README.md

---
base_model: sentence-transformers/paraphrase-MiniLM-L6-v2
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:75253
- loss:CoSENTLoss
widget:
- source_sentence: buenos aires general pueyrredon mar del plata calle 395
  sentences:
  - buenos aires lujan de cuyo mar del plata calle 395
  - buenos aires general pueyrredon mar del plata calle 499
  - buenos aires general pueyrredon calle 15
- source_sentence: buenos aires bahia blanca chacabuco
  sentences:
  - jujuy ciudad autonoma buenos aires av eva peron
  - buenos aires caada de gomez cadetes
  - buenos aires bahia blanca migueletes
- source_sentence: buenos aires bahia blanca curumalal
  sentences:
  - buenos aires punilla mar del plata corbeta uruguay
  - capital federal ciudad autonoma buenos aires av rey del bosque
  - buenos aires rio chico curumalal
- source_sentence: buenos aires lomas de zamora sixto fernandez
  sentences:
  - buenos aires general pueyrredon santa rosa de calamuchita san lorenzo
  - buenos aires jose ingenieros sixto fernandez
  - buenos aires lomas de zamora florida luis viale
- source_sentence: buenos aires moreno francisco alvarez paramaribo
  sentences:
  - mendoza general pueyrredon mar del plata calle 3 b
  - buenos aires moreno francisco alvarez bermejo
  - buenos aires ezeiza av 60
---

# SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L6-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2) <!-- at revision 3bf4ae7445aa77c8daaef06518dd78baffff53c9 -->
- **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("tomasravel/modelo_finetuneado24")
# Run inference
sentences = [
    'buenos aires moreno francisco alvarez paramaribo',
    'buenos aires moreno francisco alvarez bermejo',
    'mendoza general pueyrredon mar del plata calle 3 b',
]
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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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 75,253 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: 4 tokens</li><li>mean: 13.46 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.0 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 0.2</li><li>mean: 0.69</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                          | sentence_1                                                  | label            |
  |:--------------------------------------------------------------------|:------------------------------------------------------------|:-----------------|
  | <code>buenos aires lomas de zamora temperley cangallo</code>        | <code>buenos aires lomas de zamora cangallo</code>          | <code>1.0</code> |
  | <code>buenos aires general pueyrredon mar del plata calle 33</code> | <code>buenos aires maximo paz mar del plata calle 33</code> | <code>0.6</code> |
  | <code>buenos aires general pueyrredon mar del plata cordoba</code>  | <code>buenos aires washington mar del plata cordoba</code>  | <code>0.6</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

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

- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 10
- `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`: 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
- `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`: 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 |
|:------:|:-----:|:-------------:|
| 0.2126 | 500   | 6.2141        |
| 0.4252 | 1000  | 5.3697        |
| 0.6378 | 1500  | 5.2046        |
| 0.8503 | 2000  | 5.1007        |
| 1.0629 | 2500  | 4.9564        |
| 1.2755 | 3000  | 4.8524        |
| 1.4881 | 3500  | 4.7941        |
| 1.7007 | 4000  | 4.7099        |
| 1.9133 | 4500  | 4.6723        |
| 2.1259 | 5000  | 4.5816        |
| 2.3384 | 5500  | 4.5275        |
| 2.5510 | 6000  | 4.527         |
| 2.7636 | 6500  | 4.4588        |
| 2.9762 | 7000  | 4.4253        |
| 3.1888 | 7500  | 4.3234        |
| 3.4014 | 8000  | 4.3147        |
| 3.6139 | 8500  | 4.2644        |
| 3.8265 | 9000  | 4.256         |
| 4.0391 | 9500  | 4.1724        |
| 4.2517 | 10000 | 4.1406        |
| 4.4643 | 10500 | 4.0917        |
| 4.6769 | 11000 | 4.1334        |
| 4.8895 | 11500 | 4.0791        |
| 5.1020 | 12000 | 4.0217        |
| 5.3146 | 12500 | 3.9745        |
| 5.5272 | 13000 | 3.9575        |
| 5.7398 | 13500 | 3.942         |
| 5.9524 | 14000 | 3.9029        |
| 6.1650 | 14500 | 3.8617        |
| 6.3776 | 15000 | 3.8648        |
| 6.5901 | 15500 | 3.7995        |
| 6.8027 | 16000 | 3.83          |
| 7.0153 | 16500 | 3.734         |
| 7.2279 | 17000 | 3.7528        |
| 7.4405 | 17500 | 3.634         |
| 7.6531 | 18000 | 3.7306        |
| 7.8656 | 18500 | 3.7076        |
| 8.0782 | 19000 | 3.6494        |
| 8.2908 | 19500 | 3.664         |
| 8.5034 | 20000 | 3.5254        |
| 8.7160 | 20500 | 3.5624        |
| 8.9286 | 21000 | 3.5812        |
| 9.1412 | 21500 | 3.566         |
| 9.3537 | 22000 | 3.3967        |
| 9.5663 | 22500 | 3.474         |
| 9.7789 | 23000 | 3.5136        |
| 9.9915 | 23500 | 3.4518        |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.44.2
- PyTorch: 2.2.2+cu121
- Accelerate: 0.34.2
- Datasets: 2.21.0
- 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",
}
```

#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}
```

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