--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 'Endet in 5 Tagen () [TextView|CheckoutActivity] | Kaufe 4 Produkte von Axe - bekomme 40% Rabatt auf einen Axe Artikel () [TextView|CheckoutActivity] | Noch 4 Treuepunkte zum Prämien-Sonderpreis. () [TextView|CheckoutActivity] | Für jede Produkt Axe erhältst du einen Treuepunkt () [TextView|CheckoutActivity] | Endet in 12 Tagen () [TextView|CheckoutActivity] | Kaufe 5 Produkte von Coral, Domestos oder Viss und erhalten einen Dome () [TextView|CheckoutActivity] | Noch 5 Treuepunkte zum Prämien-Sonderpreis. () [TextView|CheckoutActivity] | Für Je Produkt Coral, Viss und oder Domestos erhältst du einen Treuepunkt () [TextView|CheckoutActivity] [SELECTED START] Noch 4 Treuepunkte zum Prämien-Sonderpreis. () [TextView|CheckoutActivity] | Für jede Produkt Axe erhältst du einen Treuepunkt () [TextView|CheckoutActivity] | Endet in 12 Tagen () [TextView|CheckoutActivity] | Kaufe 5 Produkte von Coral, Domestos oder Viss und erhalten einen Dome () [TextView|CheckoutActivity] | Noch 5 Treuepunkte zum Prämien-Sonderpreis. () [TextView|CheckoutActivity] | Für Je Produkt Coral, Viss und oder Domestos erhältst du einen Treuepunkt () [TextView|CheckoutActivity] [CONTEXT SEPARATOR] Noch 4 Treuepunkte zum Prämien-Sonderpreis. () [TextView|CheckoutActivity] | Für jede Produkt Axe erhältst du einen Treuepunkt () [TextView|CheckoutActivity] | Endet in 12 Tagen () [TextView|CheckoutActivity] | Kaufe 5 Produkte von Coral, Domestos oder Viss und erhalten einen Dome () [TextView|CheckoutActivity] | Noch 5 Treuepunkte zum Prämien-Sonderpreis. () [TextView|CheckoutActivity] | Für Je Produkt Coral, Viss und oder Domestos erhältst du einen Treuepunkt () [TextView|CheckoutActivity]' - text: '2 () [TextView|View] | . () [TextView|View] | 49 () [TextView|View] | aus Süddeutschland, Klasse I, Stück () [TextView|View] | (Zur Einkaufliste hinzufügen) [ImageView|View] | 6 () [TextView|View] | . () [TextView|View] | 99 () [TextView|View] | gebunden mit Lilien und Grün, ohne Vase, ca. 70 cm lang, • Nur in Märkten mit Blumenabteilung () [TextView|View] | (Zur Einkaufliste hinzufügen) [ImageView|View] | 1 () [TextView|View] | . () [TextView|View] | 59 () [TextView|View] | aus Süddeutschland, Topf () [TextView|View] | 1 () [TextView|View] | . () [TextView|View] | 29 () [TextView|View] | aus Süddeutschland, Klasse I, 750 g, (1 kg = 1,72) () [TextView|View] [SELECTED START] 2 () [TextView|View] | . () [TextView|View] | 49 () [TextView|View] | aus Süddeutschland, Klasse I, Stück () [TextView|View] | (Zur Einkaufliste hinzufügen) [ImageView|View] [CONTEXT SEPARATOR] 2 () [TextView|View] | . () [TextView|View] | 49 () [TextView|View] | aus Süddeutschland, Klasse I, Stück () [TextView|View] | (Zur Einkaufliste hinzufügen) [ImageView|View]' - text: 'Prospekt () [TextView|MainActivity] | 1 / 12 () [TextView|MainActivity] [SELECTED START] 2 / 12 () [TextView|MainActivity] [CONTEXT SEPARATOR] 2 / 12 () [TextView|MainActivity]' - text: 'Deine Prämien () [TextView|View] | So funktioniert’s () [TextView|View] | (Coupon Image) [ImageView|View] | Delikatess Brühe () [TextView|View] | 100% () [TextView|View] | (Coupon Image) [ImageView|View] | Bouillon mit Rind () [TextView|View] | 100% () [TextView|View] | (Coupon Image) [ImageView|View] | Gemüse Bouillon () [TextView|View] | 100% () [TextView|View] | (Coupon Image) [ImageView|View] | Bio Gemüse Bouillon () [TextView|View] | 100% () [TextView|View] | (Coupon Image) [ImageView|View] | Hühner Kraftbouillon () [TextView|View] | 100% () [TextView|View] [SELECTED START] Im ausgewählten Markt verfügbar EDEKA Schöck () [TextView|View] | Aktionszeitraum 29.12. - 11.01.2026 () [TextView|View] | Mit 8 Treuepunkten zum Prämien-Sonderpreis. Für jedes gekaufte Knorr Produkt erhältst du einen Treuepunkt. () [TextView|View] | Entdecke deine Prämien () [TextView|View] | Deine Prämien () [TextView|View] | So funktioniert’s () [TextView|View] | (Coupon Image) [ImageView|View] | Delikatess Brühe () [TextView|View] | 100% () [TextView|View] | (Coupon Image) [ImageView|View] | Bouillon mit Rind () [TextView|View] | 100% () [TextView|View] | (Coupon Image) [ImageView|View] | Gemüse Bouillon () [TextView|View] | 100% () [TextView|View] | (Coupon Image) [ImageView|View] | Bio Gemüse Bouillon () [TextView|View] | 100% () [TextView|View] | (Coupon Image) [ImageView|View] | 100% () [TextView|View] [CONTEXT SEPARATOR] Im ausgewählten Markt verfügbar EDEKA Schöck () [TextView|View] | Aktionszeitraum 29.12. - 11.01.2026 () [TextView|View] | Mit 8 Treuepunkten zum Prämien-Sonderpreis. Für jedes gekaufte Knorr Produkt erhältst du einen Treuepunkt. () [TextView|View] | Entdecke deine Prämien () [TextView|View] | Deine Prämien () [TextView|View] | So funktioniert’s () [TextView|View] | (Coupon Image) [ImageView|View] | Delikatess Brühe () [TextView|View] | 100% () [TextView|View] | (Coupon Image) [ImageView|View] | Bouillon mit Rind () [TextView|View] | 100% () [TextView|View] | (Coupon Image) [ImageView|View] | Gemüse Bouillon () [TextView|View] | 100% () [TextView|View] | (Coupon Image) [ImageView|View] | Bio Gemüse Bouillon () [TextView|View] | 100% () [TextView|View] | (Coupon Image) [ImageView|View] | 100% () [TextView|View]' - text: 'Coca-Cola Limonaden () [TextView|View] | 6x0,33l + 1 () [TextView|View] | (activate coupon button checkmark) [ImageView|View] | Coupon bereits aktiviert () [TextView|View] | - Einlösbar bis 18.10.2025 - Mehrfach einlösbar - Nicht mit anderen Rabattaktionen kombinierbar () [TextView|View] | (Info) [ImageView|View] | Erhältliche Sorten () [TextView|View] | Coca-Cola 0,33l DPG Fanta Exotic 0,33l DPG Coca-Cola Zero Sugar 0,33l DPG Fanta Orange 0,33l DPG Sprite Zitrone-Limette 0,33l DPG Mezzo Mix 0,33l DPG Coca-Cola Cherry 0,33l DPG Fanta Mango Dragonfruit 0,33l DPG Sprite Zitrone-Limette Zero 0,33l DPG Coca-Cola Light 0,33l DPG Fanta Lemon Elderflower 0,33l DPG Fanta Zero Sugar Orange 0,33l DPG Fanta Lemon 0,33l DPG Coca-Cola Lemon 0,33l DPG Coca-Cola Zero Koffeinfrei 0,33l DPG Mezzo Mix Zero 0,33l DPG Mezzo Mix Berry Love 0,33l DPG Coca-Cola Vanilla 0,33l DPG Fanta Cassis 0,33l DPG Coca-Cola Zero Sugar 0,33l DPG Fanta Zero Sugar Tutti Frutti 0,33l DPG Fanta Zero Forest Berries 0,33l DPG 5000112602029 5000112514124 5000112604450 5000112604726 5000112604740 5000112604696 5000112604467 5000112515848 5000112636420 5000112604627 5000112514100 5000112668766 5000112510379 5000112684667 5000112668872 5000112672077 5000112677430 5000112682205 5000112680546 5449000275165 5000112688801 5000112686869 () [TextView|View] [SELECTED START] Erhältliche Sorten () [TextView|View] | Coca-Cola 0,33l DPG Fanta Exotic 0,33l DPG Coca-Cola Zero Sugar 0,33l DPG Fanta Orange 0,33l DPG Sprite Zitrone-Limette 0,33l DPG Mezzo Mix 0,33l DPG Coca-Cola Cherry 0,33l DPG Fanta Mango Dragonfruit 0,33l DPG Sprite Zitrone-Limette Zero 0,33l DPG Coca-Cola Light 0,33l DPG Fanta Lemon Elderflower 0,33l DPG Fanta Zero Sugar Orange 0,33l DPG Fanta Lemon 0,33l DPG Coca-Cola Lemon 0,33l DPG Coca-Cola Zero Koffeinfrei 0,33l DPG Mezzo Mix Zero 0,33l DPG Mezzo Mix Berry Love 0,33l DPG Coca-Cola Vanilla 0,33l DPG Fanta Cassis 0,33l DPG Coca-Cola Zero Sugar 0,33l DPG Fanta Zero Sugar Tutti Frutti 0,33l DPG Fanta Zero Forest Berries 0,33l DPG 5000112602029 5000112514124 5000112604450 5000112604726 5000112604740 5000112604696 5000112604467 5000112515848 5000112636420 5000112604627 5000112514100 5000112668766 5000112510379 5000112684667 5000112668872 5000112672077 5000112677430 5000112682205 5000112680546 5449000275165 5000112688801 5000112686869 () [TextView|View] [CONTEXT SEPARATOR] Erhältliche Sorten () [TextView|View] | Coca-Cola 0,33l DPG Fanta Exotic 0,33l DPG Coca-Cola Zero Sugar 0,33l DPG Fanta Orange 0,33l DPG Sprite Zitrone-Limette 0,33l DPG Mezzo Mix 0,33l DPG Coca-Cola Cherry 0,33l DPG Fanta Mango Dragonfruit 0,33l DPG Sprite Zitrone-Limette Zero 0,33l DPG Coca-Cola Light 0,33l DPG Fanta Lemon Elderflower 0,33l DPG Fanta Zero Sugar Orange 0,33l DPG Fanta Lemon 0,33l DPG Coca-Cola Lemon 0,33l DPG Coca-Cola Zero Koffeinfrei 0,33l DPG Mezzo Mix Zero 0,33l DPG Mezzo Mix Berry Love 0,33l DPG Coca-Cola Vanilla 0,33l DPG Fanta Cassis 0,33l DPG Coca-Cola Zero Sugar 0,33l DPG Fanta Zero Sugar Tutti Frutti 0,33l DPG Fanta Zero Forest Berries 0,33l DPG 5000112602029 5000112514124 5000112604450 5000112604726 5000112604740 5000112604696 5000112604467 5000112515848 5000112636420 5000112604627 5000112514100 5000112668766 5000112510379 5000112684667 5000112668872 5000112672077 5000112677430 5000112682205 5000112680546 5449000275165 5000112688801 5000112686869 () [TextView|View]' metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: true datasets: - tmp-org/edeka-dataset-ctx-1 base_model: Alibaba-NLP/gte-multilingual-base --- # SetFit with Alibaba-NLP/gte-multilingual-base This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [tmp-org/edeka-dataset-ctx-1](https://huggingface.co/datasets/tmp-org/edeka-dataset-ctx-1) dataset that can be used for Text Classification. This SetFit model uses [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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](https://www.sbert.net) 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](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 8192 tokens - **Number of Classes:** 24 classes - **Training Dataset:** [tmp-org/edeka-dataset-ctx-1](https://huggingface.co/datasets/tmp-org/edeka-dataset-ctx-1) ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | 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| Start_Start | | | Kasse_Aktivierte Coupons | | | Other_Prospekt | | | Other_Unknown | | | Sparen_Coupons | | | Sparen_Angebote | | | Kasse_Kasse | | | Prämien_Prämien | | | Other_Treueaktionen | | | Other_Neuigkeiten | | | Other_Produktherkunft | | | Einkaufsliste_Einkaufsliste | | | Other_Loading | | | Other_Menu | | | Other_Kassenbons | | | Kasse_Unknown | | | Other_Coupon details | | | Kasse_Mobil bezahlen | | | Start_Loading | | | Sparen_Loading | | | Other_Marktsuche | | | Other_Other | | | Other_Code einlösen | | | Kasse_Loading | | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash 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("tmp-org/tmp_cv_model_2025_09_08_0") # Run inference preds = model("Prospekt () [TextView|MainActivity] | 1 / 12 () [TextView|MainActivity] [SELECTED START] 2 / 12 () [TextView|MainActivity] [CONTEXT SEPARATOR] 2 / 12 () [TextView|MainActivity]") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:---------|:----| | Word count | 9 | 270.1332 | 711 | | Label | Training Sample Count | |:----------------------------|:----------------------| | Einkaufsliste_Einkaufsliste | 31 | | Kasse_Aktivierte Coupons | 40 | | Kasse_Kasse | 17 | | Kasse_Loading | 2 | | Kasse_Mobil bezahlen | 5 | | Kasse_Unknown | 1 | | Other_Code einlösen | 1 | | Other_Coupon details | 32 | | Other_Kassenbons | 8 | | Other_Loading | 1 | | Other_Marktsuche | 2 | | Other_Menu | 40 | | Other_Neuigkeiten | 6 | | Other_Other | 1 | | Other_Produktherkunft | 11 | | Other_Prospekt | 33 | | Other_Treueaktionen | 36 | | Other_Unknown | 10 | | 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 | 0.1732 | - | | 0.0134 | 50 | 0.2443 | - | | 0.0268 | 100 | 0.1933 | - | | 0.0402 | 150 | 0.1816 | - | | 0.0535 | 200 | 0.1304 | - | | 0.0669 | 250 | 0.1268 | - | | 0.0803 | 300 | 0.122 | - | | 0.0937 | 350 | 0.1228 | - | | 0.1071 | 400 | 0.1263 | - | | 0.1205 | 450 | 0.0822 | - | | 0.1339 | 500 | 0.0648 | - | | 0.1473 | 550 | 0.1123 | - | | 0.1606 | 600 | 0.08 | - | | 0.1740 | 650 | 0.0927 | - | | 0.1874 | 700 | 0.0722 | - | | 0.2008 | 750 | 0.0662 | - | | 0.2142 | 800 | 0.098 | - | | 0.2276 | 850 | 0.0456 | - | | 0.2410 | 900 | 0.0407 | - | | 0.2544 | 950 | 0.0673 | - | | 0.2677 | 1000 | 0.0408 | - | | 0.2811 | 1050 | 0.0634 | - | | 0.2945 | 1100 | 0.0608 | - | | 0.3079 | 1150 | 0.0745 | - | | 0.3213 | 1200 | 0.0379 | - | | 0.3347 | 1250 | 0.0267 | - | | 0.3481 | 1300 | 0.0393 | - | | 0.3614 | 1350 | 0.0342 | - | | 0.3748 | 1400 | 0.0539 | - | | 0.3882 | 1450 | 0.0343 | - | | 0.4016 | 1500 | 0.0331 | - | | 0.4150 | 1550 | 0.0182 | - | | 0.4284 | 1600 | 0.0486 | - | | 0.4418 | 1650 | 0.0395 | - | | 0.4552 | 1700 | 0.0462 | - | | 0.4685 | 1750 | 0.0264 | - | | 0.4819 | 1800 | 0.0341 | - | | 0.4953 | 1850 | 0.0296 | - | | 0.5087 | 1900 | 0.0262 | - | | 0.5221 | 1950 | 0.0527 | - | | 0.5355 | 2000 | 0.0446 | - | | 0.5489 | 2050 | 0.0311 | - | | 0.5622 | 2100 | 0.025 | - | | 0.5756 | 2150 | 0.0251 | - | | 0.5890 | 2200 | 0.0224 | - | | 0.6024 | 2250 | 0.0469 | - | | 0.6158 | 2300 | 0.0336 | - | | 0.6292 | 2350 | 0.0258 | - | | 0.6426 | 2400 | 0.0326 | - | | 0.6560 | 2450 | 0.027 | - | | 0.6693 | 2500 | 0.036 | - | | 0.6827 | 2550 | 0.0286 | - | | 0.6961 | 2600 | 0.0273 | - | | 0.7095 | 2650 | 0.0288 | - | | 0.7229 | 2700 | 0.0267 | - | | 0.7363 | 2750 | 0.0412 | - | | 0.7497 | 2800 | 0.0202 | - | | 0.7631 | 2850 | 0.0244 | - | | 0.7764 | 2900 | 0.0359 | - | | 0.7898 | 2950 | 0.0377 | - | | 0.8032 | 3000 | 0.0302 | - | | 0.8166 | 3050 | 0.0192 | - | | 0.8300 | 3100 | 0.0296 | - | | 0.8434 | 3150 | 0.0292 | - | | 0.8568 | 3200 | 0.0317 | - | | 0.8701 | 3250 | 0.0246 | - | | 0.8835 | 3300 | 0.0096 | - | | 0.8969 | 3350 | 0.0306 | - | | 0.9103 | 3400 | 0.0198 | - | | 0.9237 | 3450 | 0.0188 | - | | 0.9371 | 3500 | 0.0253 | - | | 0.9505 | 3550 | 0.0292 | - | | 0.9639 | 3600 | 0.04 | - | | 0.9772 | 3650 | 0.0298 | - | | 0.9906 | 3700 | 0.0184 | - | ### 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 ```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} } ```