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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:184
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L12-v2
widget:
- source_sentence: Onde tirar dúvidas sobre o SIASS?
sentences:
- Envie um e-mail para [email protected]
- Envie um e-mail para [email protected].
- Envie um e-mail para [email protected] solicitando a alteração dos
dados bancários.
- source_sentence: Como acionar a manutenção de um bem em garantia?
sentences:
- Preencha o formulário em https://administrativo.ufes.br e envie com 15 dias de
antecedência.
- Acesse https://compras.ufes.br/inclusao-de-produto-no-catalogo-de-materiais.
- Entre em contato com o fornecedor.
- source_sentence: Computador não abre sistema operacional
sentences:
- Faça login no gmail.com com o usuário único @ufes.br e siga as instruções em https://senha.ufes.br/site/ativaGmail.
- Clique no link https://chat.google.com/room/AAAAHqHLj6c?cls=4.
- Se o sistema operacional não inicia, pode ser um problema no disco ou sistema.
Contate o suporte de TI para suporte e diagnóstico.
- source_sentence: Como acessar os dados acadêmicos e administrativos?
sentences:
- Siga as orientações disponíveis em https://progep.ufes.br/exames-periodicos.
- Acesse o Portal Administrativo em https://administrativo.ufes.br.
- Acesse https://senha.ufes.br/site/recuperaCredenciais.
- source_sentence: Como cadastrar ou alterar dados no Sistema Integrado de Ensino
(SIE), Protocolo, Portal Administrativo, Acadêmico e Reservas?
sentences:
- Siga os procedimentos em https://portaladministrativo.ufes.br/utilizacao-de-registro-de-precos-existente.
- 'Acesse nosso chat para falar com um atendente humano: https://chat.google.com/room/AAAAHqHLj6c?cls=7'
- Acesse https://dtin.saomateus.ufes.br/cadastros-e-habilitacao-aos-sistemas-institucionais
e preencha o formulário.
datasets:
- matunderstars/ufes-qa-data
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) 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 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/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) <!-- at revision 364dd28d28dcd3359b537f3cf1f5348ba679da62 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 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 -->
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### 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})
(2): Normalize()
)
```
## 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")
# Run inference
sentences = [
'Como cadastrar ou alterar dados no Sistema Integrado de Ensino (SIE), Protocolo, Portal Administrativo, Acadêmico e Reservas?',
'Acesse https://dtin.saomateus.ufes.br/cadastros-e-habilitacao-aos-sistemas-institucionais e preencha o formulário.',
'Acesse nosso chat para falar com um atendente humano: https://chat.google.com/room/AAAAHqHLj6c?cls=7',
]
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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You can finetune this model on your own dataset.
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## Training Details
### Training Datasets
#### train
* Dataset: [train](https://huggingface.co/datasets/matunderstars/ufes-qa-data) at [9021242](https://huggingface.co/datasets/matunderstars/ufes-qa-data/tree/9021242881748c37acc972de64de25d00d54f4d1)
* Size: 92 training samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 92 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 17.88 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 46.03 tokens</li><li>max: 128 tokens</li></ul> |
* Samples:
| question | answer |
|:-------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Como registrar atestado de saúde?</code> | <code>Realize o registro pelo aplicativo SouGov (Menu > Atestado de Saúde > Incluir > Selecionar arquivo no dispositivo) ou pelo Portal Sigepe em Gestão de Pessoas > Minha Saúde > Atestado Médico.</code> |
| <code>Como fazer uma doação ou empréstimo de um bem patrimonial?</code> | <code>Modelos estão em https://drm.saomateus.ufes.br → Patrimônio → Formulários e Modelos.</code> |
| <code>Onde encontrar informações sobre as salas de aula e a configuração de equipamentos?</code> | <code>Consulte o manual em https://dtin.saomateus.ufes.br/tecnologias-educacionais.</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 [9021242](https://huggingface.co/datasets/matunderstars/ufes-qa-data/tree/9021242881748c37acc972de64de25d00d54f4d1)
* Size: 92 training samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 92 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 17.32 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 40.58 tokens</li><li>max: 68 tokens</li></ul> |
* Samples:
| question | answer |
|:-----------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Como acessar o manual do Estudo Técnico Preliminar (ETP)?</code> | <code>Acesse o manual em https://gov.br/compras/pt-br/centrais-de-conteudo/manuais/manual-etp-digital.</code> |
| <code>Como solicitar material de consumo?</code> | <code>Faça login em https://administrativo.ufes.br/sistema/catalogo-produtos/catalogo.</code> |
| <code>Problemas de conexão de internet</code> | <code>Problemas de conexão de internet podem ser causados por falhas de rede. Para resolver, entre em contato com o suporte de TI da UFES.</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`: 150
- `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`: 150
- `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`: False
- `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 | 0.1147 |
| 142.8571 | 1000 | 0.0001 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.46.2
- PyTorch: 2.5.1+cu121
- Accelerate: 1.1.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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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