sentence-transformers safetensors mpnet sentence-similarity feature-extraction generated_from_trainer dataset_size:672 loss:ContrastiveLoss arxiv:1908.10084 base_model:sentence-transformers/multi-qa-mpnet-base-dot-v1 base_model:finetune:sentence-transformers/multi-qa-mpnet-base-dot-v1 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: sentence-transformers/multi-qa-mpnet-base-dot-v1
library_name: sentence-transformers
metrics:
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- dot_mcc
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:672
- loss:ContrastiveLoss
widget:
- source_sentence: '

    Animals may not be allowed onto beds or other furniture, which serves for

    guests. It is not permitted to use baths, showers or washbasins for bathing or

    washing animals.'
  sentences:
  - '

    Please advise of any special needs such as high-chairs and sleeping cots.'
  - '

    Animals may not be allowed onto beds or other furniture, which serves for

    guests. It is not permitted to use baths, showers or washbasins for bathing or

    washing animals.'
  - '

    It is strongly advised that you arrange adequate insurance cover such as cancellation
    due to illness,

    accident or injury, personal accident and personal liability, loss of or damage
    to baggage and sport

    equipment (Note that is not an exhaustive list). We will not be responsible or
    liable if you fail to take

    adequate insurance cover or none at all.'
- source_sentence: 'Owners are responsible for ensuring that animals are kept quiet
    between the

    hours of 10:00 pm and 06:00 am. In the case of failure to abide by this

    regulation the guest may be asked to leave the hotel without a refund of the

    price of the night''s accommodation.'
  sentences:
  - '

    Visitors are not allowed in the rooms and must be entertained in the lounges and/or
    other public areas

    provided.'
  - 'To ensure the safety and comfort of everyone in the hotel, the Management

    reserves the right to terminate the accommodation of guests who fail to comply

    with the following rules and regulations.'
  - 'Owners are responsible for ensuring that animals are kept quiet between the

    hours of 10:00 pm and 06:00 am. In the case of failure to abide by this

    regulation the guest may be asked to leave the hotel without a refund of the

    price of the night''s accommodation.'
- source_sentence: '

    We ask all guests to behave in such a way that they do not disturb other guests
    and the neighborhood.

    The hotel staff is authorized to refuse services to a person who violates this
    rule.'
  sentences:
  - '

    Please take note of the limitation specified for the room you have booked.

    If such number is exceeded, whether temporarily or over-night, we reserve the
    right to do one or more of

    the following: cancel your booking; retain all the monies you''ve paid; request
    you to vacate your room(s)

    forthwith, charge a higher rate for the room or recover all monies due.'
  - '

    We ask all guests to behave in such a way that they do not disturb other guests
    and the neighborhood.

    The hotel staff is authorized to refuse services to a person who violates this
    rule.'
  - 'We will only deal with your information as indicated in the booking/reservation
    and we will only process your

    personal information (both terms as defined in the Protection of Personal Information
    Act, act 4 of 2013 [''the

    POPIA''] and the European Union General Data Protection Regulation – (''GDPR'')
    and any Special Personal

    Information (as defined in the GDPR & POPIA), which processing includes amongst
    others the ''collecting,

    storing and dissemination'' of your personal information (as defined in GDPR &
    POPIA).'
- source_sentence: '

    All articles stored in the luggage storage room are received at the owner’s own
    risk.'
  sentences:
  - "\n Unregistered visitors are not permitted to enter guest rooms or other areas\
    \ of\nthe hotel. An additional fee for unregistered guests will be charged to\
    \ the\naccount of the guest(s) registered to the room."
  - 'Please advise us if you anticipate arriving late as bookings will be cancelled
    by 17:00 on the day of arrival,

    unless we have been so notified.'
  - '

    All articles stored in the luggage storage room are received at the owner’s own
    risk.'
- source_sentence: ' In the event of a disturbance, one polite request (warning) will

    be given to reduce the noise. If our request is not followed, the guest will be
    asked to leave

    the hotel without refund and may be charged Guest Compensation Disturbance Fee.'
  sentences:
  - '

    Without limiting the generality of the aforementioned, it applies to pay-to-view
    TV programmes or videos, as

    well as telephone calls or any other expenses of a similar nature that is made
    from your room, you will be

    deemed to be the contracting party.'
  - 'Pets are not allowed in the restaurant during breakfast time

    (7:00 – 10:30) for hygienic reasons due to the breakfast’s buffet style. An

    exception is the case when the hotel terrace is open, as pets can be taken to

    the terrace through the hotel''s main entrance and they can stay there during

    breakfast.'
  - ' In the event of a disturbance, one polite request (warning) will

    be given to reduce the noise. If our request is not followed, the guest will be
    asked to leave

    the hotel without refund and may be charged Guest Compensation Disturbance Fee.'
model-index:
- name: SentenceTransformer based on sentence-transformers/multi-qa-mpnet-base-dot-v1
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: dot_accuracy
      value: 0.6745562130177515
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 49.0201301574707
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.4932735426008969
      name: Dot F1
    - type: dot_f1_threshold
      value: 35.02415466308594
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.32934131736526945
      name: Dot Precision
    - type: dot_recall
      value: 0.9821428571428571
      name: Dot Recall
    - type: dot_ap
      value: 0.3294144882113245
      name: Dot Ap
    - type: dot_mcc
      value: -0.03920743101752848
      name: Dot Mcc
---

# SentenceTransformer based on sentence-transformers/multi-qa-mpnet-base-dot-v1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/multi-qa-mpnet-base-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-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/multi-qa-mpnet-base-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1) <!-- at revision 4633e80e17ea975bc090c97b049da26062b054d3 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Dot Product
<!-- - **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: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

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

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

# Download from the 🤗 Hub
model = SentenceTransformer("Marco127/Argu_T2")
# Run inference
sentences = [
    ' In the event of a disturbance, one polite request (warning) will\nbe given to reduce the noise. If our request is not followed, the guest will be asked to leave\nthe hotel without refund and may be charged Guest Compensation Disturbance Fee.',
    ' In the event of a disturbance, one polite request (warning) will\nbe given to reduce the noise. If our request is not followed, the guest will be asked to leave\nthe hotel without refund and may be charged Guest Compensation Disturbance Fee.',
    '\nWithout limiting the generality of the aforementioned, it applies to pay-to-view TV programmes or videos, as\nwell as telephone calls or any other expenses of a similar nature that is made from your room, you will be\ndeemed to be the contracting party.',
]
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]
```

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## Evaluation

### Metrics

#### Binary Classification

* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                 | Value      |
|:-----------------------|:-----------|
| dot_accuracy           | 0.6746     |
| dot_accuracy_threshold | 49.0201    |
| dot_f1                 | 0.4933     |
| dot_f1_threshold       | 35.0242    |
| dot_precision          | 0.3293     |
| dot_recall             | 0.9821     |
| **dot_ap**             | **0.3294** |
| dot_mcc                | -0.0392    |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 672 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 672 samples:
  |         | sentence1                                                                           | sentence2                                                                           | label                                           |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                              | string                                                                              | int                                             |
  | details | <ul><li>min: 11 tokens</li><li>mean: 48.63 tokens</li><li>max: 156 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 48.63 tokens</li><li>max: 156 tokens</li></ul> | <ul><li>0: ~66.67%</li><li>1: ~33.33%</li></ul> |
* Samples:
  | sentence1                                                                                                                                                                           | sentence2                                                                                                                                                                           | label          |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code><br>The pets can not be left without supervision if there is a risk of causing any<br>damage or might disturb other guests.</code>                                            | <code><br>The pets can not be left without supervision if there is a risk of causing any<br>damage or might disturb other guests.</code>                                            | <code>0</code> |
  | <code><br>Any guest in violation of these rules may be asked to leave the hotel with no refund. Extra copies of these<br>rules are available at the Front Desk upon request.</code> | <code><br>Any guest in violation of these rules may be asked to leave the hotel with no refund. Extra copies of these<br>rules are available at the Front Desk upon request.</code> | <code>0</code> |
  | <code><br>Consuming the products from the minibar involves additional costs. You can find the<br>prices in the kitchen area.</code>                                                 | <code><br>Consuming the products from the minibar involves additional costs. You can find the<br>prices in the kitchen area.</code>                                                 | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
  ```json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset

* Size: 169 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 169 samples:
  |         | sentence1                                                                           | sentence2                                                                           | label                                           |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                              | string                                                                              | int                                             |
  | details | <ul><li>min: 13 tokens</li><li>mean: 46.01 tokens</li><li>max: 156 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 46.01 tokens</li><li>max: 156 tokens</li></ul> | <ul><li>0: ~66.86%</li><li>1: ~33.14%</li></ul> |
* Samples:
  | sentence1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | label          |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code><br>I understand and accept that the BON Hotels Group collects the personal information ("personal<br>information") of all persons in my party for purposes of loyalty programmes and special offers. I, on behalf of<br>all in my party, expressly consent and grant permission to the BON Hotels Group to: -<br>collect, collate, process, study and use the personal information; and<br>communicate directly with me/us from time to time, unless I have stated to the contrary below.</code> | <code><br>I understand and accept that the BON Hotels Group collects the personal information ("personal<br>information") of all persons in my party for purposes of loyalty programmes and special offers. I, on behalf of<br>all in my party, expressly consent and grant permission to the BON Hotels Group to: -<br>collect, collate, process, study and use the personal information; and<br>communicate directly with me/us from time to time, unless I have stated to the contrary below.</code> | <code>0</code> |
  | <code>However, in lieu of the above, any such goods will only be kept by us for 6 (six) months. At the end of which<br>period, we reserve the right in our sole discretion to dispose thereof and you will have no right of recourse<br>against us.</code>                                                                                                                                                                                                                                              | <code>However, in lieu of the above, any such goods will only be kept by us for 6 (six) months. At the end of which<br>period, we reserve the right in our sole discretion to dispose thereof and you will have no right of recourse<br>against us.</code>                                                                                                                                                                                                                                              | <code>0</code> |
  | <code> In cases where the hotel<br>suffers damage (either physical, or moral) due to the guests’ violation of the above rules, it<br>may charge a compensation fee in proportion to the damage. Moral damage may be for<br>example disturbing other guests, thus ruining the reputation of the hotel.</code>                                                                                                                                                                                            | <code> In cases where the hotel<br>suffers damage (either physical, or moral) due to the guests’ violation of the above rules, it<br>may charge a compensation fee in proportion to the damage. Moral damage may be for<br>example disturbing other guests, thus ruining the reputation of the hotel.</code>                                                                                                                                                                                            | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
  ```json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 5
- `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`: 5e-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`: 5
- `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 | dot_ap |
|:------:|:----:|:-------------:|:---------------:|:------:|
| -1     | -1   | -             | -               | 0.3294 |
| 2.3333 | 100  | 0.0298        | 0.0865          | -      |
| 4.6905 | 200  | 0.0241        | 0.0865          | -      |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- 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",
}
```

#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}
```

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