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--- |
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base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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library_name: setfit |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: Para saber si un negocio va a funcionar, es necesario realizar un estudio |
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de mercado, valorar la economía local durante un año, considerar la afluencia |
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de personas y la ubicación, así como determinar el tamaño de la inversión. |
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- text: Apoyo la opinión de Tyrexito y también reclamo al Banco Sabadell por sus comisiones. |
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- text: Los resultados del Banco Sabadell impulsan al IBEX 35. |
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- text: Aunque no pude retirar el bono de festividad en el cajero, ING y AKBANK rechazaron |
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mis quejas, pero tras anunciar una denuncia, me transfirieron el dinero en una |
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hora; si tienes razón, no te rindas. |
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- text: El Gobierno presentará al nuevo gobernador del Banco de España en una Comisión |
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del Congreso este jueves. |
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inference: true |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.7739130434782608 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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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. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 128 tokens |
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- **Number of Classes:** 2 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:---------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| relevant | <ul><li>'Nuevo caso de phishing relacionado con Abanca, registrado el 23 de julio de 2024, con la URL: /www.inicio-abanca.com/es/WELE200M_Logon_Ini.aspx.'</li><li>'Una alumna que trabajó en Bancomer reveló un esquema de robo en el que dos cajeros afirmaban que un cliente había depositado mil pesos en un pago de dos mil y se quedaban con la mitad cada uno.'</li><li>'Las previsiones de crecimiento de España para 2024 han mejorado según diversas organizaciones, con estimaciones que oscilan entre el 1,8% y el 2,4%, impulsadas por turismo, exportaciones y trabajadores extranjeros.'</li></ul> | |
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| discard | <ul><li>'Banco Santander ofrece una cuenta en línea sin comisiones y un bono de 400€ por domiciliar tu nómina.'</li><li>'El BBVA fue el banco que peor me trató al tener que contratar productos innecesarios para conseguir mi primera hipoteca de funcionario.'</li><li>'CaixaBank se destaca como líder del sector bancario gracias a su sólido crecimiento y eficiencia operativa, convirtiéndose en una opción atractiva para inversores.'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.7739 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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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 setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("saraestevez/setfit-minilm-bank-tweets-processed-200") |
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# Run inference |
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preds = model("Los resultados del Banco Sabadell impulsan al IBEX 35.") |
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``` |
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### Out-of-Scope Use |
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## Bias, Risks and Limitations |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 1 | 21.3275 | 41 | |
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| Label | Training Sample Count | |
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|:---------|:----------------------| |
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| discard | 200 | |
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| relevant | 200 | |
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### Training Hyperparameters |
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- batch_size: (16, 2) |
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- num_epochs: (1, 16) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0002 | 1 | 0.4199 | - | |
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| 0.0100 | 50 | 0.3357 | - | |
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| 0.0199 | 100 | 0.3198 | - | |
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| 0.0299 | 150 | 0.2394 | - | |
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| 0.0398 | 200 | 0.2411 | - | |
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| 0.0498 | 250 | 0.2277 | - | |
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| 0.0597 | 300 | 0.1876 | - | |
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| 0.0697 | 350 | 0.1481 | - | |
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| 0.0796 | 400 | 0.1533 | - | |
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| 0.0896 | 450 | 0.0145 | - | |
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| 0.0995 | 500 | 0.0113 | - | |
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| 0.1095 | 550 | 0.0045 | - | |
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| 0.1194 | 600 | 0.0201 | - | |
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| 0.1294 | 650 | 0.0008 | - | |
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| 0.1393 | 700 | 0.0003 | - | |
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| 0.1493 | 750 | 0.0003 | - | |
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| 0.1592 | 800 | 0.0003 | - | |
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| 0.1692 | 850 | 0.0001 | - | |
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| 0.1791 | 900 | 0.0001 | - | |
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| 0.1891 | 950 | 0.0001 | - | |
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| 0.1990 | 1000 | 0.0001 | - | |
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| 0.2090 | 1050 | 0.0001 | - | |
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| 0.2189 | 1100 | 0.0002 | - | |
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| 0.2289 | 1150 | 0.0001 | - | |
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| 0.2388 | 1200 | 0.0001 | - | |
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| 0.2488 | 1250 | 0.0001 | - | |
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| 0.2587 | 1300 | 0.0 | - | |
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| 0.2687 | 1350 | 0.0001 | - | |
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| 0.2786 | 1400 | 0.0001 | - | |
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| 0.2886 | 1450 | 0.0001 | - | |
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| 0.2985 | 1500 | 0.0 | - | |
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| 0.3085 | 1550 | 0.0001 | - | |
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| 0.3184 | 1600 | 0.0 | - | |
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| 0.3284 | 1650 | 0.0 | - | |
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| 0.3383 | 1700 | 0.0 | - | |
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| 0.3483 | 1750 | 0.0001 | - | |
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| 0.3582 | 1800 | 0.0 | - | |
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| 0.3682 | 1850 | 0.0 | - | |
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| 0.3781 | 1900 | 0.0 | - | |
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| 0.3881 | 1950 | 0.0 | - | |
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| 0.3980 | 2000 | 0.0 | - | |
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| 0.4080 | 2050 | 0.0 | - | |
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| 0.4179 | 2100 | 0.0 | - | |
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| 0.4279 | 2150 | 0.0 | - | |
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| 0.4378 | 2200 | 0.0 | - | |
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| 0.4478 | 2250 | 0.0 | - | |
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| 0.4577 | 2300 | 0.0 | - | |
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| 0.4677 | 2350 | 0.0 | - | |
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| 0.4776 | 2400 | 0.0 | - | |
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| 0.4876 | 2450 | 0.0 | - | |
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| 0.4975 | 2500 | 0.0 | - | |
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| 0.5075 | 2550 | 0.0 | - | |
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| 0.5174 | 2600 | 0.0 | - | |
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| 0.5274 | 2650 | 0.0 | - | |
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| 0.5373 | 2700 | 0.0 | - | |
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| 0.5473 | 2750 | 0.0 | - | |
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| 0.5572 | 2800 | 0.0 | - | |
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| 0.5672 | 2850 | 0.0 | - | |
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| 0.5771 | 2900 | 0.0 | - | |
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| 0.5871 | 2950 | 0.0 | - | |
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| 0.5970 | 3000 | 0.0 | - | |
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| 0.6070 | 3050 | 0.0 | - | |
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| 0.6169 | 3100 | 0.0 | - | |
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| 0.6269 | 3150 | 0.0 | - | |
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| 0.6368 | 3200 | 0.0 | - | |
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| 0.6468 | 3250 | 0.0 | - | |
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| 0.6567 | 3300 | 0.0 | - | |
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| 0.6667 | 3350 | 0.0 | - | |
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| 0.6766 | 3400 | 0.0 | - | |
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| 0.6866 | 3450 | 0.0 | - | |
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| 0.6965 | 3500 | 0.0 | - | |
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| 0.7065 | 3550 | 0.0 | - | |
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| 0.7164 | 3600 | 0.0 | - | |
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| 0.7264 | 3650 | 0.0 | - | |
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| 0.7363 | 3700 | 0.0 | - | |
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| 0.7463 | 3750 | 0.0 | - | |
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| 0.7562 | 3800 | 0.0 | - | |
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| 0.7662 | 3850 | 0.0 | - | |
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| 0.7761 | 3900 | 0.0 | - | |
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| 0.7861 | 3950 | 0.0 | - | |
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| 0.7960 | 4000 | 0.0 | - | |
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| 0.8060 | 4050 | 0.0 | - | |
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| 0.8159 | 4100 | 0.0 | - | |
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| 0.8259 | 4150 | 0.0 | - | |
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| 0.8358 | 4200 | 0.0 | - | |
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| 0.8458 | 4250 | 0.0 | - | |
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| 0.8557 | 4300 | 0.0 | - | |
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| 0.8657 | 4350 | 0.0 | - | |
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| 0.8756 | 4400 | 0.0 | - | |
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| 0.8856 | 4450 | 0.0 | - | |
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| 0.8955 | 4500 | 0.0 | - | |
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| 0.9055 | 4550 | 0.0 | - | |
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| 0.9154 | 4600 | 0.0 | - | |
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| 0.9254 | 4650 | 0.0 | - | |
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| 0.9353 | 4700 | 0.0 | - | |
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| 0.9453 | 4750 | 0.0 | - | |
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| 0.9552 | 4800 | 0.0 | - | |
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| 0.9652 | 4850 | 0.0 | - | |
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| 0.9751 | 4900 | 0.0 | - | |
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| 0.9851 | 4950 | 0.0 | - | |
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| 0.9950 | 5000 | 0.0 | - | |
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### Framework Versions |
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- Python: 3.11.0rc1 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.7.0 |
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- Transformers: 4.39.0 |
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- PyTorch: 2.3.1+cu121 |
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- Datasets: 2.19.1 |
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- Tokenizers: 0.15.2 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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