checkpoint-2772/README.md

---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2130621
- loss:ContrastiveLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
- source_sentence: Kim Chol-sam
  sentences:
  - Stankevich Sergey Nikolayevich
  - Kim Chin-So’k
  - Julen Lopetegui Agote
- source_sentence: دينا بنت عبد الحميد
  sentences:
  - Alexia van Amsberg
  - Anthony Nicholas Colin Maitland Biddulph, 5th Baron Biddulph
  - Dina bint Abdul-Hamíd
- source_sentence: Մուհամեդ բեն Նաիֆ Ալ Սաուդ
  sentences:
  - Karpov Anatoly Evgenyevich
  - GNPower Mariveles Coal Plant [former]
  - Muhammed bin Nayef bin Abdul Aziz Al Saud
- source_sentence: Edward Gnehm
  sentences:
  - Шауэрте, Хартмут
  - Ханзада Филипп, Эдинбург герцогі
  - AFX
- source_sentence: Schori i Lidingö
  sentences:
  - Yordan Canev
  - ကားပေါ့ အန်နာတိုလီ
  - BYSTROV, Mikhail Ivanovich
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: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2-name-matcher-original
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: sentence transformers paraphrase multilingual MiniLM L12 v2
      type: sentence-transformers-paraphrase-multilingual-MiniLM-L12-v2
    metrics:
    - type: cosine_accuracy
      value: 0.9885216725241056
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7183246612548828
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.9824706124974221
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7085607051849365
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.9782229269572558
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9867553479166427
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9971022799526896
      name: Cosine Ap
    - type: cosine_mcc
      value: 0.9739458779668466
      name: Cosine Mcc
---

# sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2-name-matcher-original

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:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0

### 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': 128, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Schori i Lidingö',
    'Yordan Canev',
    'ကားပေါ့ အန်နာတိုလီ',
]
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

* Dataset: `sentence-transformers-paraphrase-multilingual-MiniLM-L12-v2`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                    | Value      |
|:--------------------------|:-----------|
| cosine_accuracy           | 0.9885     |
| cosine_accuracy_threshold | 0.7183     |
| cosine_f1                 | 0.9825     |
| cosine_f1_threshold       | 0.7086     |
| cosine_precision          | 0.9782     |
| cosine_recall             | 0.9868     |
| **cosine_ap**             | **0.9971** |
| cosine_mcc                | 0.9739     |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 2,130,621 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                        | sentence2                                                                        | label                                                          |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | float                                                          |
  | details | <ul><li>min: 3 tokens</li><li>mean: 9.32 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.16 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.34</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                         | sentence2                           | label            |
  |:----------------------------------|:------------------------------------|:-----------------|
  | <code>캐스린 설리번</code>              | <code>Kathryn D. Sullivanová</code> | <code>1.0</code> |
  | <code>ଶିବରାଜ ଅଧାଲରାଓ ପାଟିଲ</code> | <code>Aleksander Lubocki</code>     | <code>0.0</code> |
  | <code>Пырванов, Георги</code>     | <code>アナトーリー・セルジュコフ</code>          | <code>0.0</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: 2,663,276 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         | label                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | float                                                          |
  | details | <ul><li>min: 3 tokens</li><li>mean: 9.34 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.11 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.33</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                             | sentence2                              | label            |
  |:--------------------------------------|:---------------------------------------|:-----------------|
  | <code>Ева Херман</code>               | <code>I Xuan Karlos</code>             | <code>0.0</code> |
  | <code>Кличков Андрій Євгенович</code> | <code>Андрэй Яўгенавіч Клычкоў</code>  | <code>1.0</code> |
  | <code>Кинах А.</code>                 | <code>Senator John Hickenlooper</code> | <code>0.0</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`: epoch
- `per_device_train_batch_size`: 768
- `per_device_eval_batch_size`: 768
- `gradient_accumulation_steps`: 4
- `learning_rate`: 3e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `optim`: adafactor

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 768
- `per_device_eval_batch_size`: 768
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 4
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `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`: True
- `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}
- `tp_size`: 0
- `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`: adafactor
- `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
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | sentence-transformers-paraphrase-multilingual-MiniLM-L12-v2_cosine_ap |
|:------:|:----:|:-------------:|:---------------:|:---------------------------------------------------------------------:|
| -1     | -1   | -             | -               | 0.7140                                                                |
| 0.7207 | 500  | 0.038         | -               | -                                                                     |
| 0.9989 | 693  | -             | 0.0028          | 0.9911                                                                |
| 1.4425 | 1000 | 0.0128        | -               | -                                                                     |
| 1.9989 | 1386 | -             | 0.0021          | 0.9956                                                                |
| 2.1643 | 1500 | 0.0084        | -               | -                                                                     |
| 2.8850 | 2000 | 0.0065        | -               | -                                                                     |
| 2.9989 | 2079 | -             | 0.0015          | 0.9968                                                                |
| 3.6068 | 2500 | 0.0056        | -               | -                                                                     |
| 3.9989 | 2772 | -             | 0.0014          | 0.9971                                                                |


### Framework Versions
- Python: 3.12.9
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.7.0+cu126
- Accelerate: 1.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1

## 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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