---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:8884
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
widget:
- source_sentence: De deur tussen twee kamers
  sentences:
  - Verschillende buren hebben hetzelfde probleem
  - Alle lampen in de gemeenschappelijke ruimtes
  - De scheidingsdeur
- source_sentence: De individuele CV
  sentences:
  - Er komt geen water uit de kraan
  - De centrale waterkraan
  - Mijn eigen CV-installatie
- source_sentence: De vloer- of wandtegels zitten niet vast
  sentences:
  - Het privé-buitenverblijf
  - Er zijn tegels losgekomen
  - Een auto staat in de weg om weg te rijden
- source_sentence: Barst in het glas
  sentences:
  - De hele VvE
  - Vaststaan door een foutgeparkeerde auto
  - Er is goedkeuring
- source_sentence: De sproeier van de douche
  sentences:
  - De deur naar buiten
  - Warmwatertankje in de keuken
  - De douchesproeier is kapot
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy
      value: 0.9908906882591093
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7341352105140686
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.9909547738693467
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7341352105140686
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.9840319361277445
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9979757085020243
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9955570949668978
      name: Cosine Ap
    - type: cosine_mcc
      value: 0.9818799573285504
      name: Cosine Mcc
---

# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2). 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/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 4328cf26390c98c5e3c738b4460a05b95f4911f5 -->
- **Maximum Sequence Length:** 64 tokens
- **Output Dimensionality:** 768 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': 64, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (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})
)
```

## 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("PrabalAryal/Sentence_Transformer_v0.0.1")
# Run inference
sentences = [
    'De sproeier van de douche',
    'De douchesproeier is kapot',
    'De deur naar buiten',
]
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.*
-->

## 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      |
|:--------------------------|:-----------|
| cosine_accuracy           | 0.9909     |
| cosine_accuracy_threshold | 0.7341     |
| cosine_f1                 | 0.991      |
| cosine_f1_threshold       | 0.7341     |
| cosine_precision          | 0.984      |
| cosine_recall             | 0.998      |
| **cosine_ap**             | **0.9956** |
| cosine_mcc                | 0.9819     |

<!--
## 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: 8,884 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                      | sentence_1                                                                       | label                                                          |
  |:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                          | string                                                                           | float                                                          |
  | details | <ul><li>min: 3 tokens</li><li>mean: 8.6 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.86 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                       | sentence_1                                | label            |
  |:-------------------------------------------------|:------------------------------------------|:-----------------|
  | <code>Het slot is kapot</code>                   | <code>Schade aan de sluiting</code>       | <code>1.0</code> |
  | <code>Ik kan er niet uit met de auto</code>      | <code>De uitrit is versperd</code>        | <code>1.0</code> |
  | <code>De afvoer van de wasmachine is stuk</code> | <code>Lekkende kranen of leidingen</code> | <code>0.0</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
- `num_train_epochs`: 8
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin

#### 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`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 8
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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
- `hub_revision`: None
- `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
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss | cosine_ap |
|:------:|:----:|:-------------:|:---------:|
| 0.1942 | 27   | -             | 0.8916    |
| 0.3885 | 54   | -             | 0.9339    |
| 0.5827 | 81   | -             | 0.9614    |
| 0.7770 | 108  | -             | 0.9740    |
| 0.9712 | 135  | -             | 0.9706    |
| 1.0    | 139  | -             | 0.9732    |
| 1.1655 | 162  | -             | 0.9763    |
| 1.3597 | 189  | -             | 0.9831    |
| 1.5540 | 216  | -             | 0.9845    |
| 1.7482 | 243  | -             | 0.9858    |
| 1.9424 | 270  | -             | 0.9886    |
| 2.0    | 278  | -             | 0.9896    |
| 2.1367 | 297  | -             | 0.9904    |
| 2.3309 | 324  | -             | 0.9900    |
| 2.5252 | 351  | -             | 0.9907    |
| 2.7194 | 378  | -             | 0.9921    |
| 2.9137 | 405  | -             | 0.9919    |
| 3.0    | 417  | -             | 0.9917    |
| 3.1079 | 432  | -             | 0.9933    |
| 3.3022 | 459  | -             | 0.9923    |
| 3.4964 | 486  | -             | 0.9911    |
| 3.5971 | 500  | 3.1664        | -         |
| 3.6906 | 513  | -             | 0.9936    |
| 3.8849 | 540  | -             | 0.9926    |
| 4.0    | 556  | -             | 0.9928    |
| 4.0791 | 567  | -             | 0.9931    |
| 4.2734 | 594  | -             | 0.9949    |
| 4.4676 | 621  | -             | 0.9940    |
| 4.6619 | 648  | -             | 0.9930    |
| 4.8561 | 675  | -             | 0.9932    |
| 5.0    | 695  | -             | 0.9935    |
| 5.0504 | 702  | -             | 0.9938    |
| 5.2446 | 729  | -             | 0.9950    |
| 5.4388 | 756  | -             | 0.9949    |
| 5.6331 | 783  | -             | 0.9948    |
| 5.8273 | 810  | -             | 0.9948    |
| 6.0    | 834  | -             | 0.9946    |
| 6.0216 | 837  | -             | 0.9945    |
| 6.2158 | 864  | -             | 0.9955    |
| 6.4101 | 891  | -             | 0.9955    |
| 6.6043 | 918  | -             | 0.9955    |
| 6.7986 | 945  | -             | 0.9956    |


### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 4.1.0
- Transformers: 4.53.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.9.0
- Datasets: 4.4.1
- Tokenizers: 0.21.2

## 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.*
-->