Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:200
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/multi-qa-distilbert-dot-v1
+widget:
+- source_sentence: Qual é o horário de funcionamento do setor DCFN (Divisão de Contabilidade
+    e Finanças)?
+  sentences:
+  - 'Demais informações acesse o site: https://www.gestaoadministrativa.saomateus.ufes.br/apresentacao'
+  - Por favor, contate o suporte técnico detalhando o problema do equipamento para
+    diagnóstico e reparo.
+  - Envie um e-mail para [email protected].
+- source_sentence: Como solicitar pagamento de ajuda de custos à estudante?
+  sentences:
+  - Para instalar uma impressora, solicite o serviço ao suporte de TI em https://atendimento.ufes.br,
+    que poderá auxiliar com a instalação e configuração do equipamento.
+  - Faça login em https://administrativo.ufes.br/sistema/catalogo-produtos/catalogo.
+  - 'Cabe à Secretaria Única de Graduação – SUGRAD/CEUNES instruir devidamente o processo
+    digital, encaminhar para análise e aprovação da Direção do Ceunes, que se estiver
+    de acordo, remeterá o mesmo à DCFN (Divisão de Contabilidade e Finanças) para
+    efetivação do pagamento.
+
+    Informações sobre pagamento de ajuda de custos à estudantes entrar em contato
+    com a DCFN (Divisão de Contabilidade e Finanças).
+
+
+    E-mail institucional: [email protected].
+
+
+    Telefones: 3312-1517 e 3312-1518.
+
+
+    Demais informações acesse o site: https://www.gestaoadministrativa.saomateus.ufes.br/apresentacao'
+- source_sentence: Como solicitar atendimento social online?
+  sentences:
+  - Acesse https://administrativo.ufes.br/sistema/solicitacao/visualizar-solicitacoes-universidade.
+  - 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.
+  - Envie um e-mail para [email protected] para agendar o atendimento.
+- source_sentence: Problemas - Pontos de Internet
+  sentences:
+  - Se há pontos de internet que não estão funcionando, por favor, entre em contato
+    com o suporte de TI para solicitar manutenção ou inspeção dos cabos e conectores.
+  - Siga as orientações em https://senha.ufes.br/site/recuperaCredenciais.
+  - Procurar a aplicação Executar no menu do Windows ou pressionar as teclas simultaneamente
+    Windows + R e digitar \\172.20.110.8 .
+- source_sentence: Qual é o procedimento para solicitação de compras?
+  sentences:
+  - Envie um ofício via documento avulso para DRMN. Mais informações em https://drm.saomateus.ufes.br/agentes-patrimoniais.
+  - Para solicitar uma compra, é necessário preencher o formulário de solicitação
+    e enviá-lo ao setor de compras.
+  - Atualmente somente são realizadas consultas relativas à avaliação dos exames periódicos.
+    Envie um e-mail para [email protected] ou ligue para equipe de enfermagem
+    no ramal (27) 3312-1742. O horário de atendimento é de segunda a sexta-feira,
+    das 08h às 11h30 e das 12h30 às 17h.
+datasets:
+- matunderstars/ufes-qa-data
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sentence-transformers/multi-qa-distilbert-dot-v1
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/multi-qa-distilbert-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-distilbert-dot-v1) 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 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:** [sentence-transformers/multi-qa-distilbert-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-distilbert-dot-v1) <!-- at revision af530b176a2172b3aeeb9abc7b9d4e808f2a9477 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Dot Product
+- **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: DistilBertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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-v3")
+# Run inference
+sentences = [
+    'Qual é o procedimento para solicitação de compras?',
+    'Para solicitar uma compra, é necessário preencher o formulário de solicitação e enviá-lo ao setor de compras.',
+    'Atualmente somente são realizadas consultas relativas à avaliação dos exames periódicos. Envie um e-mail para [email protected] ou ligue para equipe de enfermagem no ramal (27) 3312-1742. O horário de atendimento é de segunda a sexta-feira, das 08h às 11h30 e das 12h30 às 17h.',
+]
+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]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## 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: 7 tokens</li><li>mean: 18.01 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 56.46 tokens</li><li>max: 390 tokens</li></ul> |
+* Samples:
+  | question                                                                                                                                | answer                                                                                                                                                                                                                                  |
+  |:----------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Onde encontrar informações sobre diárias?</code>                                                                                  | <code>Procedimentos, formulários, dúvidas e orientações estão disponíveis em:<br>https://gestaoadministrativa.saomateus.ufes.br/procedimentos-necessarios-para-solicitacao-de-diarias-e-passagens-aereas-no-ambito-do-ceunesufes</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>                                                                                                                                              |
+  | <code>Como cadastrar/alterar dados no Sistema Integrado de Ensino (SIE), Protocolo, Portal Administrativo, Acadêmico e Reservas?</code> | <code>Acesse https://dtin.saomateus.ufes.br/cadastros-e-habilitacao-aos-sistemas-institucionais e preencha o formulário.</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: 18.3 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 52.4 tokens</li><li>max: 219 tokens</li></ul> |
+* Samples:
+  | question                                                    | answer                                                                                                                                                                    |
+  |:------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Liberação de Acesso a sistemas institucionais</code>  | <code>Para liberar acesso a sistemas institucionais, entre em contato com o setor de TI da UFES, especificando o recurso ou sistema para o qual precisa de acesso.</code> |
+  | <code>Como criar uma nova ata de registro de preços?</code> | <code>Observe o calendário de compras CEUNES. Acesse https://crm.saomateus.ufes.br.</code>                                                                                |
+  | <code>Sistema dos Correios (SIGEP) não abre</code>          | <code>Verifique se o sistema SIGEP está atualizado. Consulte o suporte de TI para assistência.</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`: 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  | 0.1063        |
+| 142.8571 | 1000 | 0.0001        |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.3.1
+- Transformers: 4.46.3
+- PyTorch: 2.5.1+cu121
+- Accelerate: 1.1.1
+- Datasets: 3.2.0
+- 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "sentence-transformers/multi-qa-distilbert-dot-v1",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertModel"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.3",
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.46.3",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "dot"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4d99c08f7680c3404744305979d6bc73b192049c361e1734a85901fa05890e5
+size 265462608

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,62 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 250,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}