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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:200 |
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- loss:MultipleNegativesRankingLoss |
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base_model: neuralmind/bert-large-portuguese-cased |
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widget: |
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- source_sentence: Solicitação de manutenção nos conectores de rede |
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sentences: |
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- Para manutenção dos conectores de rede, encaminhe a solicitação ao setor de TI |
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da UFES em https://atendimento.ufes.br, especificando o laboratório e os problemas |
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encontrados. |
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- Acesse o site da Prograd em https://prograd.ufes.br para mais informações conforme |
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o edital vigente. |
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- 'Ao identificar sua convocação no SouGov.br (na funcionalidade Minha Saúde - Exames |
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Periódicos), o servidor irá decidir sobre a realização do exame periódico, conforme |
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as etapas a seguir: 1) Visualizar exames e avançar; 2) Informar se concorda ou |
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não em realizar os exames médicos periódicos, clicar em Salvar e Avançar; 3) Caso |
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o servidor tenha concordado em realizar os exames, ele deverá clicar em Emitir |
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Guia, imprimi-las e Avançar para preencher formulário de Anamnese (1. Histórico |
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Ocupacional; 2. Antecedentes Pessoais; 3. Antecedentes Familiares; 4. Hábitos |
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Pessoais; e 5. Condições Atuais de Trabalho) e finalizar o processo.' |
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- source_sentence: Quero falar com um atendente humano, pessoa real |
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sentences: |
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- Envie um e-mail para [email protected] solicitando a alteração dos |
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dados bancários. |
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- Para dificuldades de acesso à rede Eduroam, verifique as configurações de rede |
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e as credenciais fornecidas. Caso persista, contate o suporte de TI da UFES para |
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assistência. |
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- 'Acesse nosso chat para falar com um atendente humano: https://chat.google.com/room/AAAAHqHLj6c?cls=7' |
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- source_sentence: Como realizar o cadastro no Proaes? |
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sentences: |
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- Acesse o site da Proaeci em https://proaeci.ufes.br/editais para verificar se |
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há algum edital vigente para o semestre. |
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- Acesse o manual em https://gov.br/compras/pt-br/centrais-de-conteudo/manuais/manual-etp-digital. |
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- Acesse https://compras.ufes.br/inclusao-de-produto-no-catalogo-de-materiais. |
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- source_sentence: Como posso solicitar manutenção de bens? |
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sentences: |
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- A solicitação de manutenção de bens deve ser feita pelo sistema de gestão patrimonial. |
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- Por favor, contate o suporte técnico detalhando o problema do equipamento para |
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diagnóstico e reparo. |
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- Sou especializado em responder perguntas frequentes relacionadas a UFES sobre |
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a Diretoria de Suporte Administrativo - DSAN. |
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- source_sentence: Como solicitar atendimento social online? |
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sentences: |
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- Com a senha única, siga o tutorial correspondente em https://sti.ufes.br/eduroam. |
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- Envie um e-mail para [email protected] para agendar o atendimento. |
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- Envie um ofício via documento avulso para a DRMN, conforme manual disponível em |
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https://drm.saomateus.ufes.br/manuais-0. |
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datasets: |
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- matunderstars/ufes-qa-data |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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--- |
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# SentenceTransformer based on neuralmind/bert-large-portuguese-cased |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) <!-- at revision aa302f6ea73b759f7df9cad58bd272127b67ec28 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 1024 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Datasets:** |
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- [train](https://huggingface.co/datasets/matunderstars/ufes-qa-data) |
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- [test](https://huggingface.co/datasets/matunderstars/ufes-qa-data) |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("matunderstars/ufes-qa-embedding-finetuned-bert") |
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# Run inference |
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sentences = [ |
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'Como solicitar atendimento social online?', |
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'Envie um e-mail para [email protected] para agendar o atendimento.', |
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'Envie um ofício via documento avulso para a DRMN, conforme manual disponível em https://drm.saomateus.ufes.br/manuais-0.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Datasets |
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#### train |
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* Dataset: [train](https://huggingface.co/datasets/matunderstars/ufes-qa-data) at [02bfedf](https://huggingface.co/datasets/matunderstars/ufes-qa-data/tree/02bfedf96441339120864b5df6b748c47d391b2d) |
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* Size: 100 training samples |
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* Columns: <code>question</code> and <code>answer</code> |
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* Approximate statistics based on the first 100 samples: |
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| | question | answer | |
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|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
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| type | string | string | |
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| 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> | |
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* Samples: |
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| question | answer | |
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|:------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <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> | |
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| <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> | |
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| <code>Onde enviar dúvidas sobre o sistema de registro de preços?</code> | <code>Envie um e-mail para [email protected].</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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#### test |
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* Dataset: [test](https://huggingface.co/datasets/matunderstars/ufes-qa-data) at [02bfedf](https://huggingface.co/datasets/matunderstars/ufes-qa-data/tree/02bfedf96441339120864b5df6b748c47d391b2d) |
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* Size: 100 training samples |
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* Columns: <code>question</code> and <code>answer</code> |
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* Approximate statistics based on the first 100 samples: |
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| | question | answer | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| 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> | |
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* Samples: |
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| question | answer | |
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|:--------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>Como acessar os dados acadêmicos e administrativos?</code> | <code>Acesse o Portal Administrativo em https://administrativo.ufes.br.</code> | |
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| <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> | |
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| <code>Onde acessar o manual para utilizar o Portal Administrativo?</code> | <code>Acesse https://drm.saomateus.ufes.br → Patrimônio → Manuais.</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `num_train_epochs`: 180 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `batch_sampler`: no_duplicates |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 180 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | |
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|:--------:|:----:|:-------------:| |
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| 71.4286 | 500 | 0.1226 | |
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| 142.8571 | 1000 | 0.0 | |
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.3.1 |
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- Transformers: 4.47.1 |
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- PyTorch: 2.5.1+cu121 |
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- Accelerate: 1.2.1 |
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- Datasets: 3.2.0 |
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- Tokenizers: 0.21.0 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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