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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10000
- loss:CosineSimilarityLoss
base_model: google-bert/bert-base-multilingual-uncased
widget:
- source_sentence: banco bradesco sa - agencia empresas franca urb franca sp
sentences:
- ajensia santander
- drogal farmaseutica
- bradesco
- source_sentence: secretaria de estado da saude - ambulatorio medico de especialidades
s j dos campos
sentences:
- beneto roupas
- marx serbicos
- asessorias saude
- source_sentence: nacional lojas centro de distribuicao ltda - nba store arena curitiba
sentences:
- lojas centro
- fazenda ii
- bradesco
- source_sentence: fundo municipal dos direitos da crianca e do adolescente - fia fundo
da infancia e adolescencia
sentences:
- uniao igreja
- crianca adolessente
- crianca adolessente
- source_sentence: banco bradesco sa - pa prefeitura de guarulhos secretaria de educacao
sp
sentences:
- banco bradesco
- bradesco
- banco san
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 - pa prefeitura de guarulhos secretaria de educacao sp',
'bradesco',
'banco san',
]
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: 10,000 training samples
* Columns: <code>sentence1</code>, <code>score</code>, and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | score | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------|:--------------------------------------------------------------------------------|
| type | string | float | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 16.11 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.25</li><li>max: 0.66</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.4 tokens</li><li>max: 11 tokens</li></ul> |
* Samples:
| sentence1 | score | sentence2 |
|:------------------------------------------------------|:--------------------------------|:-----------------------------|
| <code>lissa z modas ltda - lissa z modas</code> | <code>0.5891740918159485</code> | <code>unib das tintas</code> |
| <code>veste sa estilo - le lis blanc beaute</code> | <code>0.6208785474300385</code> | <code>unib das tintas</code> |
| <code>lux solis energy ltda - lux solis energy</code> | <code>0.6257202327251434</code> | <code>unib das tintas</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
#### csv
* Dataset: csv
* Size: 11,618 evaluation samples
* Columns: <code>sentence1</code>, <code>score</code>, and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | score | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | float | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 15.89 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.28</li><li>max: 0.72</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.18 tokens</li><li>max: 12 tokens</li></ul> |
* Samples:
| sentence1 | score | sentence2 |
|:----------------------------------------------------------------------------------------------------------------|:--------------------------------|:------------------------------------------|
| <code>ordem dos advogados do brasil seccao de sao paulo - escola superior de advocacia praia grande</code> | <code>0.2610547542572021</code> | <code>escola superior de adbocasia</code> |
| <code>ordem dos advogados do brasil seccao de sao paulo - escola superior de advocacia unidade marilia</code> | <code>0.0</code> | <code>escola superior de adbocasia</code> |
| <code>banco bradesco sa - bradesco ag prime araraquara</code> | <code>0.3069073557853699</code> | <code>banco bradesco</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
- `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 |
|:------:|:----:|:-------------:|
| 3.1847 | 500 | 0.0319 |
### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 4.0.2
- Transformers: 4.51.2
- 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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