Chernoffface
commited on
Commit
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0a24492
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Parent(s):
2c2d6bc
Add SetFit model
Browse files- README.md +74 -110
- config_setfit.json +2 -2
- model.safetensors +1 -1
- model_head.pkl +2 -2
README.md
CHANGED
@@ -9,18 +9,62 @@ base_model: sentence-transformers/paraphrase-mpnet-base-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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@@ -70,7 +114,7 @@ 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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@@ -100,9 +144,9 @@ preds = model("Grundlagen der Fachdidaktik Pädagogik")
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## Training Details
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### Training Set Metrics
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| Training set | Min | Median
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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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| 0.0003 | 1 | 0.0019 | - |
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| 0.0127 | 50 | 0.0184 | - |
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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: Serious Games Einführung in die Thematik Serious Games Grundlagen Anwendungsgebiete
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und Trends Die Einzelthemen umfassen unter anderem Einführung in Serious Games
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Game Development Game Design Game Technology Tools und Engines Personalisierung
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und Adaption Interactive Digital Storytelling Authoring und Content Generation
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Multiplayer Games Game Interfaces und Sensor Technology Effects Affects und User
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Experience Mobile Games Serious Games Anwendungsbereiche und Beispiele Die Übungen
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enthalten Theorie und Praxisanteile Dabei wird die Verwendung einer Game Engine
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gelehrt.
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- text: Aerobotics Seminar Einführung in die Aufgabenstellung die vorhandene Infrastruktur
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und den zu durchlaufenden Entwicklungsprozess Entwurf und Implementierung von
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Algorithmen zur Flugregelung in Gruppenarbeit Diskussion des Fortschritts in regelmäßigen
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Flugdemonstration Abschließende Präsentation und Dokumentation
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- text: "Seminar Intraoperative Imaging and Machine Learning For many applications\
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\ techniques like deep learning allow for considerably faster algorithm development\
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\ and allow to automate tasks that were performed manually in the past In medical\
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\ imaging a large variety of tasks that interfere with clinical workflows has\
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\ the potential for automation However at the same time new challenges arise like\
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\ data privacy regulations and ethics concerns In this seminar we want to develop\
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\ an application that allows for the automation of an based intraoperative planning\
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\ or measurement procedure from a holistic perspective To this end we will invite\
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\ a surgeon to explain the medical background and visit the operating room to\
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\ understand the surgeons\x92 needs while performing the task Having understood\
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\ the underlying medical problem we will look into topics of data privacy code\
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\ of ethics prototype development and UI design for surgeons Furthermore we will\
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\ touch regulatory requirements necessary for releasing software to clinics At\
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\ the end of the seminar the students will have developed and documented a prototypical\
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\ application for the indented intraoperative use case Students will be able to\
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\ visit an operation room following the rules of such an environment perform their\
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\ own literature research on a given subject independently research this subject\
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\ according to data privacy and ethical standard present and introduce the subject\
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\ to their student peers give a scientific talk in English according to international\
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\ conference standards describe their results in a scientific report"
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- text: Plattformen und Systeme für eLearning Platforms and Systems for eLearning
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Mit dieser Vorlesung wird eine Übersicht über technische Systeme und Plattformen
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im Bereich des eLearning gegeben insbesondere über Learning Management Systeme
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LMS Prüfungssysteme bis hin zu Campus Management Systemen Neben der Struktur und
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dem Einsatz werden auch Austauschformate sowie Individuallösungen für digitale
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Lernszenarien vorgestellt Neben den reinen funktionalen Softwareanforderung und
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deren Realisierungen werden insbesondere auch die Anforderungen aus Sicht der
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Lehrenden und Studierenden behandelt Die Benutzungsoberflächen der verwendeten
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Systeme müssen dafür eine gute User Experience aufweisen welche durch Methoden
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der messbar werden Diese werden mit dem Fokus auf didaktische Szenarien behandelt
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Grundsätzlich müssen im Lehr Lernkontext personenbezogene Daten benutzt werden
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damit ggf diverse Analysen durchgeführt werden können Diese bilden die Grundlage
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für die Learning Analytics Die Anforderungen des Datenschutzes sind zu berücksichtigen
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Neben einer theoretischen Übersicht werden anhand aktueller Systeme verschiedene
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didaktische Szenarien umgesetzt und nach technischen Kriterien analysiert Innerhalb
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der Übung werden dafür einzelne Beispiele mit einem aktuellen System vorgestellt
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und auf Herausforderungen eingegangen Diese werden mit aktuellen Forschungsergebnissen
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verglichen und kritisch diskutiert In den Übungen sind Hausübungen oder Kleinprojekte
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in Teams zu bearbeiten und in den Übungsgruppen zu präsentieren und die Lösungen
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zu verteidigen.
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- text: Seminar Internet Technology Das Seminar behandelt aktuelle Themen der Systems
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industrielle Kommunikation konfigurierbare Netze Clouds Sicherheit und Privatsphäre
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sowie Modellierung Evaluierung und Verifikation von Kommunikationssystemen und
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protokollen.
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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("Seminar Internet Technology Das Seminar behandelt aktuelle Themen der Systems industrielle Kommunikation konfigurierbare Netze Clouds Sicherheit und Privatsphäre sowie Modellierung Evaluierung und Verifikation von Kommunikationssystemen und protokollen.")
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```
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<!--
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## Training Details
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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 | 3 | 131.6738 | 514 |
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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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| 0.0014 | 1 | 0.2798 | - |
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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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"normalize_embeddings": false,
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"labels": null
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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 437967672
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version https://git-lfs.github.com/spec/v1
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oid sha256:92972fdc0601d168d56004e78d57ba956c481075c9ea4613576d48e38177b8e9
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size 437967672
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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
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version https://git-lfs.github.com/spec/v1
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oid sha256:3631581829b8a144cf4ed104775763cd8f15d7c6018e93f041e2edb943bdd511
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size 39828
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