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SetFit with sentence-transformers/distiluse-base-multilingual-cased-v1

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/distiluse-base-multilingual-cased-v1 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
0
  • 'Federer, Nadal y Djoković han gobernado con mano de hierro el tenis mundial en esta era. Se va el primero, el que empezó a instalar la tiranía. No somos conscientes de lo que se va con Roger. Afortunados de poder vivir uno de los momentos del deporte más gloriosos. Sin dudas.'
  • 'Nuestro primer viaje deportivo mayerlingaranguren ! El primero de muchos... en Distrito Federal,…'
  • 'Por nuestro país y el futuro de nuestros hijos.'
1
  • '¡Aquí estoy!, la depresión y tristeza por problemas económicos no me va a matar. Viviré a pesar de los 3 $ que como ingeniero jubilado me pagan mensual el gobierno. Ofrezco mis servicios en impermeabilización de techos y platabandas. Me disculpan que lo haga por este medio.'
  • '.: Remontarse a los precios de diciembre de 2017 generará más desempleo'
  • 'Tengo media hora intentando comprar $ por banesco y no hay disponible en la mesa de cambio'

Evaluation

Metrics

Label Accuracy
all 1.0

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("setfit_model_id")
# Run inference
preds = model("Coño cuál juego de la violencia Henry,aquí la violencia viene de un solo lado,en El Tocuyo y Carora cazaron a esos muchachos como animales")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 30.0686 76
Label Training Sample Count
0 122
1 53

Training Hyperparameters

  • batch_size: (32, 32)
  • num_epochs: (2, 2)
  • max_steps: -1
  • sampling_strategy: oversampling
  • body_learning_rate: (0.0001, 0.0001)
  • head_learning_rate: 0.0001
  • 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
0.0018 1 0.408 -
0.0894 50 0.0144 -
0.1789 100 0.0002 -
0.2683 150 0.0 -
0.3578 200 0.0 -
0.4472 250 0.0 -
0.5367 300 0.0 -
0.6261 350 0.0 -
0.7156 400 0.0 -
0.8050 450 0.0 -
0.8945 500 0.0 -
0.9839 550 0.0 -
1.0733 600 0.0 -
1.1628 650 0.0 -
1.2522 700 0.0 -
1.3417 750 0.0 -
1.4311 800 0.0 -
1.5206 850 0.0 -
1.6100 900 0.0 -
1.6995 950 0.0 -
1.7889 1000 0.0 -
1.8784 1050 0.0 -
1.9678 1100 0.0 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.1
  • Sentence Transformers: 2.2.2
  • Transformers: 4.35.2
  • PyTorch: 2.1.0+cu118
  • Datasets: 2.15.0
  • Tokenizers: 0.15.0

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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