README.md

---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:8408
- loss:CosineSimilarityLoss
widget:
- source_sentence: president
  sentences:
  - assistante de banque priv e banco santander rio
  - worldwide executive vice president corindus a siemens healthineers company
  - soporte t cnico superior
- source_sentence: chief business strategy officer
  sentences:
  - sub jefe
  - analista senior recursos humanos sales staff and logistics
  - subgerente sostenibilidad y hseq
- source_sentence: gerente de planificación
  sentences:
  - analista de soporte web
  - director
  - gestion calidad
- source_sentence: global human resources leader
  sentences:
  - director manufacturing engineering
  - quality specialist
  - asesoramiento para comprar inmuebles en uruguay paraguay espa a y usa
- source_sentence: commercial manager
  sentences:
  - jefe de turno planta envasado de vinos
  - gerente de operaciones
  - vice president of finance americas
---

# 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 bf3bf13ab40c3157080a7ab344c831b9ad18b5eb -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 tokens
- **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': 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 = [
    'commercial manager',
    'gerente de operaciones',
    'vice president of finance americas',
]
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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You can finetune this model on your own dataset.

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 8,408 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: 6.2 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 7.75 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.06</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                              | sentence_1                                                                    | label            |
  |:----------------------------------------|:------------------------------------------------------------------------------|:-----------------|
  | <code>strategic planning manager</code> | <code>senior brand manager uap southern cone & personal care cdm chile</code> | <code>0.0</code> |
  | <code>director de planificacion</code>  | <code>key account manager tiendas paris</code>                                | <code>0.0</code> |
  | <code>gerente general</code>            | <code>analista de cobranza</code>                                             | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

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

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 50
- `multi_dataset_batch_sampler`: round_robin

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `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
- `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`: 50
- `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`: False
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch   | Step  | Training Loss |
|:-------:|:-----:|:-------------:|
| 0.9506  | 500   | 0.0434        |
| 1.9011  | 1000  | 0.0135        |
| 2.8517  | 1500  | 0.0072        |
| 3.8023  | 2000  | 0.0056        |
| 4.7529  | 2500  | 0.0044        |
| 5.7034  | 3000  | 0.0038        |
| 6.6540  | 3500  | 0.0034        |
| 7.6046  | 4000  | 0.0032        |
| 8.5551  | 4500  | 0.0029        |
| 9.5057  | 5000  | 0.0028        |
| 10.4563 | 5500  | 0.0026        |
| 11.4068 | 6000  | 0.0025        |
| 12.3574 | 6500  | 0.0026        |
| 13.3080 | 7000  | 0.0023        |
| 14.2586 | 7500  | 0.0023        |
| 15.2091 | 8000  | 0.0023        |
| 16.1597 | 8500  | 0.0022        |
| 17.1103 | 9000  | 0.0021        |
| 18.0608 | 9500  | 0.0019        |
| 19.0114 | 10000 | 0.0021        |
| 19.9620 | 10500 | 0.0019        |
| 20.9125 | 11000 | 0.0019        |
| 21.8631 | 11500 | 0.0016        |
| 22.8137 | 12000 | 0.0018        |
| 23.7643 | 12500 | 0.0018        |
| 24.7148 | 13000 | 0.0018        |
| 25.6654 | 13500 | 0.0016        |
| 26.6160 | 14000 | 0.0017        |
| 27.5665 | 14500 | 0.0016        |
| 28.5171 | 15000 | 0.0016        |
| 29.4677 | 15500 | 0.0016        |
| 30.4183 | 16000 | 0.0016        |
| 31.3688 | 16500 | 0.0019        |
| 32.3194 | 17000 | 0.0018        |
| 33.2700 | 17500 | 0.0017        |
| 34.2205 | 18000 | 0.0016        |
| 35.1711 | 18500 | 0.0016        |
| 36.1217 | 19000 | 0.0016        |
| 37.0722 | 19500 | 0.0015        |
| 38.0228 | 20000 | 0.0012        |
| 38.9734 | 20500 | 0.0015        |
| 39.9240 | 21000 | 0.0015        |
| 40.8745 | 21500 | 0.0013        |
| 41.8251 | 22000 | 0.0014        |
| 42.7757 | 22500 | 0.0014        |
| 43.7262 | 23000 | 0.0014        |
| 44.6768 | 23500 | 0.0013        |
| 45.6274 | 24000 | 0.0012        |
| 46.5779 | 24500 | 0.0014        |
| 47.5285 | 25000 | 0.0012        |
| 48.4791 | 25500 | 0.0013        |
| 49.4297 | 26000 | 0.0013        |


### Framework Versions
- Python: 3.8.5
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- Tokenizers: 0.19.1

## 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",
}
```

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