adriansanz/test8

SetFit with adriansanz/halfine

This is a SetFit model that can be used for Text Classification. This SetFit model uses adriansanz/halfine 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: adriansanz/halfine
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 512 tokens
• Number of Classes: 17 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	'Aquest article tracta sobre Aigües' 'Aquest article tracta sobre Aigües' 'Aquest article tracta sobre Aigües'
1	'Aquest article tracta sobre Consum, comerç i mercats' 'Aquest article tracta sobre Consum, comerç i mercats' 'Aquest article tracta sobre Consum, comerç i mercats'
2	'Aquest article tracta sobre Cultura' 'Aquest article tracta sobre Cultura' 'Aquest article tracta sobre Cultura'
3	'Aquest article tracta sobre Economia' 'Aquest article tracta sobre Economia' 'Aquest article tracta sobre Economia'
4	'Aquest article tracta sobre Educació' 'Aquest article tracta sobre Educació' 'Aquest article tracta sobre Educació'
5	'Aquest article tracta sobre Enllumenat públic' 'Aquest article tracta sobre Enllumenat públic' 'Aquest article tracta sobre Enllumenat públic'
6	'Aquest article tracta sobre Esports' 'Aquest article tracta sobre Esports' 'Aquest article tracta sobre Esports'
7	'Aquest article tracta sobre Habitatge' 'Aquest article tracta sobre Habitatge' 'Aquest article tracta sobre Habitatge'
8	'Aquest article tracta sobre Horta' 'Aquest article tracta sobre Horta' 'Aquest article tracta sobre Horta'
9	'Aquest article tracta sobre Medi ambient' 'Aquest article tracta sobre Medi ambient' 'Aquest article tracta sobre Medi ambient'
10	'Aquest article tracta sobre Neteja de la via pública' 'Aquest article tracta sobre Neteja de la via pública' 'Aquest article tracta sobre Neteja de la via pública'
11	'Aquest article tracta sobre Salut pública i Cementiri' 'Aquest article tracta sobre Salut pública i Cementiri' 'Aquest article tracta sobre Salut pública i Cementiri'
12	'Aquest article tracta sobre Seguretat' 'Aquest article tracta sobre Seguretat' 'Aquest article tracta sobre Seguretat'
13	'Aquest article tracta sobre Serveis socials' 'Aquest article tracta sobre Serveis socials' 'Aquest article tracta sobre Serveis socials'
14	'Aquest article tracta sobre Tramitacions' 'Aquest article tracta sobre Tramitacions' 'Aquest article tracta sobre Tramitacions'
15	'Aquest article tracta sobre Urbanisme' 'Aquest article tracta sobre Urbanisme' 'Aquest article tracta sobre Urbanisme'
16	'Aquest article tracta sobre Via pública i mobilitat' 'Aquest article tracta sobre Via pública i mobilitat' 'Aquest article tracta sobre Via pública i mobilitat'

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/test8")
# Run inference
preds = model("una bombeta fosa en una farola : al carrer antoni agusti al nº 9 hi ha una farola amb una bombeta fosa fa dies que i está")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	5	5.9412	9

Label	Training Sample Count
0	8
1	8
2	8
3	8
4	8
5	8
6	8
7	8
8	8
9	8
10	8
11	8
12	8
13	8
14	8
15	8
16	8

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

Framework Versions

• Python: 3.10.12
• SetFit: 1.0.3
• Sentence Transformers: 2.7.0
• Transformers: 4.40.1
• PyTorch: 2.2.1+cu121
• Datasets: 2.19.1
• 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}
}