simonepapa/setfit-baritoday-multilabel

SetFit with nickprock/sentence-bert-base-italian-uncased

This is a SetFit model that can be used for Text Classification. This SetFit model uses nickprock/sentence-bert-base-italian-uncased as the Sentence Transformer embedding model. A OneVsRestClassifier 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 Type: SetFit
• Sentence Transformer body: nickprock/sentence-bert-base-italian-uncased
• Classification head: a OneVsRestClassifier instance
• Maximum Sequence Length: 512 tokens

Model Sources

• Repository: SetFit on GitHub
• Paper: Efficient Few-Shot Learning Without Prompts
• Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

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("simonepapa/setfit-baritoday-multilabel")
# Run inference
preds = model("Un'auto è finita, nel primo pomeriggio, sui paletti parapedonali del t5eatro petruzzelli, danneggiandoli: a guidare il mezzo, un uomo di 75 anni. Sul posto sono intervenute le pattuglie della Polizia Locale e un'autogru. Accertamenti in corso per stabilire la dinamica degli episodi. Il conducente sarà segnalato per richiesta di revisione accertamenti psicofisici e sanzionato per i danni causati.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	42	154.4615	1030

Training Hyperparameters

• batch_size: (16, 16)
• num_epochs: (1, 1)
• max_steps: -1
• sampling_strategy: oversampling
• num_iterations: 20
• body_learning_rate: (2e-05, 2e-05)
• head_learning_rate: 2e-05
• 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
• eval_max_steps: -1
• load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0038	1	0.1775	-
0.1923	50	0.1375	-
0.3846	100	0.0755	-
0.5769	150	0.0521	-
0.7692	200	0.0456	-
0.9615	250	0.0446	-

Framework Versions

• Python: 3.10.5
• SetFit: 1.1.0
• Sentence Transformers: 3.3.1
• Transformers: 4.42.2
• PyTorch: 2.5.1+cu124
• 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}
}