README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:68828
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
- source_sentence: Men is de toegangssleutels verloren
  sentences:
  - De centrale verwarming
  - niet dringend
  - Weg
- source_sentence: De bovenste constructie
  sentences:
  - Voldoende warm water in de hele woning
  - daklekkage
  - lek in kraan
- source_sentence: De box in het souterrain
  sentences:
  - Vloer
  - lift niet
  - Nood uitgang
- source_sentence: balkon
  sentences:
  - de brievenbus
  - uitgang garage dicht
  - afvoer de douche
- source_sentence: De deur naar de kelderboxen is stuk
  sentences:
  - deur met dranger
  - De beugel om de plek vrij te houden
  - kelderboxen deur
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: 0.982086820083682
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.733125627040863
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.9821498371335505
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.733125627040863
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.9787068293949623
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9856171548117155
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9972864020390366
      name: Cosine Ap
    - type: cosine_mcc
      value: 0.964197674565882
      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_v0.0.4")
# Run inference
sentences = [
    'De deur naar de kelderboxen is stuk',
    'kelderboxen deur',
    'deur met dranger',
]
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

#### 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           | 0.9821     |
| cosine_accuracy_threshold | 0.7331     |
| cosine_f1                 | 0.9821     |
| cosine_f1_threshold       | 0.7331     |
| cosine_precision          | 0.9787     |
| cosine_recall             | 0.9856     |
| **cosine_ap**             | **0.9973** |
| cosine_mcc                | 0.9642     |

<!--
## 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: 68,828 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: 7.03 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.41 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                            | sentence_1               | label            |
  |:------------------------------------------------------|:-------------------------|:-----------------|
  | <code>De sluiting van de toegangspoort is stuk</code> | <code>slot defect</code> | <code>1.0</code> |
  | <code>Woning</code>                                   | <code>trapafgang</code>  | <code>0.0</code> |
  | <code>De sleutels zijn kwijt</code>                   | <code>Nie</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 |
|:------:|:-----:|:-------------:|:---------:|
| 0.1998 | 215   | -             | 0.7638    |
| 0.3996 | 430   | -             | 0.8723    |
| 0.4647 | 500   | 4.4585        | -         |
| 0.5994 | 645   | -             | 0.9176    |
| 0.7993 | 860   | -             | 0.9475    |
| 0.9294 | 1000  | 3.6015        | -         |
| 0.9991 | 1075  | -             | 0.9595    |
| 1.0    | 1076  | -             | 0.9593    |
| 1.1989 | 1290  | -             | 0.9705    |
| 1.3941 | 1500  | 3.3729        | -         |
| 1.3987 | 1505  | -             | 0.9793    |
| 1.5985 | 1720  | -             | 0.9818    |
| 1.7983 | 1935  | -             | 0.9854    |
| 1.8587 | 2000  | 3.2631        | -         |
| 1.9981 | 2150  | -             | 0.9866    |
| 2.0    | 2152  | -             | 0.9866    |
| 2.1980 | 2365  | -             | 0.9890    |
| 2.3234 | 2500  | 3.1295        | -         |
| 2.3978 | 2580  | -             | 0.9884    |
| 2.5976 | 2795  | -             | 0.9916    |
| 2.7881 | 3000  | 3.0907        | -         |
| 2.7974 | 3010  | -             | 0.9916    |
| 2.9972 | 3225  | -             | 0.9922    |
| 3.0    | 3228  | -             | 0.9922    |
| 3.1970 | 3440  | -             | 0.9928    |
| 3.2528 | 3500  | 3.0105        | -         |
| 3.3968 | 3655  | -             | 0.9932    |
| 3.5967 | 3870  | -             | 0.9937    |
| 3.7175 | 4000  | 2.977         | -         |
| 3.7965 | 4085  | -             | 0.9939    |
| 3.9963 | 4300  | -             | 0.9944    |
| 4.0    | 4304  | -             | 0.9945    |
| 4.1822 | 4500  | 2.9488        | -         |
| 4.1961 | 4515  | -             | 0.9947    |
| 4.3959 | 4730  | -             | 0.9950    |
| 4.5957 | 4945  | -             | 0.9952    |
| 4.6468 | 5000  | 2.914         | -         |
| 4.7955 | 5160  | -             | 0.9954    |
| 4.9954 | 5375  | -             | 0.9956    |
| 5.0    | 5380  | -             | 0.9956    |
| 5.1115 | 5500  | 2.8927        | -         |
| 5.1952 | 5590  | -             | 0.9960    |
| 5.3950 | 5805  | -             | 0.9959    |
| 5.5762 | 6000  | 2.8505        | -         |
| 5.5948 | 6020  | -             | 0.9963    |
| 5.7946 | 6235  | -             | 0.9961    |
| 5.9944 | 6450  | -             | 0.9962    |
| 6.0    | 6456  | -             | 0.9962    |
| 6.0409 | 6500  | 2.8462        | -         |
| 6.1942 | 6665  | -             | 0.9963    |
| 6.3941 | 6880  | -             | 0.9965    |
| 6.5056 | 7000  | 2.8024        | -         |
| 6.5939 | 7095  | -             | 0.9967    |
| 6.7937 | 7310  | -             | 0.9969    |
| 6.9703 | 7500  | 2.8184        | -         |
| 6.9935 | 7525  | -             | 0.9968    |
| 7.0    | 7532  | -             | 0.9967    |
| 7.1933 | 7740  | -             | 0.9967    |
| 7.3931 | 7955  | -             | 0.9967    |
| 7.4349 | 8000  | 2.7761        | -         |
| 7.5929 | 8170  | -             | 0.9968    |
| 7.7928 | 8385  | -             | 0.9969    |
| 7.8996 | 8500  | 2.7736        | -         |
| 7.9926 | 8600  | -             | 0.9970    |
| 8.0    | 8608  | -             | 0.9971    |
| 8.1924 | 8815  | -             | 0.9972    |
| 8.3643 | 9000  | 2.7627        | -         |
| 8.3922 | 9030  | -             | 0.9970    |
| 8.5920 | 9245  | -             | 0.9972    |
| 8.7918 | 9460  | -             | 0.9972    |
| 8.8290 | 9500  | 2.7604        | -         |
| 8.9916 | 9675  | -             | 0.9972    |
| 9.0    | 9684  | -             | 0.9972    |
| 9.1914 | 9890  | -             | 0.9971    |
| 9.2937 | 10000 | 2.7467        | -         |
| 9.3913 | 10105 | -             | 0.9972    |
| 9.5911 | 10320 | -             | 0.9973    |
| 9.7584 | 10500 | 2.7441        | -         |
| 9.7909 | 10535 | -             | 0.9973    |


### 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.*
-->