---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:15178
- loss:CachedMultipleNegativesSymmetricRankingLoss
base_model: google-bert/bert-base-multilingual-uncased
widget:
- source_sentence: hapvida assistencia medica sa - coleta e vida imagem centro sao
    jose dos campos
  sentences:
  - sao jose imag
  - parokia gracas
  - igreja evangelica assembleia de deus - igreja evangelica assembleia de deusbairro
    saramandaia
- source_sentence: banco bradesco sa - bradesco pa brumadinho mg
  sentences:
  - bradesco
  - cdd balsas jeral
  - banco bradesco sa - bradesco ag aracuai mg est unif
- source_sentence: et renovavel - edificio et renovavel
  sentences:
  - edificio e.t.
  - lojas queroquero sa - queroquero
  - parokia gracas
- source_sentence: banco bradesco sa - bradesco ag cidade de deus est unif
  sentences:
  - bradesco ag. cidade
  - banco bradesco sa - bradesco ag prime sao cristovao est unif
  - bradesco paa triunfo paa
- source_sentence: banco bradesco sa - bradesco ag caico est unif
  sentences:
  - banco bradesco sa - agencia empresas aracatuba  urb aracatuba sp
  - monsenhor condominio
  - bradesco ag. caico est
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on google-bert/bert-base-multilingual-uncased

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the csv dataset. 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:** [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) <!-- at revision 7cbf9a625e29989f6b9c6c2fa68234c304f7e38f -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - csv
<!-- - **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("sentence_transformers_model_id")
# Run inference
sentences = [
    'banco bradesco sa - bradesco ag caico est unif',
    'bradesco ag. caico est',
    'banco bradesco sa - agencia empresas aracatuba  urb aracatuba sp',
]
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

#### csv

* Dataset: csv
* Size: 15,178 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                         | negative                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 16.2 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.91 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 17.69 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
  | anchor                                          | positive                | negative                                                                           |
  |:------------------------------------------------|:------------------------|:-----------------------------------------------------------------------------------|
  | <code>magazine luiza sa - magazine luiza</code> | <code>magazine l</code> | <code>centro espirita allan kardec - educandario euripedes creche mae luiza</code> |
  | <code>magazine luiza sa - magazine luiza</code> | <code>magazine l</code> | <code>secretaria de estado de saude  ses - upa 24 horas  nova iguacu ii</code>     |
  | <code>magazine luiza sa - magazine luiza</code> | <code>magazine l</code> | <code>expresso guanabara ltda - filial n 5</code>                                  |
* Loss: [<code>CachedMultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativessymmetricrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 32
  }
  ```

### Evaluation Dataset

#### csv

* Dataset: csv
* Size: 10,118 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                         | negative                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                           |
  | details | <ul><li>min: 6 tokens</li><li>mean: 16.05 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.98 tokens</li><li>max: 13 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 17.0 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
  | anchor                                                        | positive                    | negative                                                               |
  |:--------------------------------------------------------------|:----------------------------|:-----------------------------------------------------------------------|
  | <code>banco bradesco sa - ag bela vista</code>                | <code>bela bco</code>       | <code>banco bradesco sa - bradesco ag senhor do bonfim est unif</code> |
  | <code>banco bradesco sa - ag bela vista</code>                | <code>bela bco</code>       | <code>banco bradesco sa - bradesco ag teolandia</code>                 |
  | <code>banco bradesco sa - ag corporate passo fundo  rs</code> | <code>ag passo fundo</code> | <code>banco bradesco sa - bradesco ag sao jose da tapera al</code>     |
* Loss: [<code>CachedMultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativessymmetricrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 32
  }
  ```

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

- `num_train_epochs`: 4
- `batch_sampler`: no_duplicates

#### 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`: 8
- `per_device_eval_batch_size`: 8
- `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`: 4
- `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}
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 1.0526 | 500  | 0.4032        |
| 2.1053 | 1000 | 0.1094        |
| 3.1579 | 1500 | 0.0476        |


### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 4.0.1
- Transformers: 4.51.0
- PyTorch: 2.2.1
- Accelerate: 1.6.0
- Datasets: 3.5.0
- 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",
}
```

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