SetFit with Alibaba-NLP/gte-multilingual-base

This is a SetFit model trained on the tmp-org/edeka-dataset-ctx-1 dataset that can be used for Text Classification. This SetFit model uses Alibaba-NLP/gte-multilingual-base 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: Alibaba-NLP/gte-multilingual-base
Classification head: a LogisticRegression instance
Maximum Sequence Length: 8192 tokens
Number of Classes: 24 classes
Training Dataset: tmp-org/edeka-dataset-ctx-1

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
Other_Prospekt	'Prospekt () [TextView
Start_Start	'Tierischer Sammelspaß mit schleich® und WWF! () [TextView
Other_Code einlösen	'Hallo Murmuras! () [TextView
Prämien_Prämien	'Noch 5 Treuepunkte zum Prämien-Sonderpreis. () [TextView
Other_Loading	'Noch 5 Treuepunkte zum Prämien-Sonderpreis. () [TextView
Other_Treueaktionen	'Kaufe 4 Packungen 3/4er & Mini - bekomme Coupon für 1 Gratis-Artikel () [TextView
Other_Neuigkeiten	'EDEKA Schöck () [TextView
Other_Produktherkunft	'Aktuelles () [TextView
Other_Marktsuche	'Produktherkunft () [TextView
Other_Menu	'PAYBACK () [TextView
Kasse_Mobil bezahlen	' (Tabelle schließen) [View
Other_Kassenbons	' (Tabelle schließen) [View
Sparen_Angebote	" (Aktivierte Coupons) [View
Sparen_Coupons	'PAYBACK () [TextView
Other_Coupon details	' (coupon image) [ImageView
Kasse_Kasse	'Coupons () [TextView
Kasse_Aktivierte Coupons	' (Aktivierte Coupons) [View
Einkaufsliste_Einkaufsliste	'Coupons () [TextView
Other_Unknown	'Prospekt () [TextView
Kasse_Unknown	' (Tabelle schließen) [View
Start_Loading	'Gratisartikel im November: Gut&Günstig – Classic Spülmittel Apfelduft () [TextView
Sparen_Loading	'Zukünftige Treueaktionen () [TextView
Other_Other	'Obst & Gemüse () [TextView
Kasse_Loading	'Mobil bezahlen per App () [TextView

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("tmp-org/tmp_cv_model_2025_10_13_0")
# Run inference
preds = model("Prospekt () [TextView|SubActivity] | 1 / 12 () [TextView|SubActivity]

[SELECTED START]

Prospekt () [TextView|MainActivity] | 1 / 12 () [TextView|MainActivity]
[CONTEXT SEPARATOR]
Prospekt () [TextView|MainActivity] | 1 / 12 () [TextView|MainActivity]")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	9	269.5969	675

Label	Training Sample Count
Einkaufsliste_Einkaufsliste	31
Kasse_Aktivierte Coupons	40
Kasse_Kasse	18
Kasse_Loading	2
Kasse_Mobil bezahlen	6
Kasse_Unknown	1
Other_Code einlösen	2
Other_Coupon details	32
Other_Kassenbons	8
Other_Loading	2
Other_Marktsuche	6
Other_Menu	40
Other_Neuigkeiten	8
Other_Other	1
Other_Produktherkunft	13
Other_Prospekt	33
Other_Treueaktionen	36
Other_Unknown	4
Prämien_Prämien	38
Sparen_Angebote	40
Sparen_Coupons	40
Sparen_Loading	3
Start_Loading	5
Start_Start	40

Training Hyperparameters

batch_size: (4, 4)
num_epochs: (1, 1)
max_steps: -1
sampling_strategy: undersampling
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: 4242
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0003	1	0.0447	-
0.0133	50	0.2437	-
0.0266	100	0.1809	-
0.0399	150	0.135	-
0.0533	200	0.1713	-
0.0666	250	0.1265	-
0.0799	300	0.1339	-
0.0932	350	0.1054	-
0.1065	400	0.1501	-
0.1198	450	0.0962	-
0.1332	500	0.1028	-
0.1465	550	0.115	-
0.1598	600	0.1005	-
0.1731	650	0.0803	-
0.1864	700	0.0666	-
0.1997	750	0.0982	-
0.2130	800	0.0703	-
0.2264	850	0.0549	-
0.2397	900	0.0518	-
0.2530	950	0.0357	-
0.2663	1000	0.0503	-
0.2796	1050	0.0541	-
0.2929	1100	0.0407	-
0.3063	1150	0.0534	-
0.3196	1200	0.0382	-
0.3329	1250	0.0406	-
0.3462	1300	0.0427	-
0.3595	1350	0.0432	-
0.3728	1400	0.033	-
0.3862	1450	0.0341	-
0.3995	1500	0.0347	-
0.4128	1550	0.0362	-
0.4261	1600	0.0645	-
0.4394	1650	0.0357	-
0.4527	1700	0.0383	-
0.4660	1750	0.0374	-
0.4794	1800	0.0345	-
0.4927	1850	0.0254	-
0.5060	1900	0.0369	-
0.5193	1950	0.0328	-
0.5326	2000	0.0347	-
0.5459	2050	0.0228	-
0.5593	2100	0.0367	-
0.5726	2150	0.0225	-
0.5859	2200	0.0266	-
0.5992	2250	0.0164	-
0.6125	2300	0.0261	-
0.6258	2350	0.0181	-
0.6391	2400	0.0354	-
0.6525	2450	0.0333	-
0.6658	2500	0.0294	-
0.6791	2550	0.0229	-
0.6924	2600	0.0268	-
0.7057	2650	0.0252	-
0.7190	2700	0.03	-
0.7324	2750	0.0395	-
0.7457	2800	0.0377	-
0.7590	2850	0.0216	-
0.7723	2900	0.0287	-
0.7856	2950	0.0186	-
0.7989	3000	0.0267	-
0.8123	3050	0.0294	-
0.8256	3100	0.0097	-
0.8389	3150	0.0204	-
0.8522	3200	0.0288	-
0.8655	3250	0.0238	-
0.8788	3300	0.0274	-
0.8921	3350	0.0253	-
0.9055	3400	0.016	-
0.9188	3450	0.015	-
0.9321	3500	0.0368	-
0.9454	3550	0.0162	-
0.9587	3600	0.0238	-
0.9720	3650	0.0385	-
0.9854	3700	0.0428	-
0.9987	3750	0.0121	-

Framework Versions

Python: 3.12.6
SetFit: 1.1.2
Sentence Transformers: 5.2.2
Transformers: 4.57.1
PyTorch: 2.10.0+cu128
Datasets: 3.6.0
Tokenizers: 0.22.2

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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Model size

0.3B params

Tensor type

F32

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Alibaba-NLP/gte-multilingual-base

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Paper for tmp-org/tmp_cv_model_2025_10_13_0

Efficient Few-Shot Learning Without Prompts

Paper • 2209.11055 • Published Sep 22, 2022 • 4