---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:33054
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
- source_sentence: Het communicatiesysteem of de belplaat
  sentences:
  - Nee, het is een ander soort probleem
  - Sluiting
  - Alle lampen op de loopbruggen zijn kapot
- source_sentence: Appartement
  sentences:
  - De bewoners van de bovenliggende woning
  - trap
  - Onveilig
- source_sentence: afzuiging
  sentences:
  - lucht afvoer
  - weg rijden
  - Verloren
- source_sentence: Buis
  sentences:
  - Zelf
  - De verlichting van alle loopbruggen werkt niet
  - Klem
- source_sentence: De verlichting van de gang werkt niet
  sentences:
  - Donker
  - Het raamglas is gebroken
  - Vast
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-MiniLM-L12-v2
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy
      value: 0.9692433315187806
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7461047172546387
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.9689816085643701
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7461047172546387
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.9772978959025471
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9608056614044638
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9924589219952974
      name: Cosine Ap
    - type: cosine_mcc
      value: 0.9386203213441777
      name: Cosine Mcc
---

# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d -->
- **Maximum Sequence Length:** 64 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': 64, '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})
)
```

## 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_v0.0.3")
# Run inference
sentences = [
    'De verlichting van de gang werkt niet',
    'Donker',
    'Het raamglas is gebroken',
]
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]
```

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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      |
|:--------------------------|:-----------|
| cosine_accuracy           | 0.9692     |
| cosine_accuracy_threshold | 0.7461     |
| cosine_f1                 | 0.969      |
| cosine_f1_threshold       | 0.7461     |
| cosine_precision          | 0.9773     |
| cosine_recall             | 0.9608     |
| **cosine_ap**             | **0.9925** |
| cosine_mcc                | 0.9386     |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 33,054 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: 7.39 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.78 tokens</li><li>max: 16 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>Defect</code>                                      | <code>Lek</code>             | <code>1.0</code> |
  | <code>Dakbedekking</code>                                | <code>Weggewaaid</code>      | <code>1.0</code> |
  | <code>Het slot werkt niet, de deur is geblokkeerd</code> | <code>druppende kraan</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`: 10
- `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`: 10
- `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 |
|:------:|:----:|:-------------:|:---------:|
| 0.1992 | 103  | -             | 0.7450    |
| 0.3985 | 206  | -             | 0.8186    |
| 0.5977 | 309  | -             | 0.8716    |
| 0.7969 | 412  | -             | 0.9074    |
| 0.9671 | 500  | 4.302         | -         |
| 0.9961 | 515  | -             | 0.9307    |
| 1.0    | 517  | -             | 0.9315    |
| 1.1954 | 618  | -             | 0.9434    |
| 1.3946 | 721  | -             | 0.9555    |
| 1.5938 | 824  | -             | 0.9637    |
| 1.7930 | 927  | -             | 0.9676    |
| 1.9342 | 1000 | 3.5031        | -         |
| 1.9923 | 1030 | -             | 0.9725    |
| 2.0    | 1034 | -             | 0.9731    |
| 2.1915 | 1133 | -             | 0.9741    |
| 2.3907 | 1236 | -             | 0.9753    |
| 2.5899 | 1339 | -             | 0.9802    |
| 2.7892 | 1442 | -             | 0.9806    |
| 2.9014 | 1500 | 3.2728        | -         |
| 2.9884 | 1545 | -             | 0.9843    |
| 3.0    | 1551 | -             | 0.9846    |
| 3.1876 | 1648 | -             | 0.9839    |
| 3.3868 | 1751 | -             | 0.9844    |
| 3.5861 | 1854 | -             | 0.9847    |
| 3.7853 | 1957 | -             | 0.9868    |
| 3.8685 | 2000 | 3.1567        | -         |
| 3.9845 | 2060 | -             | 0.9882    |
| 4.0    | 2068 | -             | 0.9876    |
| 4.1838 | 2163 | -             | 0.9880    |
| 4.3830 | 2266 | -             | 0.9880    |
| 4.5822 | 2369 | -             | 0.9887    |
| 4.7814 | 2472 | -             | 0.9887    |
| 4.8356 | 2500 | 3.0525        | -         |
| 4.9807 | 2575 | -             | 0.9899    |
| 5.0    | 2585 | -             | 0.9901    |
| 5.1799 | 2678 | -             | 0.9896    |
| 5.3791 | 2781 | -             | 0.9894    |
| 5.5783 | 2884 | -             | 0.9904    |
| 5.7776 | 2987 | -             | 0.9906    |
| 5.8027 | 3000 | 3.0061        | -         |
| 5.9768 | 3090 | -             | 0.9910    |
| 6.0    | 3102 | -             | 0.9911    |
| 6.1760 | 3193 | -             | 0.9904    |
| 6.3752 | 3296 | -             | 0.9907    |
| 6.5745 | 3399 | -             | 0.9915    |
| 6.7698 | 3500 | 2.9548        | -         |
| 6.7737 | 3502 | -             | 0.9915    |
| 6.9729 | 3605 | -             | 0.9917    |
| 7.0    | 3619 | -             | 0.9917    |
| 7.1721 | 3708 | -             | 0.9912    |
| 7.3714 | 3811 | -             | 0.9915    |
| 7.5706 | 3914 | -             | 0.9916    |
| 7.7369 | 4000 | 2.9023        | -         |
| 7.7698 | 4017 | -             | 0.9917    |
| 7.9691 | 4120 | -             | 0.9919    |
| 8.0    | 4136 | -             | 0.9921    |
| 8.1683 | 4223 | -             | 0.9919    |
| 8.3675 | 4326 | -             | 0.9919    |
| 8.5667 | 4429 | -             | 0.9925    |


### 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}
}
```

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