |
| 0.5162 | 4500 | 0.907         | 0.5097          |
| 0.5174 | 4510 | -             | 0.5092          |
| 0.5185 | 4520 | -             | 0.5086          |
| 0.5197 | 4530 | -             | 0.5082          |
| 0.5208 | 4540 | -             | 0.5079          |
| 0.5220 | 4550 | -             | 0.5075          |
| 0.5231 | 4560 | -             | 0.5071          |
| 0.5243 | 4570 | -             | 0.5067          |
| 0.5254 | 4580 | -             | 0.5066          |
| 0.5266 | 4590 | -             | 0.5062          |
| 0.5277 | 4600 | 0.913         | 0.5059          |
| 0.5289 | 4610 | -             | 0.5056          |
| 0.5300 | 4620 | -             | 0.5052          |
| 0.5311 | 4630 | -             | 0.5046          |
| 0.5323 | 4640 | -             | 0.5039          |
| 0.5334 | 4650 | -             | 0.5033          |
| 0.5346 | 4660 | -             | 0.5030          |
| 0.5357 | 4670 | -             | 0.5028          |
| 0.5369 | 4680 | -             | 0.5027          |
| 0.5380 | 4690 | -             | 0.5023          |
| 0.5392 | 4700 | 0.9047        | 0.5020          |
| 0.5403 | 4710 | -             | 0.5018          |
| 0.5415 | 4720 | -             | 0.5015          |
| 0.5426 | 4730 | -             | 0.5009          |
| 0.5438 | 4740 | -             | 0.5003          |
| 0.5449 | 4750 | -             | 0.4997          |
| 0.5461 | 4760 | -             | 0.4991          |
| 0.5472 | 4770 | -             | 0.4984          |
| 0.5484 | 4780 | -             | 0.4980          |
| 0.5495 | 4790 | -             | 0.4980          |
| 0.5506 | 4800 | 0.887         | 0.4979          |
| 0.5518 | 4810 | -             | 0.4975          |
| 0.5529 | 4820 | -             | 0.4973          |
| 0.5541 | 4830 | -             | 0.4969          |
| 0.5552 | 4840 | -             | 0.4966          |
| 0.5564 | 4850 | -             | 0.4964          |
| 0.5575 | 4860 | -             | 0.4964          |
| 0.5587 | 4870 | -             | 0.4960          |
| 0.5598 | 4880 | -             | 0.4957          |
| 0.5610 | 4890 | -             | 0.4955          |
| 0.5621 | 4900 | 0.8645        | 0.4952          |
| 0.5633 | 4910 | -             | 0.4950          |
| 0.5644 | 4920 | -             | 0.4952          |
| 0.5656 | 4930 | -             | 0.4949          |
| 0.5667 | 4940 | -             | 0.4943          |
| 0.5679 | 4950 | -             | 0.4938          |
| 0.5690 | 4960 | -             | 0.4936          |
| 0.5702 | 4970 | -             | 0.4933          |
| 0.5713 | 4980 | -             | 0.4931          |
| 0.5724 | 4990 | -             | 0.4929          |
| 0.5736 | 5000 | 0.8348        | 0.4924          |
| 0.5747 | 5010 | -             | 0.4921          |
| 0.5759 | 5020 | -             | 0.4915          |
| 0.5770 | 5030 | -             | 0.4911          |
| 0.5782 | 5040 | -             | 0.4909          |
| 0.5793 | 5050 | -             | 0.4905          |
| 0.5805 | 5060 | -             | 0.4900          |
| 0.5816 | 5070 | -             | 0.4892          |
| 0.5828 | 5080 | -             | 0.4886          |
| 0.5839 | 5090 | -             | 0.4883          |
| 0.5851 | 5100 | 0.871         | 0.4879          |
| 0.5862 | 5110 | -             | 0.4877          |
| 0.5874 | 5120 | -             | 0.4874          |
| 0.5885 | 5130 | -             | 0.4870          |
| 0.5897 | 5140 | -             | 0.4867          |
| 0.5908 | 5150 | -             | 0.4864          |
| 0.5919 | 5160 | -             | 0.4862          |
| 0.5931 | 5170 | -             | 0.4860          |
| 0.5942 | 5180 | -             | 0.4857          |
| 0.5954 | 5190 | -             | 0.4855          |
| 0.5965 | 5200 | 0.8522        | 0.4850          |
| 0.5977 | 5210 | -             | 0.4846          |
| 0.5988 | 5220 | -             | 0.4844          |
| 0.6000 | 5230 | -             | 0.4842          |
| 0.6011 | 5240 | -             | 0.4837          |
| 0.6023 | 5250 | -             | 0.4835          |
| 0.6034 | 5260 | -             | 0.4831          |
| 0.6046 | 5270 | -             | 0.4826          |
| 0.6057 | 5280 | -             | 0.4822          |
| 0.6069 | 5290 | -             | 0.4822          |
| 0.6080 | 5300 | 0.869         | 0.4820          |
| 0.6092 | 5310 | -             | 0.4818          |
| 0.6103 | 5320 | -             | 0.4819          |
| 0.6114 | 5330 | -             | 0.4819          |
| 0.6126 | 5340 | -             | 0.4815          |
| 0.6137 | 5350 | -             | 0.4813          |
| 0.6149 | 5360 | -             | 0.4812          |
| 0.6160 | 5370 | -             | 0.4810          |
| 0.6172 | 5380 | -             | 0.4809          |
| 0.6183 | 5390 | -             | 0.4806          |
| 0.6195 | 5400 | 0.8548        | 0.4805          |
| 0.6206 | 5410 | -             | 0.4800          |
| 0.6218 | 5420 | -             | 0.4798          |
| 0.6229 | 5430 | -             | 0.4795          |
| 0.6241 | 5440 | -             | 0.4792          |
| 0.6252 | 5450 | -             | 0.4790          |
| 0.6264 | 5460 | -             | 0.4790          |
| 0.6275 | 5470 | -             | 0.4791          |
| 0.6287 | 5480 | -             | 0.4794          |
| 0.6298 | 5490 | -             | 0.4792          |
| 0.6310 | 5500 | 0.8366        | 0.4790          |
| 0.6321 | 5510 | -             | 0.4786          |
| 0.6332 | 5520 | -             | 0.4780          |
| 0.6344 | 5530 | -             | 0.4773          |
| 0.6355 | 5540 | -             | 0.4768          |
| 0.6367 | 5550 | -             | 0.4767          |
| 0.6378 | 5560 | -             | 0.4765          |
| 0.6390 | 5570 | -             | 0.4765          |
| 0.6401 | 5580 | -             | 0.4763          |
| 0.6413 | 5590 | -             | 0.4760          |
| 0.6424 | 5600 | 0.8696        | 0.4757          |
| 0.6436 | 5610 | -             | 0.4754          |
| 0.6447 | 5620 | -             | 0.4752          |
| 0.6459 | 5630 | -             | 0.4751          |
| 0.6470 | 5640 | -             | 0.4747          |
| 0.6482 | 5650 | -             | 0.4747          |
| 0.6493 | 5660 | -             | 0.4742          |
| 0.6505 | 5670 | -             | 0.4740          |
| 0.6516 | 5680 | -             | 0.4736          |
| 0.6527 | 5690 | -             | 0.4730          |
| 0.6539 | 5700 | 0.8302        | 0.4725          |
| 0.6550 | 5710 | -             | 0.4723          |
| 0.6562 | 5720 | -             | 0.4720          |
| 0.6573 | 5730 | -             | 0.4718          |
| 0.6585 | 5740 | -             | 0.4715          |
| 0.6596 | 5750 | -             | 0.4714          |
| 0.6608 | 5760 | -             | 0.4711          |
| 0.6619 | 5770 | -             | 0.4707          |
| 0.6631 | 5780 | -             | 0.4707          |
| 0.6642 | 5790 | -             | 0.4703          |
| 0.6654 | 5800 | 0.8128        | 0.4703          |
| 0.6665 | 5810 | -             | 0.4701          |
| 0.6677 | 5820 | -             | 0.4699          |
| 0.6688 | 5830 | -             | 0.4697          |
| 0.6700 | 5840 | -             | 0.4698          |
| 0.6711 | 5850 | -             | 0.4695          |
| 0.6722 | 5860 | -             | 0.4691          |
| 0.6734 | 5870 | -             | 0.4689          |
| 0.6745 | 5880 | -             | 0.4689          |
| 0.6757 | 5890 | -             | 0.4688          |
| 0.6768 | 5900 | 0.8437        | 0.4683          |
| 0.6780 | 5910 | -             | 0.4683          |
| 0.6791 | 5920 | -             | 0.4681          |
| 0.6803 | 5930 | -             | 0.4678          |
| 0.6814 | 5940 | -             | 0.4677          |
| 0.6826 | 5950 | -             | 0.4676          |
| 0.6837 | 5960 | -             | 0.4673          |
| 0.6849 | 5970 | -             | 0.4668          |
| 0.6860 | 5980 | -             | 0.4667          |
| 0.6872 | 5990 | -             | 0.4661          |
| 0.6883 | 6000 | 0.7774        | 0.4657          |

</details>

### Framework Versions
- Python: 3.12.8
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.2.0+cu121
- Accelerate: 1.4.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0

## 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->