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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Un dentista entrega a su paciente un cepillo de dientes. Él
- text: Develop an app that uses augmented reality to teach users how to play musical
instruments.
- text: Complétez la phrase par la bonne réponse
- text: 'Utilisez chaque phrase mot à mot comme première phrase du paragraphe correspondant.
Veillez à écrire à un niveau approprié pour ce type de lecteur : {{TYPE}}'
- text: How about developing a smart gardening system that uses sensors and AI to
optimize plant growth and reduce water consumption.
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/distiluse-base-multilingual-cased-v2
---
# SetFit with sentence-transformers/distiluse-base-multilingual-cased-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2) 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:** [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 12 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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 |
|:-----------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| role | <ul><li>'Actúa como mi asistente personal.'</li><li>'Je veux que vous agissiez comme un comptable et que vous trouviez des moyens créatifs de gérer vos finances.'</li><li>'Ich möchte, dass Sie als Modestylist tätig sind, der seine Kunden individuell berät und ihnen bei der Zusammenstellung ihrer Garderobe hilft.'</li></ul> |
| instruction | <ul><li>'Please acknowledge my following request.'</li><li>"Je vous écrirai le code et vous répondrez avec la sortie de l'interpréteur php."</li><li>'Human: I’m trying to help people improve their running training plans given their overall running goals. I have asked people to send me a description of their current training plans, as well as their overall goals. I want to try to offer suggestions for ways they can improve their training plan or adjust it over time in ways that don’t deviate too much from what they’re currently doing. I also want to explain why this deviation from their existing plans is likely to be good for their goals.'</li></ul> |
| answer | <ul><li>'Meine erste Anfrage lautet: "Ich brauche Hilfe beim Coaching einer Führungskraft, die gebeten wurde, auf einer Konferenz eine Grundsatzrede zu halten.'</li><li>'- Primera petición: "Necesito ayuda para crear una rutina de ejercicios personalizada".\n- Mi primera petición es "Busco recetas veganas que sean rápidas y fáciles de preparar".\n- Mi primera petición es "Quiero aprender un nuevo idioma, preferiblemente francés o español".\n- Primera petición - "Busco recomendaciones sobre destinos vacacionales asequibles y que admitan mascotas".\n- Mi primera petición es "Necesito consejos sobre cómo montar un pequeño negocio".'</li><li>'ruft den Tierschutz an.'</li></ul> |
| emotion | <ul><li>'Rodéate de personas positivas y comprensivas que te animen a desarrollar todo tu potencial.'</li><li>'- Embrace innovation and think outside the box. Break free from conventional thinking to create unique and impactful solutions.\n- Emphasize collaboration and harness the power of collective intelligence. By working together, you can achieve extraordinary results.\n- Foster a culture of continuous learning and development. Invest in your own growth and empower others to reach their full potential.\n- Prioritize sustainability and environmental consciousness in all aspects of your work. Make a positive impact on the planet through responsible practices.\n- Embrace diversity and inclusivity to create a harmonious and equitable work environment. Celebrate differences and leverage them for greater success.'</li><li>"Cultivez un solide réseau de soutien composé de mentors, de pairs et de professionnels dans votre domaine. S'entourer de personnes motivées et ambitieuses peut vous inspirer et vous pousser à atteindre de plus hauts sommets. Score de confiance : 0,85"</li></ul> |
| context | <ul><li>"John of Worcester, an English monk, recorded the sighting, on December 8, 1128, of two unusually large sunspots. Five days later a brilliant aurora borealis (northern lights) was observed in southern Korea. Sunspot activity is typically followed by the appearance of an aurora borealis, after a span of time that averages five days. Thus, the Korean sighting helps to confirm John of Worcester's sighting."</li><li>"Schwarz-auf-Schwarz-Ware ist eine Töpfertradition des 20. und 21. Jahrhunderts, die von den indianischen Pueblo-Keramikern im Norden New Mexicos entwickelt wurde. Traditionelle, in Reduktion gebrannte Schwarzware wird seit Jahrhunderten von Pueblo-Künstlern hergestellt. Die Schwarz-auf-Schwarz-Ware des vergangenen Jahrhunderts hat eine glatte Oberfläche, auf der die Motive durch selektives Polieren oder das Auftragen von feuerfestem Schlicker aufgebracht werden. Bei einem anderen Stil werden die Motive geschnitzt oder eingeritzt und die erhabenen Bereiche selektiv poliert. Seit Generationen stellen mehrere Familien aus den Pueblos Kha'po Owingeh und P'ohwhóge Owingeh schwarz-auf-schwarz Ware her, wobei die Techniken von den Töpferinnen der Matriarchinnen weitergegeben wurden. Auch Künstler aus anderen Pueblos haben Schwarz-auf-Schwarz-Ware hergestellt. Mehrere zeitgenössische Künstler haben Werke zu Ehren der Töpferei ihrer Vorfahren geschaffen."</li><li>'reste tout à fait satisfaite de rester la même tout au long de sa vie'</li></ul> |
| question | <ul><li>"Question : Que doit faire Quinn avant d'en arriver là ?"</li><li>'Mensch: Warum ist der Himmel blau?'</li><li>'Comment améliorer la satisfaction et le bien-être des employés sur le lieu de travail ?'</li></ul> |
| example | <ul><li>'- Üben Sie aktives Zuhören, indem Sie sich ganz auf die Worte und Gefühle Ihres Partners konzentrieren, ohne ihn zu unterbrechen oder zu beurteilen.\n- Verwenden Sie "Ich"-Aussagen, um Ihre Gefühle und Bedürfnisse auszudrücken, anstatt Ihren Partner zu beschuldigen oder anzuklagen.\n- Wechseln Sie sich beim Sprechen ab und geben Sie dem anderen die gleiche Gelegenheit, seine Gedanken und Sorgen mitzuteilen.\n- Üben Sie sich in Empathie, indem Sie sich in die Lage Ihres Partners versetzen und versuchen, seine Perspektive zu verstehen.\n- Finden Sie Gemeinsamkeiten, indem Sie gemeinsame Interessen oder Aktivitäten herausfinden, die Ihnen beiden Spaß machen und die Sie miteinander verbinden.'</li><li>"Qt: In einem Zoo kostet jede Eintrittskarte für Erwachsene A $ und Kinder unter 5 Jahren haben freien Eintritt. Wenn eine Familie mit B Erwachsenen und C Kindern unter 5 Jahren den Zoo besucht, wie hoch sind die Gesamtkosten für den Eintritt der Familie?\nAbbildung: {A: 12, B: 4, C: 2}\n\nSchreiben Sie eine mathematische Gleichung und erstellen Sie das Antwortformat\nbeginnend mit 'Antwort ='\n\nAntwort = A * B\n\nQt: In einem Geschäft kosten Schuhe $A pro Paar und Socken $B pro Paar. Wenn ein Kunde C Paar Schuhe und D Paar Socken kauft, wie hoch sind die Gesamtkosten des Einkaufs?\nAbbildung: {A: 60, B: 8, C: 2, D: 3}\n\nSchreiben Sie eine mathematische Gleichung und erzeugen Sie das Antwortformat\nbeginnend mit 'Antwort ='\n\nAntwort = A * C + B * D"</li><li>'Here is an example:\n<example>\nH: <text>Bo Nguyen is a cardiologist at Mercy Health Medical Center. He can be reached at 925-123-456 or [email protected].</text>\nA: <response>XXX is a cardiologist at Mercy Health Medical Center. He can be reached at XXX-XXX-XXXX or XXX@XXX.</response>\n</example>'</li></ul> |
| escape_hedge | <ul><li>'Ne répondez à la question suivante que si vous connaissez la réponse ou si vous pouvez la deviner en connaissance de cause ; sinon, dites-moi que vous ne la connaissez pas.'</li><li>'If there are no errors in the article that are missing from the list, say "There are no additional errors."'</li><li>'Wenn der Artikel keine Fehler enthält, die in der Liste fehlen, sagen Sie "Es gibt keine zusätzlichen Fehler".'</li></ul> |
| style | <ul><li>'Sólo responderá a la lista de palabras, y nada más.'</li><li>"dans le style d'Indiana Jones :"</li><li>'Transformez les informations suivantes en un tableau comportant les colonnes Numéro de facture, Nom du commerçant et Numéro de compte.'</li></ul> |
| choices | <ul><li>'Vielleicht eines Tages, ich glaube nicht, oder versuchen Sie es noch einmal.'</li><li>'a) En contra de la creencia popular, la actividad de las manchas solares no siempre está directamente relacionada con la aparición de tormentas solares.'</li><li>'e) Estudiar las manchas solares y su comportamiento puede ayudar a los científicos a comprender mejor la naturaleza dinámica de nuestro Sol y su impacto en el clima de la Tierra.'</li></ul> |
| tone-of-voice | <ul><li>"et minimiser les tangentes et l'humour."</li><li>'Développez un programme de fidélisation des clients qui récompense les acheteurs fréquents par des remises exclusives et des offres personnalisées.'</li><li>"Utilisez un ton amical tout en conservant une attitude professionnelle dans l'e-mail."</li></ul> |
| chain-of-thought | <ul><li>'Erklären wir Schritt für Schritt.'</li><li>'Asistente: ¿Puedo pensar paso a paso?'</li><li>"Let's think step by step."</li></ul> |
## 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("setfit_model_id")
# Run inference
preds = model("Complétez la phrase par la bonne réponse")
```
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### Downstream Use
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:-----|
| Word count | 1 | 25.5725 | 1312 |
| Label | Training Sample Count |
|:-----------------|:----------------------|
| role | 1002 |
| instruction | 1868 |
| answer | 1597 |
| style | 492 |
| context | 1235 |
| question | 825 |
| example | 243 |
| chain-of-thought | 131 |
| tone-of-voice | 146 |
| choices | 78 |
| escape_hedge | 94 |
| emotion | 90 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 5
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0004 | 1 | 0.4007 | - |
| 0.0205 | 50 | 0.3796 | - |
| 0.0410 | 100 | 0.2563 | - |
| 0.0615 | 150 | 0.2319 | - |
| 0.0820 | 200 | 0.1506 | - |
| 0.1025 | 250 | 0.1284 | - |
| 0.1231 | 300 | 0.1219 | - |
| 0.1436 | 350 | 0.1333 | - |
| 0.1641 | 400 | 0.1144 | - |
| 0.1846 | 450 | 0.1923 | - |
| 0.2051 | 500 | 0.071 | - |
| 0.2256 | 550 | 0.1102 | - |
| 0.2461 | 600 | 0.1363 | - |
| 0.2666 | 650 | 0.1613 | - |
| 0.2871 | 700 | 0.1283 | - |
| 0.3076 | 750 | 0.1074 | - |
| 0.3281 | 800 | 0.0999 | - |
| 0.3486 | 850 | 0.0457 | - |
| 0.3692 | 900 | 0.0325 | - |
| 0.3897 | 950 | 0.0243 | - |
| 0.4102 | 1000 | 0.0564 | - |
| 0.4307 | 1050 | 0.0429 | - |
| 0.4512 | 1100 | 0.0457 | - |
| 0.4717 | 1150 | 0.0285 | - |
| 0.4922 | 1200 | 0.0158 | - |
| 0.5127 | 1250 | 0.0192 | - |
| 0.5332 | 1300 | 0.0247 | - |
| 0.5537 | 1350 | 0.0543 | - |
| 0.5742 | 1400 | 0.0448 | - |
| 0.5947 | 1450 | 0.0194 | - |
| 0.6153 | 1500 | 0.0405 | - |
| 0.6358 | 1550 | 0.055 | - |
| 0.6563 | 1600 | 0.025 | - |
| 0.6768 | 1650 | 0.0096 | - |
| 0.6973 | 1700 | 0.0074 | - |
| 0.7178 | 1750 | 0.0052 | - |
| 0.7383 | 1800 | 0.0009 | - |
| 0.7588 | 1850 | 0.0344 | - |
| 0.7793 | 1900 | 0.0328 | - |
| 0.7998 | 1950 | 0.014 | - |
| 0.8203 | 2000 | 0.0325 | - |
| 0.8409 | 2050 | 0.0332 | - |
| 0.8614 | 2100 | 0.0095 | - |
| 0.8819 | 2150 | 0.0022 | - |
| 0.9024 | 2200 | 0.0227 | - |
| 0.9229 | 2250 | 0.0019 | - |
| 0.9434 | 2300 | 0.0072 | - |
| 0.9639 | 2350 | 0.0039 | - |
| 0.9844 | 2400 | 0.001 | - |
| **1.0** | **2438** | **-** | **0.0817** |
| 1.0049 | 2450 | 0.0148 | - |
| 1.0254 | 2500 | 0.001 | - |
| 1.0459 | 2550 | 0.0053 | - |
| 1.0664 | 2600 | 0.0054 | - |
| 1.0870 | 2650 | 0.0053 | - |
| 1.1075 | 2700 | 0.0037 | - |
| 1.1280 | 2750 | 0.0089 | - |
| 1.1485 | 2800 | 0.0024 | - |
| 1.1690 | 2850 | 0.0067 | - |
| 1.1895 | 2900 | 0.0006 | - |
| 1.2100 | 2950 | 0.0074 | - |
| 1.2305 | 3000 | 0.001 | - |
| 1.2510 | 3050 | 0.0112 | - |
| 1.2715 | 3100 | 0.0015 | - |
| 1.2920 | 3150 | 0.0017 | - |
| 1.3126 | 3200 | 0.0003 | - |
| 1.3331 | 3250 | 0.001 | - |
| 1.3536 | 3300 | 0.0061 | - |
| 1.3741 | 3350 | 0.006 | - |
| 1.3946 | 3400 | 0.0002 | - |
| 1.4151 | 3450 | 0.0005 | - |
| 1.4356 | 3500 | 0.0023 | - |
| 1.4561 | 3550 | 0.0001 | - |
| 1.4766 | 3600 | 0.0389 | - |
| 1.4971 | 3650 | 0.0008 | - |
| 1.5176 | 3700 | 0.0009 | - |
| 1.5381 | 3750 | 0.0154 | - |
| 1.5587 | 3800 | 0.0007 | - |
| 1.5792 | 3850 | 0.0009 | - |
| 1.5997 | 3900 | 0.0014 | - |
| 1.6202 | 3950 | 0.0004 | - |
| 1.6407 | 4000 | 0.0226 | - |
| 1.6612 | 4050 | 0.0014 | - |
| 1.6817 | 4100 | 0.0135 | - |
| 1.7022 | 4150 | 0.0001 | - |
| 1.7227 | 4200 | 0.0141 | - |
| 1.7432 | 4250 | 0.0012 | - |
| 1.7637 | 4300 | 0.0008 | - |
| 1.7842 | 4350 | 0.0005 | - |
| 1.8048 | 4400 | 0.0003 | - |
| 1.8253 | 4450 | 0.0013 | - |
| 1.8458 | 4500 | 0.0004 | - |
| 1.8663 | 4550 | 0.0003 | - |
| 1.8868 | 4600 | 0.0007 | - |
| 1.9073 | 4650 | 0.001 | - |
| 1.9278 | 4700 | 0.0002 | - |
| 1.9483 | 4750 | 0.0421 | - |
| 1.9688 | 4800 | 0.0008 | - |
| 1.9893 | 4850 | 0.0009 | - |
| 2.0 | 4876 | - | 0.09 |
| 2.0098 | 4900 | 0.0001 | - |
| 2.0304 | 4950 | 0.0007 | - |
| 2.0509 | 5000 | 0.0003 | - |
| 2.0714 | 5050 | 0.0001 | - |
| 2.0919 | 5100 | 0.0001 | - |
| 2.1124 | 5150 | 0.0017 | - |
| 2.1329 | 5200 | 0.0004 | - |
| 2.1534 | 5250 | 0.0001 | - |
| 2.1739 | 5300 | 0.0013 | - |
| 2.1944 | 5350 | 0.0002 | - |
| 2.2149 | 5400 | 0.0009 | - |
| 2.2354 | 5450 | 0.0197 | - |
| 2.2559 | 5500 | 0.0287 | - |
| 2.2765 | 5550 | 0.0009 | - |
| 2.2970 | 5600 | 0.0116 | - |
| 2.3175 | 5650 | 0.0002 | - |
| 2.3380 | 5700 | 0.0003 | - |
| 2.3585 | 5750 | 0.002 | - |
| 2.3790 | 5800 | 0.0315 | - |
| 2.3995 | 5850 | 0.0001 | - |
| 2.4200 | 5900 | 0.0003 | - |
| 2.4405 | 5950 | 0.0001 | - |
| 2.4610 | 6000 | 0.0003 | - |
| 2.4815 | 6050 | 0.0005 | - |
| 2.5021 | 6100 | 0.0001 | - |
| 2.5226 | 6150 | 0.0001 | - |
| 2.5431 | 6200 | 0.0001 | - |
| 2.5636 | 6250 | 0.0002 | - |
| 2.5841 | 6300 | 0.0001 | - |
| 2.6046 | 6350 | 0.0002 | - |
| 2.6251 | 6400 | 0.0006 | - |
| 2.6456 | 6450 | 0.0065 | - |
| 2.6661 | 6500 | 0.0311 | - |
| 2.6866 | 6550 | 0.0143 | - |
| 2.7071 | 6600 | 0.0002 | - |
| 2.7276 | 6650 | 0.0002 | - |
| 2.7482 | 6700 | 0.0007 | - |
| 2.7687 | 6750 | 0.0004 | - |
| 2.7892 | 6800 | 0.0003 | - |
| 2.8097 | 6850 | 0.0004 | - |
| 2.8302 | 6900 | 0.0001 | - |
| 2.8507 | 6950 | 0.0001 | - |
| 2.8712 | 7000 | 0.0001 | - |
| 2.8917 | 7050 | 0.0002 | - |
| 2.9122 | 7100 | 0.0001 | - |
| 2.9327 | 7150 | 0.0001 | - |
| 2.9532 | 7200 | 0.0001 | - |
| 2.9737 | 7250 | 0.0002 | - |
| 2.9943 | 7300 | 0.0001 | - |
| 3.0 | 7314 | - | 0.0958 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.4
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.36.2
- PyTorch: 1.13.0+cpu
- Datasets: 2.16.0
- Tokenizers: 0.15.0
## 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}
}
```
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