---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:91044
- loss:CosineSimilarityLoss
base_model: hiiamsid/sentence_similarity_spanish_es
widget:
- source_sentence: ¿Cuánto debo pagar por la llave con código VA34P?
  sentences:
  - ¿La llave HY5P pertenece a qué marca?
  - ¿Cuál es el valor actual de VA34P JMA?
  - ¿Cuánto cuesta la llave ME4P?
- source_sentence: ¿Me puedes decir cuánto vale TOYOTA (TOY43) 2 BOTONES HILUX TOYO04?
  sentences:
  - ¿Cuánto debo pagar por la llave con código CAB?
  - ¿Qué llave tiene el código TOYO04?
  - ¿Qué código tiene la LETRA D?
- source_sentence: ¿Tienen disponible la NISSAN DER LOGO?
  sentences:
  - ¿Cuál es el valor actual de NISSAN DER LOGO?
  - ¿La llave MZ13A pertenece a qué marca?
  - ¿CARRIAGE TENSION SPRING 017-24 tiene un precio accesible?
- source_sentence: ¿Qué código tiene la GLOBE DER PEQ MKS?
  sentences:
  - ¿Qué llave tiene el código YM021?
  - ¿Qué llave tiene el código MER03?
  - ¿Cuánto cuesta la llave VP095?
- source_sentence: ¿Qué modelo corresponde al código YP107?
  sentences:
  - ¿La llave TE4 pertenece a qué marca?
  - ¿Me puedes decir cuánto vale FANAL?
  - ¿Cuánto cuesta la llave P123VE?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on hiiamsid/sentence_similarity_spanish_es

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [hiiamsid/sentence_similarity_spanish_es](https://huggingface.co/hiiamsid/sentence_similarity_spanish_es). 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:** [hiiamsid/sentence_similarity_spanish_es](https://huggingface.co/hiiamsid/sentence_similarity_spanish_es) <!-- at revision 66ab46adac3910bb6ea6085b962a25e49513b981 -->
- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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("sd-dreambooth-library/mks-similarity")
# Run inference
sentences = [
    '¿Qué modelo corresponde al código YP107?',
    '¿Cuánto cuesta la llave P123VE?',
    '¿La llave TE4 pertenece a qué marca?',
]
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]
```

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 91,044 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: 10 tokens</li><li>mean: 17.16 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 17.26 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                          | sentence2                                                      | label            |
  |:-------------------------------------------------------------------|:---------------------------------------------------------------|:-----------------|
  | <code>¿CY1 HELLO KITTY CORAZONES tiene un precio accesible?</code> | <code>¿Cuánto cuesta la llave CY43?</code>                     | <code>0.0</code> |
  | <code>¿Qué modelo corresponde al código OP12?</code>               | <code>¿Me puedes decir cuánto vale CHEVROLET GM29?</code>      | <code>0.0</code> |
  | <code>¿YALE PERSONAJE HULK tiene un precio accesible?</code>       | <code>¿Cuánto debo pagar por la llave con código YP117?</code> | <code>1.0</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"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset

* Size: 10,116 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: 10 tokens</li><li>mean: 17.08 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 17.33 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                     | sentence2                                                    | label            |
  |:--------------------------------------------------------------|:-------------------------------------------------------------|:-----------------|
  | <code>¿Cuál es el precio de la AM3 AMERICAN LOCK?</code>      | <code>¿Cuánto debo pagar por la llave con código AM3?</code> | <code>1.0</code> |
  | <code>¿Cuánto debo pagar por la llave con código MAS9?</code> | <code>¿La llave MAS9 pertenece a qué marca?</code>           | <code>1.0</code> |
  | <code>¿La llave YP113 pertenece a qué marca?</code>           | <code>¿Qué llave tiene el código E029?</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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 2
- `warmup_ratio`: 0.1
- `fp16`: True

#### 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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 1.0
- `num_train_epochs`: 2
- `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`: 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}
- `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`: 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
- `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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0351 | 100  | 0.1663        | -               |
| 0.0703 | 200  | 0.1023        | -               |
| 0.1054 | 300  | 0.0807        | -               |
| 0.1405 | 400  | 0.0723        | -               |
| 0.1757 | 500  | 0.0614        | 0.0535          |
| 0.2108 | 600  | 0.0569        | -               |
| 0.2460 | 700  | 0.052         | -               |
| 0.2811 | 800  | 0.0382        | -               |
| 0.3162 | 900  | 0.0408        | -               |
| 0.3514 | 1000 | 0.0358        | 0.0329          |
| 0.3865 | 1100 | 0.0353        | -               |
| 0.4216 | 1200 | 0.032         | -               |
| 0.4568 | 1300 | 0.0303        | -               |
| 0.4919 | 1400 | 0.0275        | -               |
| 0.5271 | 1500 | 0.0263        | 0.0223          |
| 0.5622 | 1600 | 0.0237        | -               |
| 0.5973 | 1700 | 0.0215        | -               |
| 0.6325 | 1800 | 0.0233        | -               |
| 0.6676 | 1900 | 0.0198        | -               |
| 0.7027 | 2000 | 0.022         | 0.0163          |
| 0.7379 | 2100 | 0.0185        | -               |
| 0.7730 | 2200 | 0.0178        | -               |
| 0.8082 | 2300 | 0.0168        | -               |
| 0.8433 | 2400 | 0.018         | -               |
| 0.8784 | 2500 | 0.0158        | 0.0127          |
| 0.9136 | 2600 | 0.0141        | -               |
| 0.9487 | 2700 | 0.015         | -               |
| 0.9838 | 2800 | 0.0131        | -               |
| 1.0190 | 2900 | 0.0117        | -               |
| 1.0541 | 3000 | 0.0106        | 0.0100          |
| 1.0892 | 3100 | 0.0082        | -               |
| 1.1244 | 3200 | 0.0088        | -               |
| 1.1595 | 3300 | 0.0084        | -               |
| 1.1947 | 3400 | 0.0087        | -               |
| 1.2298 | 3500 | 0.0093        | 0.0079          |
| 1.2649 | 3600 | 0.0106        | -               |
| 1.3001 | 3700 | 0.0097        | -               |
| 1.3352 | 3800 | 0.0074        | -               |
| 1.3703 | 3900 | 0.0072        | -               |
| 1.4055 | 4000 | 0.0094        | 0.0067          |
| 1.4406 | 4100 | 0.0062        | -               |
| 1.4758 | 4200 | 0.0072        | -               |
| 1.5109 | 4300 | 0.0081        | -               |
| 1.5460 | 4400 | 0.0075        | -               |
| 1.5812 | 4500 | 0.0071        | 0.0059          |
| 1.6163 | 4600 | 0.0049        | -               |
| 1.6514 | 4700 | 0.0064        | -               |
| 1.6866 | 4800 | 0.0072        | -               |
| 1.7217 | 4900 | 0.0075        | -               |
| 1.7569 | 5000 | 0.0062        | 0.0052          |
| 1.7920 | 5100 | 0.0061        | -               |
| 1.8271 | 5200 | 0.0059        | -               |
| 1.8623 | 5300 | 0.0062        | -               |
| 1.8974 | 5400 | 0.005         | -               |
| 1.9325 | 5500 | 0.0068        | 0.0048          |
| 1.9677 | 5600 | 0.0051        | -               |


### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.1
- 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",
}
```

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