README.md

---
base_model: somosnlp-hackathon-2022/paraphrase-spanish-distilroberta
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:44147
- loss:SoftmaxLoss
widget:
- source_sentence: Componentes y Equipos para Distribución y Sistemas de Acondicionamiento
    Instalaciones de tubos y entubamientos
  sentences:
  - Frijoles verdes congelados Fríjoles congelados
  - 'Brida reductora para tubos de plástico cpvc Bridas reductoras para tubos '
  - Naranja hamlin orgánica en lata o en frasco Naranjas orgánicas en lata o en frasco
- source_sentence: Componentes y Suministros de Manufactura Ferretería
  sentences:
  - Terfenadina Antihistamínicos (bloqueadores H1)
  - Tomates verde Tomates
  - Ciruela sloe seca Ciruelas secas
- source_sentence: Servicios Públicos y Servicios Relacionados con el Sector Público
    Servicios públicos
  sentences:
  - Chalote pikant orgánico Chalotes orgánicos
  - Rosal cortado seco ciciolina Rosas cortadas secas rosados
  - Rosal vivo peach sherbet Rosales vivos anaranjados
- source_sentence: Maquinaria y Accesorios para Manufactura y Procesamiento Industrial
    Maquinaria y accesorios para cortar metales
  sentences:
  - Pimentón peperoncini seco Pimientos Secos
  - Ciruela diamante rojo congelada orgánica Ciruelas orgánicas congeladas
  - Máquinas para dar formas al metal en la superficie Máquinas perforadoras de metales
- source_sentence: Alimentos, Bebidas y Tabaco  Vegetales orgánicos secos
  sentences:
  - Coliflo rdok elgon orgánica seca Coliflores  orgánicas secas
  - Arame  orgánica seca Vegetales marinos orgánicos secos
  - Cereza dark guines Cerezas
---

# SentenceTransformer based on somosnlp-hackathon-2022/paraphrase-spanish-distilroberta

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [somosnlp-hackathon-2022/paraphrase-spanish-distilroberta](https://huggingface.co/somosnlp-hackathon-2022/paraphrase-spanish-distilroberta). 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:** [somosnlp-hackathon-2022/paraphrase-spanish-distilroberta](https://huggingface.co/somosnlp-hackathon-2022/paraphrase-spanish-distilroberta) <!-- at revision 5ed9fdaabd705e7bd88029a3f08ce7397a666d6a -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 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': 256, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (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("dfsandovalp01/paraphrase-spanish-distilroberta-MDD-pucCO-V2")
# Run inference
sentences = [
    'Alimentos, Bebidas y Tabaco  Vegetales orgánicos secos',
    'Coliflo rdok elgon orgánica seca Coliflores  orgánicas secas',
    'Arame  orgánica seca Vegetales marinos orgánicos secos',
]
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]
```

<!--
### 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.*
-->

<!--
## 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: 44,147 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                                                                            | int                                                               |
  | details | <ul><li>min: 5 tokens</li><li>mean: 15.49 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.39 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>0: ~48.80%</li><li>1: ~8.30%</li><li>2: ~42.90%</li></ul> |
* Samples:
  | sentence_0                                                                                           | sentence_1                                                                                     | label          |
  |:-----------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:---------------|
  | <code>Maquinaria y Accesorios para Generación y Distribución de Energía Generación de energía</code> | <code>Amortiguador de veleta Equipo de cribado o estructuras de tubo de escape</code>          | <code>0</code> |
  | <code>Alimentos, Bebidas y Tabaco  Fruta orgánica en lata o en frasco</code>                         | <code>Mangos mayaguez orgánico en lata o en frasco Mangos orgánicos en lata o en frasco</code> | <code>0</code> |
  | <code>Alimentos, Bebidas y Tabaco  Fruta orgánica congelada</code>                                   | <code>Bolsa para transportar quimioterapia Equipo y suministros de quimioterapia</code>        | <code>1</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

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

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `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
- `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`: 1
- `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
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.1812 | 500  | 0.6649        |
| 0.3623 | 1000 | 0.4498        |
| 0.5435 | 1500 | 0.3788        |
| 0.7246 | 2000 | 0.3636        |
| 0.9058 | 2500 | 0.353         |
| 0.1812 | 500  | 0.3429        |
| 0.3623 | 1000 | 0.3254        |
| 0.5435 | 1500 | 0.3359        |
| 0.7246 | 2000 | 0.3209        |
| 0.9058 | 2500 | 0.3311        |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.0
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.0
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers and SoftmaxLoss
```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",
}
```

<!--
## 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.*
-->