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faodl/
model_child_and_family_support_benefits_mpnet_30_sample

Name: model_child_and_family_support_benefits_mpnet_30_sample
Availability: InStock

setfit

safetensors

xlm-roberta

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

sentence-transformers/paraphrase-multilingual-mpnet-base-v2

finetune

model-index

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SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-mpnet-base-v2 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:

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

Model Details

Model Description

Model Type: SetFit
Sentence Transformer body: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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

Irrelevant

'Agri-business (Market access for agricultural products) \n\t4.'
'These are goals that translate into many programmes and policies, and countless \n\ninstitutional plans and activities.'
'Planning: developing synergies between different \ntypes of infrastructure that facilitate socio-economic \nintegration and the timely delivery of aid in crisis.'

Relevant

'Fiscal policies that sustain non-contributory family benefits ensure that the most disadvantaged children receive continuous support regardless of labor market fluctuations.'
'Social protection instruments that prioritize children living in poor households have a multiplier effect, positively influencing nutrition, education, and health indicators.'
'The expansion of benefits coverage to include all children, irrespective of socioeconomic status, underscores a commitment to universality and equitable social protection.'

Evaluation

Metrics

Label	Accuracy	F1_Score	Precision	Recall
all	1.0	1.0	1.0	1.0

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("faodl/model_child_and_family_support_benefits_mpnet_30_sample")
# Run inference
preds = model("There are challenges in the labour market 

regarding realization of decent work for the majority of workers.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	25.3542	95

Label	Training Sample Count
Irrelevant	24
Relevant	24

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
0.0083	1	0.1695	-
0.4167	50	0.0676	-
0.8333	100	0.0008	-

Framework Versions

Python: 3.11.13
SetFit: 1.1.2
Sentence Transformers: 4.1.0
Transformers: 4.52.4
PyTorch: 2.6.0+cu124
Datasets: 3.6.0
Tokenizers: 0.21.1

Citation

BibTeX

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}
}