nyylisaa/skinwise-setfit-model

SetFit with BAAI/bge-base-en-v1.5

This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-base-en-v1.5 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: BAAI/bge-base-en-v1.5
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 512 tokens
• Number of Classes: 13 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
ANTIOXIDANT	'Quercetin' 'Vitamin K1' 'Caffeine'
BARRIER_SUPPORT	'Cholesterol' 'Ceramides' 'Glycosphingolipids'
DIRECT_ACID	'Capryloyl Salicylic Acid' 'Willow Bark Extract' 'Citric Acid'
EMOLLIENT	'Mango Seed Butter' 'Argan Oil' 'Sweet Almond Oil'
HUMECTANT	'Glycerol' 'Serine' 'Taurine'
NIACINAMIDE	'Nicotinamide' 'Niacinamide Serum' 'Niacinamide'
NON_TARGET	'Turmeric Root Extract' 'Alpha-Glucan Oligosaccharide' 'Neurophroline'
PEPTIDE	'Acetyl Hexapeptide' 'Myristol Pentapeptide' 'Palmitoyl Tripeptide'
PIGMENT_REGULATOR	'Zinc Gluconate' 'Zinc Sulfate' 'Arbutin'
RETINOID	'Tretinoin Cream' 'Adapalene Gel' 'Bakuchiol'
SOOTHING	'Chamomile Extract' 'Magnesium Sulfate' 'Centella Asiatica'
UV_FILTER	'Octinoxate' 'Zinc Oxide' 'Butyl Methoxydibenzoylmethane'
VITAMIN_C	'Vitamin C' 'Ascorbyl Palmitate' 'Magnesium Ascorbyl Phosphate'

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("setfit_model_id")
# Run inference
preds = model("Lysine")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	1.9836	5

Label	Training Sample Count
ANTIOXIDANT	15
BARRIER_SUPPORT	17
DIRECT_ACID	13
EMOLLIENT	11
HUMECTANT	30
NIACINAMIDE	8
NON_TARGET	40
PEPTIDE	33
PIGMENT_REGULATOR	15
RETINOID	12
SOOTHING	27
UV_FILTER	14
VITAMIN_C	9

Training Hyperparameters

• batch_size: (16, 16)
• num_epochs: (1, 1)
• max_steps: -1
• sampling_strategy: oversampling
• num_iterations: 10
• 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
• l2_weight: 0.01
• seed: 42
• eval_max_steps: -1
• load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0033	1	0.1906	-
0.1639	50	0.2095	-
0.3279	100	0.1719	-
0.4918	150	0.1369	-
0.6557	200	0.1034	-
0.8197	250	0.0909	-
0.9836	300	0.0921	-

Framework Versions

• Python: 3.13.7
• SetFit: 1.2.0
• Sentence Transformers: 6.1.0
• Transformers: 5.19.0
• PyTorch: 2.14.1+cpu
• Datasets: 5.1.0
• Tokenizers: 0.23.3

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}
}