tmp-org/tmp_cv_model_2025_09_29_0

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:

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: 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
Kasse_Unknown	' (Tabelle schließen) [View
Other_Unknown	' (Auf die Einkaufliste) [ImageView
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_09_29_0")
# Run inference
preds = model("Ja, sehr! () [TextView|SubActivity]

[SELECTED START]

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

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	9	266.6559	711

Label	Training Sample Count
Einkaufsliste_Einkaufsliste	27
Kasse_Aktivierte Coupons	36
Kasse_Kasse	18
Kasse_Loading	2
Kasse_Mobil bezahlen	6
Kasse_Unknown	1
Other_Code einlösen	2
Other_Coupon details	28
Other_Kassenbons	9
Other_Loading	2
Other_Marktsuche	6
Other_Menu	36
Other_Neuigkeiten	4
Other_Other	1
Other_Produktherkunft	7
Other_Prospekt	29
Other_Treueaktionen	32
Other_Unknown	8
Prämien_Prämien	34
Sparen_Angebote	36
Sparen_Coupons	36
Sparen_Loading	3
Start_Loading	5
Start_Start	36

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	0.1534	-
0.0166	50	0.2317	-
0.0331	100	0.1725	-
0.0497	150	0.1498	-
0.0663	200	0.1253	-
0.0828	250	0.1364	-
0.0994	300	0.0826	-
0.1160	350	0.1027	-
0.1325	400	0.0815	-
0.1491	450	0.0738	-
0.1657	500	0.0501	-
0.1822	550	0.0653	-
0.1988	600	0.0657	-
0.2154	650	0.0497	-
0.2319	700	0.0575	-
0.2485	750	0.0563	-
0.2651	800	0.0483	-
0.2816	850	0.0639	-
0.2982	900	0.0515	-
0.3148	950	0.0436	-
0.3313	1000	0.0465	-
0.3479	1050	0.0339	-
0.3645	1100	0.033	-
0.3810	1150	0.05	-
0.3976	1200	0.0486	-
0.4142	1250	0.0388	-
0.4307	1300	0.032	-
0.4473	1350	0.0268	-
0.4639	1400	0.0163	-
0.4805	1450	0.0373	-
0.4970	1500	0.0226	-
0.5136	1550	0.033	-
0.5302	1600	0.0295	-
0.5467	1650	0.0518	-
0.5633	1700	0.0318	-
0.5799	1750	0.0164	-
0.5964	1800	0.0377	-
0.6130	1850	0.0204	-
0.6296	1900	0.0094	-
0.6461	1950	0.0363	-
0.6627	2000	0.0565	-
0.6793	2050	0.0289	-
0.6958	2100	0.0246	-
0.7124	2150	0.0327	-
0.7290	2200	0.0217	-
0.7455	2250	0.045	-
0.7621	2300	0.0145	-
0.7787	2350	0.0188	-
0.7952	2400	0.0154	-
0.8118	2450	0.009	-
0.8284	2500	0.0344	-
0.8449	2550	0.0324	-
0.8615	2600	0.0162	-
0.8781	2650	0.0209	-
0.8946	2700	0.0259	-
0.9112	2750	0.0236	-
0.9278	2800	0.0231	-
0.9443	2850	0.0138	-
0.9609	2900	0.0141	-
0.9775	2950	0.019	-
0.9940	3000	0.0237	-

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}
}