README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:150468
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: Third-party payment providers, such as Stripe, Checkout. Coingate
    and similar they help us to process payments together with our own authorized
    payment processing companiesUnited States, Ireland, BVIStorage and infrastructure
    service providers, such as BigQuery (by Google), Stitch (by Talend)they help us
    to deliver targeted advertising to the Website visitors United StatesLive chat
    and support service providers, such as Zendesk we use them to provide live chat
    technology and provide support to our users United StatesSecurity service providers,
    such as Cloudflare we work with them to provide improved security and performance
    United StatesAttorneys, notaries, bailiffs we transfer personal information in
    cases when we seek to defend our rights and legal interests
  sentences:
  - Many third parties are involved in operating the service
  - Only aggregate data is given to third parties
  - You should revisit the terms periodically, although in case of material changes,
    the service will notify
- source_sentence: 'The Privacy and Cookie Policy states: "Please note, however, that
    by blocking or deleting cookies used on the Service, you may not be able to take
    full advantage of the Service and you may not be able to log on to the Service
    or play the Roblox games."'
  sentences:
  - Blocking first party cookies may limit your ability to use the service
  - There is a date of the last update of the agreements
  - Other applicable rules, terms, conditions or guidelines
- source_sentence: You may also request a copy of your data by (a) logging into your
    Swarm account or (b) logging into your City Guide account (web only) and clicking
    on “Export My Data” in your privacy settings. You may also delete your data and
  sentences:
  - The court of law governing the terms is in location X
  - This Service provides a list of Third Parties involved in its operation.
  - You can request access, correction and/or deletion of your data
- source_sentence: Conducting relevant promotional activities, such as providing marketing
    and promotional materials and updates.
  sentences:
  - Your personal data may be sold or otherwise transferred as part of a bankruptcy
    proceeding or other type of financial transaction
  - You authorise the service to charge a credit card supplied on re-occurring basis
  - Your personal data may be used for marketing purposes
- source_sentence: Pexgle will need to share your information, including personal
    information, in order to ensure the adequate performance of our contract with
    you.
  sentences:
  - IP addresses of website visitors are not tracked
  - Extra data may be collected about you through promotions
  - This service gives your personal data to third parties involved in its operation
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: all nli dev
      type: all-nli-dev
    metrics:
    - type: cosine_accuracy
      value: 0.9992556571960449
      name: Cosine Accuracy
---

# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **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': 256, '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("AryehRotberg/ToS-Sentence-Transformers")
# Run inference
sentences = [
    'Pexgle will need to share your information, including personal information, in order to ensure the adequate performance of our contract with you.',
    'This service gives your personal data to third parties involved in its operation',
    'Extra data may be collected about you through promotions',
]
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

#### Triplet

* Dataset: `all-nli-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9993** |

<!--
## 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: 150,468 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: 4 tokens</li><li>mean: 48.6 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.72 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.26 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | positive                                                                                                         | negative                                                                                                |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|
  | <code>For all User Submissions, you hereby grant Guilded a license to translate, modify (for technical purposes, for example, making sure your content is viewable on a mobile device as well as a computer) and reproduce and otherwise act with respect to such User Submissions, in each case to enable us to operate the Services, as described in more detail below.</code>                                                                                                                                                            | <code>Copyright license limited for the purposes of that same service but transferable and sublicenseable</code> | <code>You are prohibited from sending chain letters, junk mail, spam or any unsolicited messages</code> |
  | <code>Our data is stored in the EU or USA with robust physical, digital, and procedural safeguards in place to protect your personal data, including the use of SSL encryption, redundant servers and data centers, and sophisticated perimeter security. We continuously audit for security vulnerabilities and make software patching a priority.</code>                                                                                                                                                                                  | <code>Information is provided about security practices</code>                                                    | <code>The service disables software that you are not licensed to use.</code>                            |
  | <code>No part of our Platform may be reproduced in any form or incorporated into any information retrieval system, electronic or mechanical, other than for your personal use. While using our Platform, you cannot redistribute your license (“Premium”, “Pro”, “Lite”) to anyone in any way that can make them use the features bound to your account. Unless otherwise specified, the developer tools and components, download areas, communication forums, and product information are for your personal and non-commercial use.</code> | <code>This service is only available for use individually and non-commercially.</code>                           | <code>Accessibility to this service is guaranteed at 99% or more</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

#### Unnamed Dataset

* Size: 37,617 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: 3 tokens</li><li>mean: 45.61 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.64 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.26 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                                                                                                                                                                                                                                                                                      | positive                                                                                                            | negative                                                                  |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------|
  | <code>non-exclusive, worldwide right and license to use,</code>                                                                                                                                                                                                                                                                                                                                                                             | <code>The service has non-exclusive use of your content</code>                                                      | <code>You are not being tracked</code>                                    |
  | <code>We also reserve the right to suspend or end the Service at any time at our discretion and without notice. For example, we may suspend or terminate your use of the Service and remove Your Content if you’re not complying with these AUP Guidelines, or using the Service in a manner that may cause us legal liability, disrupt the Service, disrupt others’ use of the Service or, in our sole opinion, reason, cause harm.</code> | <code>Your account can be deleted or permanently suspended without prior notice and without a reason</code>         | <code>The service claims to be CCPA compliant for California users</code> |
  | <code>ExpressVPN uses mobile identifiers to generate statistics related to the marketing channels and advertising partners through which users learned about and signed up for ExpressVPN mobile apps.</code>                                                                                                                                                                                                                               | <code>You are tracked via web beacons, tracking pixels, browser fingerprinting, and/or device fingerprinting</code> | <code>Your personal data is used for advertising</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`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 1
- `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`: 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
| Epoch  | Step | Training Loss | Validation Loss | all-nli-dev_cosine_accuracy |
|:------:|:----:|:-------------:|:---------------:|:---------------------------:|
| -1     | -1   | -             | -               | 0.9527                      |
| 0.0106 | 100  | 1.3092        | 1.1396          | 0.9620                      |
| 0.0213 | 200  | 1.0389        | 0.8936          | 0.9742                      |
| 0.0319 | 300  | 0.8838        | 0.7500          | 0.9793                      |
| 0.0425 | 400  | 0.7582        | 0.6477          | 0.9843                      |
| 0.0532 | 500  | 0.6358        | 0.5727          | 0.9871                      |
| 0.0638 | 600  | 0.6451        | 0.5158          | 0.9889                      |
| 0.0744 | 700  | 0.4932        | 0.4715          | 0.9903                      |
| 0.0851 | 800  | 0.4865        | 0.4355          | 0.9913                      |
| 0.0957 | 900  | 0.4636        | 0.4035          | 0.9927                      |
| 0.1063 | 1000 | 0.4406        | 0.3846          | 0.9930                      |
| 0.1170 | 1100 | 0.3824        | 0.3691          | 0.9934                      |
| 0.1276 | 1200 | 0.3967        | 0.3411          | 0.9944                      |
| 0.1382 | 1300 | 0.3448        | 0.3264          | 0.9945                      |
| 0.1489 | 1400 | 0.3372        | 0.3018          | 0.9955                      |
| 0.1595 | 1500 | 0.3035        | 0.2941          | 0.9959                      |
| 0.1701 | 1600 | 0.319         | 0.2864          | 0.9956                      |
| 0.1808 | 1700 | 0.292         | 0.2743          | 0.9964                      |
| 0.1914 | 1800 | 0.2647        | 0.2727          | 0.9965                      |
| 0.2020 | 1900 | 0.2948        | 0.2517          | 0.9968                      |
| 0.2127 | 2000 | 0.2583        | 0.2456          | 0.9971                      |
| 0.2233 | 2100 | 0.2685        | 0.2352          | 0.9970                      |
| 0.2339 | 2200 | 0.2879        | 0.2327          | 0.9969                      |
| 0.2446 | 2300 | 0.2366        | 0.2271          | 0.9972                      |
| 0.2552 | 2400 | 0.231         | 0.2164          | 0.9972                      |
| 0.2658 | 2500 | 0.2639        | 0.2124          | 0.9973                      |
| 0.2764 | 2600 | 0.2543        | 0.2078          | 0.9976                      |
| 0.2871 | 2700 | 0.2261        | 0.2043          | 0.9972                      |
| 0.2977 | 2800 | 0.2239        | 0.1976          | 0.9978                      |
| 0.3083 | 2900 | 0.2271        | 0.1932          | 0.9977                      |
| 0.3190 | 3000 | 0.2334        | 0.1845          | 0.9979                      |
| 0.3296 | 3100 | 0.2021        | 0.1867          | 0.9981                      |
| 0.3402 | 3200 | 0.2237        | 0.1762          | 0.9984                      |
| 0.3509 | 3300 | 0.2109        | 0.1730          | 0.9983                      |
| 0.3615 | 3400 | 0.2047        | 0.1663          | 0.9985                      |
| 0.3721 | 3500 | 0.1904        | 0.1629          | 0.9984                      |
| 0.3828 | 3600 | 0.1687        | 0.1643          | 0.9984                      |
| 0.3934 | 3700 | 0.2071        | 0.1584          | 0.9984                      |
| 0.4040 | 3800 | 0.1609        | 0.1543          | 0.9983                      |
| 0.4147 | 3900 | 0.1862        | 0.1525          | 0.9984                      |
| 0.4253 | 4000 | 0.1925        | 0.1504          | 0.9984                      |
| 0.4359 | 4100 | 0.1714        | 0.1484          | 0.9985                      |
| 0.4466 | 4200 | 0.2025        | 0.1472          | 0.9985                      |
| 0.4572 | 4300 | 0.1427        | 0.1422          | 0.9986                      |
| 0.4678 | 4400 | 0.1458        | 0.1401          | 0.9986                      |
| 0.4785 | 4500 | 0.1796        | 0.1371          | 0.9985                      |
| 0.4891 | 4600 | 0.1289        | 0.1317          | 0.9987                      |
| 0.4997 | 4700 | 0.1427        | 0.1298          | 0.9988                      |
| 0.5104 | 4800 | 0.1349        | 0.1313          | 0.9988                      |
| 0.5210 | 4900 | 0.149         | 0.1293          | 0.9987                      |
| 0.5316 | 5000 | 0.1633        | 0.1230          | 0.9988                      |
| 0.5423 | 5100 | 0.1241        | 0.1240          | 0.9988                      |
| 0.5529 | 5200 | 0.1532        | 0.1196          | 0.9988                      |
| 0.5635 | 5300 | 0.1547        | 0.1173          | 0.9988                      |
| 0.5742 | 5400 | 0.1652        | 0.1167          | 0.9990                      |
| 0.5848 | 5500 | 0.1505        | 0.1120          | 0.9989                      |
| 0.5954 | 5600 | 0.1309        | 0.1106          | 0.9990                      |
| 0.6061 | 5700 | 0.1648        | 0.1089          | 0.9988                      |
| 0.6167 | 5800 | 0.118         | 0.1070          | 0.9988                      |
| 0.6273 | 5900 | 0.1207        | 0.1062          | 0.9988                      |
| 0.6380 | 6000 | 0.1104        | 0.1046          | 0.9989                      |
| 0.6486 | 6100 | 0.1262        | 0.1040          | 0.9989                      |
| 0.6592 | 6200 | 0.1236        | 0.1008          | 0.9990                      |
| 0.6699 | 6300 | 0.122         | 0.1005          | 0.9990                      |
| 0.6805 | 6400 | 0.1244        | 0.1005          | 0.9991                      |
| 0.6911 | 6500 | 0.1176        | 0.0998          | 0.9991                      |
| 0.7018 | 6600 | 0.1215        | 0.0994          | 0.9991                      |
| 0.7124 | 6700 | 0.1079        | 0.0983          | 0.9991                      |
| 0.7230 | 6800 | 0.1099        | 0.0957          | 0.9991                      |
| 0.7337 | 6900 | 0.1121        | 0.0950          | 0.9992                      |
| 0.7443 | 7000 | 0.1137        | 0.0942          | 0.9992                      |
| 0.7549 | 7100 | 0.1082        | 0.0929          | 0.9991                      |
| 0.7656 | 7200 | 0.1047        | 0.0923          | 0.9991                      |
| 0.7762 | 7300 | 0.1147        | 0.0904          | 0.9992                      |
| 0.7868 | 7400 | 0.1336        | 0.0895          | 0.9991                      |
| 0.7974 | 7500 | 0.1122        | 0.0889          | 0.9992                      |
| 0.8081 | 7600 | 0.1126        | 0.0884          | 0.9993                      |
| 0.8187 | 7700 | 0.116         | 0.0864          | 0.9992                      |
| 0.8293 | 7800 | 0.0991        | 0.0857          | 0.9992                      |
| 0.8400 | 7900 | 0.1091        | 0.0851          | 0.9992                      |
| 0.8506 | 8000 | 0.1052        | 0.0846          | 0.9993                      |
| 0.8612 | 8100 | 0.1105        | 0.0839          | 0.9992                      |
| 0.8719 | 8200 | 0.1101        | 0.0836          | 0.9992                      |
| 0.8825 | 8300 | 0.107         | 0.0832          | 0.9993                      |
| 0.8931 | 8400 | 0.0867        | 0.0827          | 0.9993                      |
| 0.9038 | 8500 | 0.0965        | 0.0823          | 0.9992                      |
| 0.9144 | 8600 | 0.1108        | 0.0817          | 0.9993                      |
| 0.9250 | 8700 | 0.1219        | 0.0814          | 0.9992                      |
| 0.9357 | 8800 | 0.1169        | 0.0809          | 0.9992                      |
| 0.9463 | 8900 | 0.0964        | 0.0805          | 0.9992                      |
| 0.9569 | 9000 | 0.0939        | 0.0804          | 0.9992                      |
| 0.9676 | 9100 | 0.0955        | 0.0803          | 0.9993                      |
| 0.9782 | 9200 | 0.1076        | 0.0800          | 0.9993                      |
| 0.9888 | 9300 | 0.1049        | 0.0798          | 0.9992                      |
| 0.9995 | 9400 | 0.0826        | 0.0798          | 0.9993                      |


### Framework Versions
- Python: 3.9.19
- Sentence Transformers: 4.0.2
- Transformers: 4.48.1
- PyTorch: 2.4.1+cu124
- Accelerate: 1.6.0
- Datasets: 2.21.0
- 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.*
-->