fede-m/FGSDI_final_setfit_fold_4

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 Type: SetFit
• Sentence Transformer body: sentence-transformers/distiluse-base-multilingual-cased-v1
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 128 tokens
• Number of Classes: 2 classes

Model Sources

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

Model Labels

Label	Examples
0	'La sua pagina Twitter si è trasformata in un diario spagnolo.' 'Allagamenti si sono verificati in strade terranei per il sollevamento dei tombini a causa della forte pressione dell’acqua.' 'In un post su Medium dal titolo Quello che il New York Times non ti ha raccontato , Carney [Jay Carney] ha attaccato duramente il metodo di lavoro dei due giornalisti [Jodi Kantor] [David Streifeld] che hanno curato l’inchiesta.'
1	'Arrestati cinque cittadini marocchini e due italiani, tra cui una donna, per rapina aggravata in concorso.' 'I promotori sono tredici organizzazioni di varie nazioni che si sono già riunite a Roma durante il sinodo del 2014 e danno vita al "Global Network of Rainbow Catholic", una rete internazionale di gay, lesbiche e trans cattolici.' 'Se avesse deciso di scrivere e basta [Vera Schiavazzi] sarebbe diventata una inviata vecchio stile, di quelli che sanno fare tutto e bene.'

Evaluation

Metrics

Label	Accuracy
all	0.5414

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("fede-m/FGSDI_final_setfit_fold_4")
# Run inference
preds = model("Tags Argomenti zona 2 milano Protagonisti: Adriano Manesco")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	37.3937	139

Label	Training Sample Count
0	45
1	242

Training Hyperparameters

• batch_size: (16, 16)
• num_epochs: (1, 1)
• max_steps: -1
• sampling_strategy: oversampling
• num_iterations: 10
• 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
0.0028	1	0.5866	-
0.1393	50	0.2196	-
0.2786	100	0.0584	-
0.4178	150	0.011	-
0.5571	200	0.003	-
0.6964	250	0.0017	-
0.8357	300	0.001	-
0.9749	350	0.0005	-

Framework Versions

• Python: 3.12.12
• SetFit: 1.1.3
• Sentence Transformers: 5.1.2
• Transformers: 4.57.1
• PyTorch: 2.8.0+cu126
• Datasets: 4.0.0
• Tokenizers: 0.22.1

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}
}