| --- |
| base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
| library_name: setfit |
| metrics: |
| - accuracy |
| pipeline_tag: text-classification |
| tags: |
| - setfit |
| - sentence-transformers |
| - text-classification |
| - generated_from_setfit_trainer |
| widget: |
| - text: Pasivo ahorro y retiro job mejor atención y disponibilidad |
| - text: Detractor ahorro y retiro ahorro y retiro premium La atenció telefónica no |
| es buena solo habla una maquina y nunca responde una persona para que le ayude |
| a uno y poder expresar lo que se necesita. |
| - text: Detractor gestión patrimonial alto perfil Difícil hacer una gestión por la |
| página. No he podido retirar un saldo porque no llevo carta y no me dicen qué |
| hacer si esa empresa ya no existe |
| - text: Detractor ahorro y retiro dynamic top POrque tengo una inversion y hace tiempo |
| que no se contacta mi asesor conmigo, le escribí un correo hace unos días y no |
| me contestó, cambie de celular y no he podido actiualizarlo, estoy buscando como |
| sacar mi dinero de alla, por la mala experiencia. |
| - text: Detractor ahorro y retiro pensionado Empecé el proceso en****, y terminé consiguiéndolo |
| en el****, me dejé en el camino más de 250€ en llamadas desde España a Colombia, |
| y cada mes me toca pagar para traer el dinero de mi pensión hasta España porque |
| no hay convenios con los bancos, pierdes en el año más o menos el 80% de una mesada. |
| inference: true |
| model-index: |
| - name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
| results: |
| - task: |
| type: text-classification |
| name: Text Classification |
| dataset: |
| name: Unknown |
| type: unknown |
| split: test |
| metrics: |
| - type: accuracy |
| value: 0.8823529411764706 |
| name: Accuracy |
| --- |
| |
| # SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
|
|
| This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. |
|
|
| The model has been trained using an efficient few-shot learning technique that involves: |
|
|
| 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
| 2. Training a classification head with features from the fine-tuned Sentence Transformer. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** SetFit |
| - **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) |
| - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
| - **Maximum Sequence Length:** 128 tokens |
| - **Number of Classes:** 4 classes |
| <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
| - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
| - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
|
|
| ### Model Labels |
| | Label | Examples | |
| |:----------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | Construcción de mi pensión personas | <ul><li>'Promotor ahorro y retiro job Excelente servicio'</li><li>'Promotor ahorro y retiro pensionado Asesoría sobre las modalidades de pensión'</li><li>'Pasivo ahorro y retiro hni job Mejorar la asesoría personalizada según el nivel de ingresos de la persona'</li></ul> | |
| | Solución de ahorro e inversión personas | <ul><li>'Detractor ahorro y retiro job No estoy muy relacionada con el tema'</li><li>'Detractor gestión patrimonial alto perfil Mal servicio por desconocimiento, decisiones unilaterales de Proteccion que afectan a los usuarios, falta de trasparencia en negociones de bonos, falta de soportes aritmeticos y financieros en sus datos a clientes, etc, ect.'</li><li>'Pasivo ahorro y retiro job Asesor pendiente del ahorro sea mucho o poco para tener más rendimientos.'</li></ul> | |
| | Cesantías Personas | <ul><li>'Detractor gestión patrimonial alto perfil No me volvieron a enviar información de mi estado de cuenta de las cesantías'</li></ul> | |
| | Construcción de mi pensión empresas | <ul><li>'Detractor ahorro y retiro ahorro y retiro basic No contamos con acompañamiento.'</li><li>'Promotor grandes empleadores grandes empleadores el reconocimiento y trayectoria'</li><li>'Pasivo ahorro y retiro ahorro y retiro basic Mejor asesoramiento'</li></ul> | |
|
|
| ## Evaluation |
|
|
| ### Metrics |
| | Label | Accuracy | |
| |:--------|:---------| |
| | **all** | 0.8824 | |
|
|
| ## Uses |
|
|
| ### Direct Use for Inference |
|
|
| First install the SetFit library: |
|
|
| ```bash |
| pip install setfit |
| ``` |
|
|
| Then you can load this model and run inference. |
|
|
| ```python |
| from setfit import SetFitModel |
| |
| # Download from the 🤗 Hub |
| model = SetFitModel.from_pretrained("camaosos/journey") |
| # Run inference |
| preds = model("Pasivo ahorro y retiro job mejor atención y disponibilidad") |
| ``` |
|
|
| <!-- |
| ### Downstream Use |
|
|
| *List how someone could finetune this model on their own dataset.* |
| --> |
|
|
| <!-- |
| ### 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 Set Metrics |
| | Training set | Min | Median | Max | |
| |:-------------|:----|:--------|:----| |
| | Word count | 5 | 18.7576 | 169 | |
|
|
| | Label | Training Sample Count | |
| |:----------------------------------------|:----------------------| |
| | Cesantías Personas | 1 | |
| | Construcción de mi pensión empresas | 8 | |
| | Construcción de mi pensión personas | 31 | |
| | Solución de ahorro e inversión personas | 26 | |
|
|
| ### Training Hyperparameters |
| - batch_size: (16, 16) |
| - num_epochs: (4, 4) |
| - max_steps: -1 |
| - sampling_strategy: oversampling |
| - body_learning_rate: (2e-05, 1e-05) |
| - head_learning_rate: 0.01 |
| - loss: CosineSimilarityLoss |
| - distance_metric: cosine_distance |
| - margin: 0.25 |
| - end_to_end: False |
| - use_amp: False |
| - warmup_proportion: 0.1 |
| - seed: 42 |
| - eval_max_steps: -1 |
| - load_best_model_at_end: True |
|
|
| ### Training Results |
| | Epoch | Step | Training Loss | Validation Loss | |
| |:-------:|:-------:|:-------------:|:---------------:| |
| | 0.0060 | 1 | 0.1959 | - | |
| | 0.3012 | 50 | 0.196 | - | |
| | 0.6024 | 100 | 0.0082 | - | |
| | 0.9036 | 150 | 0.0016 | - | |
| | 1.0 | 166 | - | 0.1009 | |
| | 1.2048 | 200 | 0.0012 | - | |
| | 1.5060 | 250 | 0.0012 | - | |
| | 1.8072 | 300 | 0.0004 | - | |
| | **2.0** | **332** | **-** | **0.095** | |
| | 2.1084 | 350 | 0.0005 | - | |
| | 2.4096 | 400 | 0.0004 | - | |
| | 2.7108 | 450 | 0.0005 | - | |
| | 3.0 | 498 | - | 0.1009 | |
| | 3.0120 | 500 | 0.0005 | - | |
| | 3.3133 | 550 | 0.0003 | - | |
| | 3.6145 | 600 | 0.0003 | - | |
| | 3.9157 | 650 | 0.0011 | - | |
| | 4.0 | 664 | - | 0.1002 | |
|
|
| * The bold row denotes the saved checkpoint. |
| ### Framework Versions |
| - Python: 3.10.10 |
| - SetFit: 1.0.3 |
| - Sentence Transformers: 3.0.1 |
| - Transformers: 4.42.3 |
| - PyTorch: 2.2.1+cu121 |
| - Datasets: 2.20.0 |
| - Tokenizers: 0.19.1 |
|
|
| ## Citation |
|
|
| ### BibTeX |
| ```bibtex |
| @article{https://doi.org/10.48550/arxiv.2209.11055, |
| doi = {10.48550/ARXIV.2209.11055}, |
| url = {https://arxiv.org/abs/2209.11055}, |
| author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
| keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
| title = {Efficient Few-Shot Learning Without Prompts}, |
| publisher = {arXiv}, |
| year = {2022}, |
| copyright = {Creative Commons Attribution 4.0 International} |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
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| *Clearly define terms in order to be accessible across audiences.* |
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| ## Model Card Authors |
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| ## Model Card Contact |
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| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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