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faodl/
model_cca_multilabel_MiniLM-L12-v01

Name: model_cca_multilabel_MiniLM-L12-v01
Availability: InStock

setfit

safetensors

bert

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

finetune

text-embeddings-inference

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SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 as the Sentence Transformer embedding model. A OneVsRestClassifier 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-MiniLM-L12-v2
Classification head: a OneVsRestClassifier instance
Maximum Sequence Length: 128 tokens

Model Sources

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

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_cca_multilabel_MiniLM-L12-v01")
# Run inference
preds = model("To monitor market dynamics and inform policy responses, the government will track the retail value of ultra-processed foods and analyze shifts in consumption in relation to labeling and advertising reforms. Data from these analyses will feed annual dashboards that link labeling density, promotional intensity, and dietary outcomes to guide targeted interventions and budget planning.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	123.6200	951

Training Hyperparameters

batch_size: (32, 32)
num_epochs: (2, 2)
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
0.0011	1	0.1892	-
0.0566	50	0.192	-
0.1131	100	0.1681	-
0.1697	150	0.1518	-
0.2262	200	0.1361	-
0.2828	250	0.1389	-
0.3394	300	0.1321	-
0.3959	350	0.1297	-
0.4525	400	0.1236	-
0.5090	450	0.1116	-
0.5656	500	0.1194	-
0.6222	550	0.1105	-
0.6787	600	0.1047	-
0.7353	650	0.1124	-
0.7919	700	0.1069	-
0.8484	750	0.108	-
0.9050	800	0.1072	-
0.9615	850	0.1011	-
1.0181	900	0.098	-
1.0747	950	0.0893	-
1.1312	1000	0.0979	-
1.1878	1050	0.0967	-
1.2443	1100	0.0887	-
1.3009	1150	0.0908	-
1.3575	1200	0.0906	-
1.4140	1250	0.0869	-
1.4706	1300	0.0873	-
1.5271	1350	0.0943	-
1.5837	1400	0.0886	-
1.6403	1450	0.0911	-
1.6968	1500	0.0832	-
1.7534	1550	0.0859	-
1.8100	1600	0.0862	-
1.8665	1650	0.09	-
1.9231	1700	0.0836	-
1.9796	1750	0.0884	-

Framework Versions

Python: 3.12.12
SetFit: 1.1.3
Sentence Transformers: 5.1.1
Transformers: 4.57.1
PyTorch: 2.8.0+cu126
Datasets: 4.0.0
Tokenizers: 0.22.1

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}
}