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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:200
- loss:MultipleNegativesRankingLoss
base_model: neuralmind/bert-large-portuguese-cased
widget:
- source_sentence: Solicitação de manutenção nos conectores de rede
sentences:
- Para manutenção dos conectores de rede, encaminhe a solicitação ao setor de TI
da UFES em https://atendimento.ufes.br, especificando o laboratório e os problemas
encontrados.
- Acesse o site da Prograd em https://prograd.ufes.br para mais informações conforme
o edital vigente.
- 'Ao identificar sua convocação no SouGov.br (na funcionalidade Minha Saúde - Exames
Periódicos), o servidor irá decidir sobre a realização do exame periódico, conforme
as etapas a seguir: 1) Visualizar exames e avançar; 2) Informar se concorda ou
não em realizar os exames médicos periódicos, clicar em Salvar e Avançar; 3) Caso
o servidor tenha concordado em realizar os exames, ele deverá clicar em Emitir
Guia, imprimi-las e Avançar para preencher formulário de Anamnese (1. Histórico
Ocupacional; 2. Antecedentes Pessoais; 3. Antecedentes Familiares; 4. Hábitos
Pessoais; e 5. Condições Atuais de Trabalho) e finalizar o processo.'
- source_sentence: Quero falar com um atendente humano, pessoa real
sentences:
- Envie um e-mail para [email protected] solicitando a alteração dos
dados bancários.
- Para dificuldades de acesso à rede Eduroam, verifique as configurações de rede
e as credenciais fornecidas. Caso persista, contate o suporte de TI da UFES para
assistência.
- 'Acesse nosso chat para falar com um atendente humano: https://chat.google.com/room/AAAAHqHLj6c?cls=7'
- source_sentence: Como realizar o cadastro no Proaes?
sentences:
- Acesse o site da Proaeci em https://proaeci.ufes.br/editais para verificar se
há algum edital vigente para o semestre.
- Acesse o manual em https://gov.br/compras/pt-br/centrais-de-conteudo/manuais/manual-etp-digital.
- Acesse https://compras.ufes.br/inclusao-de-produto-no-catalogo-de-materiais.
- source_sentence: Como posso solicitar manutenção de bens?
sentences:
- A solicitação de manutenção de bens deve ser feita pelo sistema de gestão patrimonial.
- Por favor, contate o suporte técnico detalhando o problema do equipamento para
diagnóstico e reparo.
- Sou especializado em responder perguntas frequentes relacionadas a UFES sobre
a Diretoria de Suporte Administrativo - DSAN.
- source_sentence: Como solicitar atendimento social online?
sentences:
- Com a senha única, siga o tutorial correspondente em https://sti.ufes.br/eduroam.
- Envie um e-mail para [email protected] para agendar o atendimento.
- Envie um ofício via documento avulso para a DRMN, conforme manual disponível em
https://drm.saomateus.ufes.br/manuais-0.
datasets:
- matunderstars/ufes-qa-data
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on neuralmind/bert-large-portuguese-cased
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the [train](https://huggingface.co/datasets/matunderstars/ufes-qa-data) and [test](https://huggingface.co/datasets/matunderstars/ufes-qa-data) datasets. It maps sentences & paragraphs to a 1024-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:** [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) <!-- at revision aa302f6ea73b759f7df9cad58bd272127b67ec28 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- [train](https://huggingface.co/datasets/matunderstars/ufes-qa-data)
- [test](https://huggingface.co/datasets/matunderstars/ufes-qa-data)
<!-- - **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': 1024, '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("matunderstars/ufes-qa-embedding-finetuned-bert")
# Run inference
sentences = [
'Como solicitar atendimento social online?',
'Envie um e-mail para [email protected] para agendar o atendimento.',
'Envie um ofício via documento avulso para a DRMN, conforme manual disponível em https://drm.saomateus.ufes.br/manuais-0.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# 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 Datasets
#### train
* Dataset: [train](https://huggingface.co/datasets/matunderstars/ufes-qa-data) at [02bfedf](https://huggingface.co/datasets/matunderstars/ufes-qa-data/tree/02bfedf96441339120864b5df6b748c47d391b2d)
* Size: 100 training samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 100 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 12.81 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 47.79 tokens</li><li>max: 272 tokens</li></ul> |
* Samples:
| question | answer |
|:------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Problemas para acessar a internet</code> | <code>Para problemas de acesso à internet, verifique as configurações de rede. Se o problema continuar, entre em contato com a equipe de TI para suporte.</code> |
| <code>Como solicitar o tombamento de um bem extraorçamentário?</code> | <code>Envie a documentação via https://protocolo.ufes.br. Manual em https://drm.saomateus.ufes.br/manuais-0.</code> |
| <code>Onde enviar dúvidas sobre o sistema de registro de preços?</code> | <code>Envie um e-mail para [email protected].</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### test
* Dataset: [test](https://huggingface.co/datasets/matunderstars/ufes-qa-data) at [02bfedf](https://huggingface.co/datasets/matunderstars/ufes-qa-data/tree/02bfedf96441339120864b5df6b748c47d391b2d)
* Size: 100 training samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 100 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.65 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 35.4 tokens</li><li>max: 78 tokens</li></ul> |
* Samples:
| question | answer |
|:--------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Como acessar os dados acadêmicos e administrativos?</code> | <code>Acesse o Portal Administrativo em https://administrativo.ufes.br.</code> |
| <code>Suporte técnico para notebook</code> | <code>Para solicitar suporte técnico para notebooks institucionais, entre em contato com o setor de TI da UFES, detalhando o problema encontrado.</code> |
| <code>Onde acessar o manual para utilizar o Portal Administrativo?</code> | <code>Acesse https://drm.saomateus.ufes.br → Patrimônio → Manuais.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 180
- `warmup_ratio`: 0.1
- `fp16`: True
- `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`: 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`: 1.0
- `num_train_epochs`: 180
- `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}
- `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
- `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
- `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 |
|:--------:|:----:|:-------------:|
| 71.4286 | 500 | 0.1226 |
| 142.8571 | 1000 | 0.0 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## 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",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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