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saraestevez
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setfit-minilm-bank-tweets-processed-200

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
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SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 as the Sentence Transformer embedding model. A LogisticRegression 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 with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
relevant
  • '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.'
  • '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.'
  • '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.'
discard
  • 'Banco Santander ofrece una cuenta en línea sin comisiones y un bono de 400€ por domiciliar tu nómina.'
  • 'El BBVA fue el banco que peor me trató al tener que contratar productos innecesarios para conseguir mi primera hipoteca de funcionario.'
  • 'CaixaBank se destaca como líder del sector bancario gracias a su sólido crecimiento y eficiencia operativa, convirtiéndose en una opción atractiva para inversores.'

Evaluation

Metrics

Label Accuracy
all 0.7739

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("saraestevez/setfit-minilm-bank-tweets-processed-200")
# Run inference
preds = model("Los resultados del Banco Sabadell impulsan al IBEX 35.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 21.3275 41
Label Training Sample Count
discard 200
relevant 200

Training Hyperparameters

  • batch_size: (16, 2)
  • num_epochs: (1, 16)
  • 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: False

Training Results

Epoch Step Training Loss Validation Loss
0.0002 1 0.4199 -
0.0100 50 0.3357 -
0.0199 100 0.3198 -
0.0299 150 0.2394 -
0.0398 200 0.2411 -
0.0498 250 0.2277 -
0.0597 300 0.1876 -
0.0697 350 0.1481 -
0.0796 400 0.1533 -
0.0896 450 0.0145 -
0.0995 500 0.0113 -
0.1095 550 0.0045 -
0.1194 600 0.0201 -
0.1294 650 0.0008 -
0.1393 700 0.0003 -
0.1493 750 0.0003 -
0.1592 800 0.0003 -
0.1692 850 0.0001 -
0.1791 900 0.0001 -
0.1891 950 0.0001 -
0.1990 1000 0.0001 -
0.2090 1050 0.0001 -
0.2189 1100 0.0002 -
0.2289 1150 0.0001 -
0.2388 1200 0.0001 -
0.2488 1250 0.0001 -
0.2587 1300 0.0 -
0.2687 1350 0.0001 -
0.2786 1400 0.0001 -
0.2886 1450 0.0001 -
0.2985 1500 0.0 -
0.3085 1550 0.0001 -
0.3184 1600 0.0 -
0.3284 1650 0.0 -
0.3383 1700 0.0 -
0.3483 1750 0.0001 -
0.3582 1800 0.0 -
0.3682 1850 0.0 -
0.3781 1900 0.0 -
0.3881 1950 0.0 -
0.3980 2000 0.0 -
0.4080 2050 0.0 -
0.4179 2100 0.0 -
0.4279 2150 0.0 -
0.4378 2200 0.0 -
0.4478 2250 0.0 -
0.4577 2300 0.0 -
0.4677 2350 0.0 -
0.4776 2400 0.0 -
0.4876 2450 0.0 -
0.4975 2500 0.0 -
0.5075 2550 0.0 -
0.5174 2600 0.0 -
0.5274 2650 0.0 -
0.5373 2700 0.0 -
0.5473 2750 0.0 -
0.5572 2800 0.0 -
0.5672 2850 0.0 -
0.5771 2900 0.0 -
0.5871 2950 0.0 -
0.5970 3000 0.0 -
0.6070 3050 0.0 -
0.6169 3100 0.0 -
0.6269 3150 0.0 -
0.6368 3200 0.0 -
0.6468 3250 0.0 -
0.6567 3300 0.0 -
0.6667 3350 0.0 -
0.6766 3400 0.0 -
0.6866 3450 0.0 -
0.6965 3500 0.0 -
0.7065 3550 0.0 -
0.7164 3600 0.0 -
0.7264 3650 0.0 -
0.7363 3700 0.0 -
0.7463 3750 0.0 -
0.7562 3800 0.0 -
0.7662 3850 0.0 -
0.7761 3900 0.0 -
0.7861 3950 0.0 -
0.7960 4000 0.0 -
0.8060 4050 0.0 -
0.8159 4100 0.0 -
0.8259 4150 0.0 -
0.8358 4200 0.0 -
0.8458 4250 0.0 -
0.8557 4300 0.0 -
0.8657 4350 0.0 -
0.8756 4400 0.0 -
0.8856 4450 0.0 -
0.8955 4500 0.0 -
0.9055 4550 0.0 -
0.9154 4600 0.0 -
0.9254 4650 0.0 -
0.9353 4700 0.0 -
0.9453 4750 0.0 -
0.9552 4800 0.0 -
0.9652 4850 0.0 -
0.9751 4900 0.0 -
0.9851 4950 0.0 -
0.9950 5000 0.0 -

Framework Versions

  • Python: 3.11.0rc1
  • SetFit: 1.0.3
  • Sentence Transformers: 2.7.0
  • Transformers: 4.39.0
  • PyTorch: 2.3.1+cu121
  • Datasets: 2.19.1
  • Tokenizers: 0.15.2

Citation

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}
}
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Evaluation results

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