--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: "2 () [TextView|View] | Vegane Alternative () [TextView|View] | versch. Sorten,\ \ je 180 g Packung (1 kg = € 16.61) () [TextView|View] | (Zur Einkaufliste hinzufügen)\ \ [ImageView|View] | 2 () [TextView|View] | . () [TextView|View] | 29 () [TextView|View]\ \ | Burgis Knödel () [TextView|View] | versch. Sorten, je 360 g - 500 g Packung\ \ (1 kg = ab € 4.58) () [TextView|View] | (Zur Einkaufliste hinzufügen) [ImageView|View]\ \ | 50 () [TextView|View] | Extra°Punkte () [TextView|View] | 2 () [TextView|View]\ \ | . () [TextView|View] | 79 () [TextView|View] | Rana Ravioli, Tortelloni oder\ \ Cappelletti () [TextView|View] | versch. Sorten, je 250 g Beutel (1 kg = € 11.16)\ \ () [TextView|View] | (Zur Einkaufliste hinzufügen) [ImageView|View]\n\n[SELECTED\ \ START]\n\n (Zur Einkaufliste hinzufügen) [ImageView|View] | Tiefkühl () [TextView|View]\ \ | 1 () [TextView|View] | . () [TextView|View] | 99 () [TextView|View] | agrarfrost\ \ Kartoffelpuffer () [TextView|View] | tiefgefroren, 600 g Packung (1 kg = € 3.32)\ \ () [TextView|View] | (Zur Einkaufliste hinzufügen) [ImageView|View] | 2 ()\ \ [TextView|View] | . () [TextView|View] | 00 () [TextView|View] | GUT & GÜNSTIG\ \ - Gemüse () [TextView|View] | tiefgefroren, versch. Sorten, je 1 kg Beutel ()\ \ [TextView|View] | (Zur Einkaufliste hinzufügen) [ImageView|View]\n[CONTEXT\ \ SEPARATOR]\n (Zur Einkaufliste hinzufügen) [ImageView|View] | Tiefkühl () [TextView|View]\ \ | 1 () [TextView|View] | . () [TextView|View] | 99 () [TextView|View] | agrarfrost\ \ Kartoffelpuffer () [TextView|View] | tiefgefroren, 600 g Packung (1 kg = € 3.32)\ \ () [TextView|View] | (Zur Einkaufliste hinzufügen) [ImageView|View] | 2 ()\ \ [TextView|View] | . () [TextView|View] | 00 () [TextView|View] | GUT & GÜNSTIG\ \ - Gemüse () [TextView|View] | tiefgefroren, versch. Sorten, je 1 kg Beutel ()\ \ [TextView|View] | (Zur Einkaufliste hinzufügen) [ImageView|View]" - text: 'Coupon bereits aktiviert () [TextView|View] | APP Preis () [TextView|View] | (Coupon Image) [View|View] | Gültig bis 20.09.2025 () [TextView|View] | Yfood Trinkmahlzeit () [TextView|View] | 500ml () [TextView|View] | Coupon bereits aktiviert () [TextView|View] | APP Preis () [TextView|View] | (Coupon Image) [View|View] | Gültig bis 20.09.2025 () [TextView|View] | Pick up Multi 5er inkl. Pick-Up Minis 12er () [TextView|View] | 140g / 127g () [TextView|View] | Coupon bereits aktiviert () [TextView|View] | APP Preis () [TextView|View] | (Coupon Image) [View|View] | Gültig bis 20.09.2025 () [TextView|View] | Persil Pulver, Caps & Bars () [TextView|View] | 18 WL Tabs / 20 WL Flüssig () [TextView|View] | Coupon bereits aktiviert () [TextView|View] [SELECTED START] Coupon bereits aktiviert () [TextView|View] | APP Preis () [TextView|View] | (Coupon Image) [View|View] | Gültig bis 20.09.2025 () [TextView|View] | Yfood Trinkmahlzeit () [TextView|View] | 500ml () [TextView|View] | Coupon bereits aktiviert () [TextView|View] | APP Preis () [TextView|View] | (Coupon Image) [View|View] | Gültig bis 20.09.2025 () [TextView|View] | Pick up Multi 5er inkl. Pick-Up Minis 12er () [TextView|View] [CONTEXT SEPARATOR] Coupon bereits aktiviert () [TextView|View] | APP Preis () [TextView|View] | (Coupon Image) [View|View] | Gültig bis 20.09.2025 () [TextView|View] | Yfood Trinkmahlzeit () [TextView|View] | 500ml () [TextView|View] | Coupon bereits aktiviert () [TextView|View] | APP Preis () [TextView|View] | (Coupon Image) [View|View] | Gültig bis 20.09.2025 () [TextView|View] | Pick up Multi 5er inkl. Pick-Up Minis 12er () [TextView|View]' - text: 'Dein Aktionscode () [TextView|View] | Was steckt hinter deinem Code? Jetzt eingeben und Vorteil entdecken () [TextView|View] | Code eingeben () [EditText|View] | Überprüfen () [TextView|View] | Die Teilnahmebedingungen unserer Aktionen findest du u.a. auf den Werbematerialien zur Aktion. Weitere Informationen entnimmst du bitte unseren Nutzungsbedingungen. () [TextView|View] | So funktionieren Aktionscodes () [TextView|View] | Du registrierst dich für ein Kundenkonto bei EDEKA. So kannst du unsere Coupons und Angebote nutzen. () [TextView|View] | Gib einen gültigen Aktionscode in das Eingabefeld ein. () [TextView|View] | Wähle einen Markt in dem du deinen Aktionscode einlösen willst. () [TextView|View] | Warum einen Markt auswählen? () [TextView|View] | Weil nicht alle Märkte an jeder Aktion teilnehmen. () [TextView|View] [SELECTED START] 24.08. - 18.10.2025 () [TextView|View] | Tierischer Sammelspaß mit schleich® und WWF! () [TextView|View] | Noch 5 Treuepunkte zum Prämien-Sonderpreis. () [TextView|View] | Pro 5 Euro Einkaufswert gibt es einen Treuepunkt. () [TextView|View] | 07.09. - 05.10.2025 () [TextView|View] | Deine Treue wird belohnt - einfach punkten und sparen! () [TextView|View] | Noch 15 Treuepunkte zum Prämien-Sonderpreis. () [TextView|View] | Für je 20€ Einkaufwert erhälst du einen Treuepunkt () [TextView|View] | (EDEKA Logo) [ImageView|View] | EDEKA Schöck () [TextView|View] | (Pfeil nach unten) [View|View] | MT () [TextView|View] | (Start) [View|View] | Start () [TextView|View] | (Sparen) [View|View] | Sparen () [TextView|View] | Kasse () [TextView|View] | (Prämien) [View|View] | Prämien () [TextView|View] | (Einkaufsliste) [View|View] | Einkaufsliste () [TextView|View] | (Kasse) [View|View] [CONTEXT SEPARATOR] 24.08. - 18.10.2025 () [TextView|View] | Tierischer Sammelspaß mit schleich® und WWF! () [TextView|View] | Noch 5 Treuepunkte zum Prämien-Sonderpreis. () [TextView|View] | Pro 5 Euro Einkaufswert gibt es einen Treuepunkt. () [TextView|View] | 07.09. - 05.10.2025 () [TextView|View] | Deine Treue wird belohnt - einfach punkten und sparen! () [TextView|View] | Noch 15 Treuepunkte zum Prämien-Sonderpreis. () [TextView|View] | Für je 20€ Einkaufwert erhälst du einen Treuepunkt () [TextView|View] | (EDEKA Logo) [ImageView|View] | EDEKA Schöck () [TextView|View] | (Pfeil nach unten) [View|View] | MT () [TextView|View] | (Start) [View|View] | Start () [TextView|View] | (Sparen) [View|View] | Sparen () [TextView|View] | Kasse () [TextView|View] | (Prämien) [View|View] | Prämien () [TextView|View] | (Einkaufsliste) [View|View] | Einkaufsliste () [TextView|View] | (Kasse) [View|View]' - text: "APP Preis () [TextView|View] | (Coupon Image) [View|View] | Gültig bis 27.12.2025\ \ () [TextView|View] | M&Ms Kleinbeutel () [TextView|View] | 128g - 150g () [TextView|View]\ \ | Coupon bereits aktiviert () [TextView|View] | APP Preis () [TextView|View]\ \ | (Coupon Image) [View|View] | Gültig bis 27.12.2025 () [TextView|View] | Melitta\ \ Bella Crema () [TextView|View] | 1000g - 1100g () [TextView|View] | Coupon bereits\ \ aktiviert () [TextView|View] | APP Preis () [TextView|View] | (Coupon Image)\ \ [View|View] | Gültig bis 27.12.2025 () [TextView|View] | Milka Tafelschokolade\ \ () [TextView|View] | 250g - 300g () [TextView|View] | Coupon bereits aktiviert\ \ () [TextView|View] | APP Preis () [TextView|View] | (Coupon Image) [View|View]\ \ | Gültig bis 27.12.2025 () [TextView|View] | Dallmayr Prodomo () [TextView|View]\ \ | 500g () [TextView|View]\n\n[SELECTED START]\n\n (Coupon Image) [View|View]\ \ | Coupon bereits aktiviert () [TextView|View] | APP Preis () [TextView|View]\ \ | (Coupon Image) [View|View] | Gültig bis 27.12.2025 () [TextView|View] | Melitta\ \ Bella Crema () [TextView|View] | 1000g - 1100g () [TextView|View] | Coupon bereits\ \ aktiviert () [TextView|View] | APP Preis () [TextView|View] | (Coupon Image)\ \ [View|View] | Gültig bis 27.12.2025 () [TextView|View] | Milka Tafelschokolade\ \ () [TextView|View] | 250g - 300g () [TextView|View] | Coupon bereits aktiviert\ \ () [TextView|View] | APP Preis () [TextView|View] | (Coupon Image) [View|View]\ \ | Gültig bis 27.12.2025 () [TextView|View] | Dallmayr Prodomo () [TextView|View]\ \ | 500g () [TextView|View] | Coupon bereits aktiviert () [TextView|View]\n[CONTEXT\ \ SEPARATOR]\n (Coupon Image) [View|View] | Coupon bereits aktiviert () [TextView|View]\ \ | APP Preis () [TextView|View] | (Coupon Image) [View|View] | Gültig bis 27.12.2025\ \ () [TextView|View] | Melitta Bella Crema () [TextView|View] | 1000g - 1100g\ \ () [TextView|View] | Coupon bereits aktiviert () [TextView|View] | APP Preis\ \ () [TextView|View] | (Coupon Image) [View|View] | Gültig bis 27.12.2025 ()\ \ [TextView|View] | Milka Tafelschokolade () [TextView|View] | 250g - 300g ()\ \ [TextView|View] | Coupon bereits aktiviert () [TextView|View] | APP Preis ()\ \ [TextView|View] | (Coupon Image) [View|View] | Gültig bis 27.12.2025 () [TextView|View]\ \ | Dallmayr Prodomo () [TextView|View] | 500g () [TextView|View] | Coupon bereits\ \ aktiviert () [TextView|View]" - text: '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]' 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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| Other_Prospekt | | | Start_Start | | | Other_Code einlösen | | | Prämien_Prämien | | | Other_Loading | | | Other_Treueaktionen | | | Other_Neuigkeiten | | | Other_Produktherkunft | | | Other_Marktsuche | | | Other_Menu | | | Kasse_Mobil bezahlen | | | Other_Kassenbons | | | Sparen_Angebote | | | Sparen_Coupons | | | Other_Coupon details | | | Kasse_Kasse | | | Kasse_Aktivierte Coupons | | | Einkaufsliste_Einkaufsliste | | | Other_Unknown | | | Kasse_Unknown | | | Start_Loading | | | Sparen_Loading | | | Other_Other | | | 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_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 ```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} } ```