SetFit with projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base

This is a SetFit model that can be used for Text Classification. This SetFit model uses projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base 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
  • "Sóc ciutadà i m'agradaria saber quin és el tràmit per a la renovació del DNI."
  • "Quin és el propòsit de la garantia per a l'abocament controlat de runes?"
  • 'Quin és el benefici de la devolució de fiances i avals?'
1
  • "Aquest text és Saludo per a un cercador de tràmits d'un ajuntament"
  • 'Bon dia, vull saber més sobre els tràmits disponibles.'
  • "Bona nit, com t'has anat acostant al final del dia?"

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("adriansanz/gret4")
# Run inference
preds = model("Hola!")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 9.3444 17
Label Training Sample Count
0 45
1 45

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (3, 3)
  • 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
  • l2_weight: 0.01
  • seed: 42
  • evaluation_strategy: epoch
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0039 1 0.2366 -
0.1931 50 0.1287 -
0.3861 100 0.0039 -
0.5792 150 0.0003 -
0.7722 200 0.0001 -
0.9653 250 0.0001 -
1.0 259 - 0.0001
1.1583 300 0.0001 -
1.3514 350 0.0001 -
1.5444 400 0.0001 -
1.7375 450 0.0001 -
1.9305 500 0.0001 -
2.0 518 - 0.0001
2.1236 550 0.0 -
2.3166 600 0.0 -
2.5097 650 0.0 -
2.7027 700 0.0 -
2.8958 750 0.0 -
3.0 777 - 0.0001

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.1.0
  • Sentence Transformers: 3.2.1
  • Transformers: 4.42.2
  • PyTorch: 2.5.0+cu121
  • Datasets: 3.1.0
  • Tokenizers: 0.19.1

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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