Add SetFit model
Browse files- README.md +104 -72
- config_setfit.json +2 -2
- model.safetensors +1 -1
- model_head.pkl +1 -1
README.md
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@@ -9,62 +9,89 @@ base_model: sentence-transformers/paraphrase-MiniLM-L6-v2
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metrics:
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- accuracy
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widget:
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pipeline_tag: text-classification
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inference: false
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---
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@@ -114,7 +141,7 @@ from setfit import SetFitModel
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("Chernoffface/fs-setfit-multilable-model")
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# Run inference
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preds = model("
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```
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<!--
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### Training Set Metrics
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| Training set | Min | Median | Max |
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|:-------------|:----|:---------|:----|
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| Word count |
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### Training Hyperparameters
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- batch_size: (16, 16)
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:------:|:----:|:-------------:|:---------------:|
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### Framework Versions
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- Python: 3.12.3
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metrics:
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- accuracy
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widget:
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- text: Mitarbeit am wissenschaftlichen Arbeitsplatz (LV0125)
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- text: '20-00-0366-iv Serious Games: Einführung in die Thematik „Serious Games“:
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wissenschaftlich-technische Grundlagen, Anwendungsgebiete und Trends. Die Einzelthemen
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umfassen unter anderem: • Einführung in Serious Games • Game Development, Game
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Design • Game Technology, Tools und Engines • Personalisierung und Adaption •
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Interactive Digital Storytelling • Authoring und Content Generation • Multiplayer
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Games • Game Interfaces und Sensor Technology • Effects, Affects und User Experience
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• Mobile Games • Serious Games Anwendungsbereiche und Best-Practice Beispiele
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Die Übungen enthalten Theorie- und Praxisanteile. Dabei wird die Verwendung einer
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Game Engine gelehrt.'
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- text: 'Aerobotics Seminar: - Einführung in die Aufgabenstellung, die vorhandene
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Infrastruktur und den zu durchlaufenden Entwicklungsprozess
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- Entwurf und Implementierung von Algorithmen zur Flugregelung in Gruppenarbeit
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- Diskussion des Fortschritts in regelmäßigen Progress-Meetings
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- Flugdemonstration
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- Abschließende Präsentation und Dokumentation'
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- text: "Digital Transformations, Consumer Well-Being, and Sustainability: Physical\
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\ consumption, and individual ownership of material products in particular, has\
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\ traditionally been the default mode of consumption and its extent has long been\
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\ considered a measure of personal and societal prosperity. However, our daily\
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\ lives increasingly shift towards or are altered by digital environments, which\
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\ nurture alternative forms of consumption such as sharing or access-based offers,\
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\ cultivate the prevalence of virtual living worlds through fictional experiences,\
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\ and alter our relations to material possessions by an increasing availability\
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\ of digital solutions.\n\n\n\nThe course exposes you to state-of-the-art research\
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\ on consumer research and digital transformations in fields such as virtual reality,\
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\ sharing economy, and blockchain technologies. You will be guided through background\
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\ information of consumer behavior and consumer psychology. By creating a collaborative\
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\ learning environment, we will explore and critically discuss how digital transformations\
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\ affect consumer well-being and sustainability. \n\n\n\nYour role is to be an\
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\ active contributor in the class. This course consists of a lectures, discussion\
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\ sessions and group presentations. Generally, analysis of readings will be used\
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\ to guide our discussion. \n\n\n\nThe main objective of the course is to critically\
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\ reflect upon current technological advancements that increasingly permeate everyday\
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\ lives. Students will be engaged in exploring technological-social issues in\
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\ marketing and be guided into a critical approach on technology-brands-consumers\
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\ relationships behind digital transformations."
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- text: 'Grundlagen der IT-Sicherheit: Um einen Überblick der IT-Sicherheit zu vermitteln
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werden folgende Themen behandelt:
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Motivation für IT-Sicherheit
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Grundbegriffe der IT-Sicherheit
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Computer Malware
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Kryptographische Grundlagen
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Authentisierung
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Biometrie
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Zugriffskontrolle
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Netzwerkund
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Internetsicherheit
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Physikalische Sicherheit / Physikalische Angriffe
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Sicherheitsevaluierung und Zertifizierung
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Einführung in den Datenschutz'
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pipeline_tag: text-classification
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inference: false
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---
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("Chernoffface/fs-setfit-multilable-model")
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# Run inference
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preds = model("Mitarbeit am wissenschaftlichen Arbeitsplatz (LV0125)")
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```
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<!--
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### Training Set Metrics
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| Training set | Min | Median | Max |
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|:-------------|:----|:---------|:----|
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| Word count | 1 | 120.1215 | 537 |
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### Training Hyperparameters
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- batch_size: (16, 16)
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:------:|:----:|:-------------:|:---------------:|
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| 0.0011 | 1 | 0.2952 | - |
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| 0.0552 | 50 | 0.2217 | - |
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| 0.1105 | 100 | 0.1748 | - |
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| 0.1657 | 150 | 0.15 | - |
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| 0.2210 | 200 | 0.1397 | - |
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| 0.2762 | 250 | 0.1241 | - |
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| 0.3315 | 300 | 0.1208 | - |
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| 0.3867 | 350 | 0.1128 | - |
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| 0.4420 | 400 | 0.1044 | - |
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| 0.4972 | 450 | 0.1121 | - |
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| 0.5525 | 500 | 0.106 | - |
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| 0.6077 | 550 | 0.1069 | - |
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| 0.6630 | 600 | 0.098 | - |
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| 0.7182 | 650 | 0.0921 | - |
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| 0.7735 | 700 | 0.0986 | - |
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| 0.8287 | 750 | 0.0838 | - |
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| 0.8840 | 800 | 0.091 | - |
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| 0.9392 | 850 | 0.0934 | - |
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| 0.9945 | 900 | 0.0954 | - |
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### Framework Versions
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- Python: 3.12.3
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config_setfit.json
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{
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}
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{
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"labels": null,
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"normalize_embeddings": false
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 90864192
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version https://git-lfs.github.com/spec/v1
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oid sha256:94453a28695f826bd7955817136533b0d3cd57f03a9f4b5907b986b8d419d207
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size 90864192
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model_head.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 21396
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version https://git-lfs.github.com/spec/v1
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oid sha256:e0a432ee29f7090fa5a16b651ad4b4773ae8eecf202bb18dc4b5f0bfa5051115
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size 21396
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