JohanHeinsen/Labour_ads_gender

SetFit with JohanHeinsen/OldNewsSegmentationSBERTV0.1

This is a SetFit model used to classify gender in labour advertisements from the eigtheenth and nineteenth centuries. It was trained by Sofus Landor Dam and Johan Heinsen.

The model has been trained using an efficient few-shot learning technique that involves:

1. 1.Fine-tuning a Sentence Transformer with contrastive learning.
2. 2.Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

• —Model Type: SetFit
• —Sentence Transformer body: JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
• —Classification head: a LogisticRegression instance
• —Maximum Sequence Length: 512 tokens
• —Number of Classes: 2 classes <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

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

Model Labels

Evaluation

Metrics

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("JohanHeinsen/Labour_ads_gender")
# Run inference
preds = model("En Stuepige, som forstaaer hvad hun bør, søger til Paaske; er at finde i Dronningens Tvergade Nr. 363 i Stuen.")

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Training Details

Training Set Metrics

Training Hyperparameters

• —batch_size: (16, 16)
• —num_epochs: (3, 3)
• —max_steps: -1
• —sampling_strategy: oversampling
• —num_iterations: 12
• —bodylearningrate: (2e-05, 2e-05)
• —headlearningrate: 2e-05
• —loss: CosineSimilarityLoss
• —distancemetric: cosinedistance
• —margin: 0.25
• —endtoend: False
• —use_amp: False
• —warmup_proportion: 0.1
• —l2_weight: 0.01
• —seed: 42
• —evalmaxsteps: -1
• —loadbestmodelatend: False

Training Results

Framework Versions

• —Python: 3.11.12
• —SetFit: 1.1.3
• —Sentence Transformers: 4.1.0
• —Transformers: 4.51.3
• —PyTorch: 2.7.0
• —Datasets: 2.19.2
• —Tokenizers: 0.21.1

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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