Add SetFit model
Browse files- README.md +347 -139
- config.json +1 -1
- config_sentence_transformers.json +3 -3
- config_setfit.json +9 -2
- model.safetensors +1 -1
- model_head.pkl +2 -2
- tokenizer_config.json +1 -1
README.md
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---
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library_name: setfit
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tags:
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- setfit
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- sentence-transformers
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- text-classification
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- generated_from_setfit_trainer
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base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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metrics:
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- accuracy
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widget:
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- text: '
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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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# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) as the Sentence Transformer embedding model. A
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The model has been trained using an efficient few-shot learning technique that involves:
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### Model Description
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- **Model Type:** SetFit
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- **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
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- **Classification head:** a
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- **Maximum Sequence Length:** 128 tokens
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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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 Details
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### Training Set Metrics
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| Training set | Min | Median
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| Word count | 1 |
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### Training Hyperparameters
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- batch_size: (16, 16)
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- num_epochs: (2, 2)
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- max_steps: -1
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- sampling_strategy: oversampling
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- num_iterations:
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- body_learning_rate: (2e-05, 2e-05)
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- head_learning_rate: 2e-05
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- loss: CosineSimilarityLoss
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- load_best_model_at_end: False
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### Training Results
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### Framework Versions
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- Python: 3.12.3
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- SetFit: 1.1.0
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- Sentence Transformers: 3.
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- Transformers: 4.
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- PyTorch: 2.
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- Datasets:
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- Tokenizers: 0.
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## Citation
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---
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base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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library_name: setfit
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metrics:
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- accuracy
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pipeline_tag: text-classification
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tags:
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- setfit
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- sentence-transformers
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- text-classification
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- generated_from_setfit_trainer
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widget:
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- text: How much should I invest in communication activities?
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- text: In addition, we will consider public reactions and reviews of these works.
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- text: Grundlagen der Fachdidaktik Pädagogik
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- text: 'Die Einzelthemen umfassen: * Hard- and Software-Architecture of Modern Game
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Systems * Time Management in Milliseconds * Asset Loading and Compression * Physically
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Based Realtime Rendering and Animations * Handling of Large Game Scenes * Audio
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Simulation and Mixing * Constraint-Based Physics Simulation * Artificial Intelligence
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for Games * Multiplayer-Networking * Procedural Content Creation * Integration
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of Scripting Languages * Optimization and parallelization of CPU and GPU Code
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Die Übungen enthalten Theorie- und Praxisanteile.'
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- text: 'Wie entsteht überhaupt eine Ausstellung und in diesem Fall: eine, die weniger
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auf den Wert des Originals als die Kreativität ihrer Besucher setzt?'
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inference: false
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---
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# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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+
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) as the Sentence Transformer embedding model. A MultiOutputClassifier instance is used for classification.
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The model has been trained using an efficient few-shot learning technique that involves:
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### Model Description
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- **Model Type:** SetFit
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- **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
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- **Classification head:** a MultiOutputClassifier instance
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- **Maximum Sequence Length:** 128 tokens
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- **Number of Classes:** 6 classes
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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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("Grundlagen der Fachdidaktik Pädagogik")
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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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| 104 |
+
|:-------------|:----|:--------|:----|
|
| 105 |
+
| Word count | 1 | 12.9119 | 131 |
|
| 106 |
|
| 107 |
### Training Hyperparameters
|
| 108 |
- batch_size: (16, 16)
|
| 109 |
- num_epochs: (2, 2)
|
| 110 |
- max_steps: -1
|
| 111 |
- sampling_strategy: oversampling
|
| 112 |
+
- num_iterations: 40
|
| 113 |
- body_learning_rate: (2e-05, 2e-05)
|
| 114 |
- head_learning_rate: 2e-05
|
| 115 |
- loss: CosineSimilarityLoss
|
|
|
|
| 124 |
- load_best_model_at_end: False
|
| 125 |
|
| 126 |
### Training Results
|
| 127 |
+
| Epoch | Step | Training Loss | Validation Loss |
|
| 128 |
+
|:------:|:-----:|:-------------:|:---------------:|
|
| 129 |
+
| 0.0001 | 1 | 0.1571 | - |
|
| 130 |
+
| 0.0063 | 50 | 0.1986 | - |
|
| 131 |
+
| 0.0127 | 100 | 0.1774 | - |
|
| 132 |
+
| 0.0190 | 150 | 0.136 | - |
|
| 133 |
+
| 0.0254 | 200 | 0.1061 | - |
|
| 134 |
+
| 0.0317 | 250 | 0.0779 | - |
|
| 135 |
+
| 0.0380 | 300 | 0.0671 | - |
|
| 136 |
+
| 0.0444 | 350 | 0.0482 | - |
|
| 137 |
+
| 0.0507 | 400 | 0.0444 | - |
|
| 138 |
+
| 0.0571 | 450 | 0.0427 | - |
|
| 139 |
+
| 0.0634 | 500 | 0.0323 | - |
|
| 140 |
+
| 0.0698 | 550 | 0.0274 | - |
|
| 141 |
+
| 0.0761 | 600 | 0.0301 | - |
|
| 142 |
+
| 0.0824 | 650 | 0.0259 | - |
|
| 143 |
+
| 0.0888 | 700 | 0.0274 | - |
|
| 144 |
+
| 0.0951 | 750 | 0.0305 | - |
|
| 145 |
+
| 0.1015 | 800 | 0.0221 | - |
|
| 146 |
+
| 0.1078 | 850 | 0.0185 | - |
|
| 147 |
+
| 0.1141 | 900 | 0.0208 | - |
|
| 148 |
+
| 0.1205 | 950 | 0.0198 | - |
|
| 149 |
+
| 0.1268 | 1000 | 0.0107 | - |
|
| 150 |
+
| 0.1332 | 1050 | 0.0149 | - |
|
| 151 |
+
| 0.1395 | 1100 | 0.0162 | - |
|
| 152 |
+
| 0.1458 | 1150 | 0.0119 | - |
|
| 153 |
+
| 0.1522 | 1200 | 0.0162 | - |
|
| 154 |
+
| 0.1585 | 1250 | 0.0133 | - |
|
| 155 |
+
| 0.1649 | 1300 | 0.0177 | - |
|
| 156 |
+
| 0.1712 | 1350 | 0.0102 | - |
|
| 157 |
+
| 0.1776 | 1400 | 0.0224 | - |
|
| 158 |
+
| 0.1839 | 1450 | 0.0107 | - |
|
| 159 |
+
| 0.1902 | 1500 | 0.0182 | - |
|
| 160 |
+
| 0.1966 | 1550 | 0.0137 | - |
|
| 161 |
+
| 0.2029 | 1600 | 0.0158 | - |
|
| 162 |
+
| 0.2093 | 1650 | 0.0142 | - |
|
| 163 |
+
| 0.2156 | 1700 | 0.0117 | - |
|
| 164 |
+
| 0.2219 | 1750 | 0.0161 | - |
|
| 165 |
+
| 0.2283 | 1800 | 0.0128 | - |
|
| 166 |
+
| 0.2346 | 1850 | 0.0118 | - |
|
| 167 |
+
| 0.2410 | 1900 | 0.0125 | - |
|
| 168 |
+
| 0.2473 | 1950 | 0.0135 | - |
|
| 169 |
+
| 0.2536 | 2000 | 0.0123 | - |
|
| 170 |
+
| 0.2600 | 2050 | 0.0128 | - |
|
| 171 |
+
| 0.2663 | 2100 | 0.0119 | - |
|
| 172 |
+
| 0.2727 | 2150 | 0.0074 | - |
|
| 173 |
+
| 0.2790 | 2200 | 0.0116 | - |
|
| 174 |
+
| 0.2854 | 2250 | 0.0088 | - |
|
| 175 |
+
| 0.2917 | 2300 | 0.008 | - |
|
| 176 |
+
| 0.2980 | 2350 | 0.0137 | - |
|
| 177 |
+
| 0.3044 | 2400 | 0.0087 | - |
|
| 178 |
+
| 0.3107 | 2450 | 0.0107 | - |
|
| 179 |
+
| 0.3171 | 2500 | 0.0118 | - |
|
| 180 |
+
| 0.3234 | 2550 | 0.0096 | - |
|
| 181 |
+
| 0.3297 | 2600 | 0.0073 | - |
|
| 182 |
+
| 0.3361 | 2650 | 0.0125 | - |
|
| 183 |
+
| 0.3424 | 2700 | 0.0085 | - |
|
| 184 |
+
| 0.3488 | 2750 | 0.0081 | - |
|
| 185 |
+
| 0.3551 | 2800 | 0.0097 | - |
|
| 186 |
+
| 0.3614 | 2850 | 0.0104 | - |
|
| 187 |
+
| 0.3678 | 2900 | 0.0062 | - |
|
| 188 |
+
| 0.3741 | 2950 | 0.0124 | - |
|
| 189 |
+
| 0.3805 | 3000 | 0.0115 | - |
|
| 190 |
+
| 0.3868 | 3050 | 0.012 | - |
|
| 191 |
+
| 0.3932 | 3100 | 0.0147 | - |
|
| 192 |
+
| 0.3995 | 3150 | 0.0097 | - |
|
| 193 |
+
| 0.4058 | 3200 | 0.0107 | - |
|
| 194 |
+
| 0.4122 | 3250 | 0.0074 | - |
|
| 195 |
+
| 0.4185 | 3300 | 0.013 | - |
|
| 196 |
+
| 0.4249 | 3350 | 0.0115 | - |
|
| 197 |
+
| 0.4312 | 3400 | 0.008 | - |
|
| 198 |
+
| 0.4375 | 3450 | 0.0087 | - |
|
| 199 |
+
| 0.4439 | 3500 | 0.0099 | - |
|
| 200 |
+
| 0.4502 | 3550 | 0.0076 | - |
|
| 201 |
+
| 0.4566 | 3600 | 0.0118 | - |
|
| 202 |
+
| 0.4629 | 3650 | 0.013 | - |
|
| 203 |
+
| 0.4692 | 3700 | 0.0107 | - |
|
| 204 |
+
| 0.4756 | 3750 | 0.0123 | - |
|
| 205 |
+
| 0.4819 | 3800 | 0.0101 | - |
|
| 206 |
+
| 0.4883 | 3850 | 0.0095 | - |
|
| 207 |
+
| 0.4946 | 3900 | 0.01 | - |
|
| 208 |
+
| 0.5010 | 3950 | 0.0068 | - |
|
| 209 |
+
| 0.5073 | 4000 | 0.0064 | - |
|
| 210 |
+
| 0.5136 | 4050 | 0.0096 | - |
|
| 211 |
+
| 0.5200 | 4100 | 0.0063 | - |
|
| 212 |
+
| 0.5263 | 4150 | 0.0083 | - |
|
| 213 |
+
| 0.5327 | 4200 | 0.0067 | - |
|
| 214 |
+
| 0.5390 | 4250 | 0.0095 | - |
|
| 215 |
+
| 0.5453 | 4300 | 0.0097 | - |
|
| 216 |
+
| 0.5517 | 4350 | 0.0057 | - |
|
| 217 |
+
| 0.5580 | 4400 | 0.0101 | - |
|
| 218 |
+
| 0.5644 | 4450 | 0.0101 | - |
|
| 219 |
+
| 0.5707 | 4500 | 0.0043 | - |
|
| 220 |
+
| 0.5770 | 4550 | 0.0099 | - |
|
| 221 |
+
| 0.5834 | 4600 | 0.0091 | - |
|
| 222 |
+
| 0.5897 | 4650 | 0.0065 | - |
|
| 223 |
+
| 0.5961 | 4700 | 0.0071 | - |
|
| 224 |
+
| 0.6024 | 4750 | 0.0035 | - |
|
| 225 |
+
| 0.6088 | 4800 | 0.0088 | - |
|
| 226 |
+
| 0.6151 | 4850 | 0.0079 | - |
|
| 227 |
+
| 0.6214 | 4900 | 0.0094 | - |
|
| 228 |
+
| 0.6278 | 4950 | 0.0105 | - |
|
| 229 |
+
| 0.6341 | 5000 | 0.0091 | - |
|
| 230 |
+
| 0.6405 | 5050 | 0.0109 | - |
|
| 231 |
+
| 0.6468 | 5100 | 0.0081 | - |
|
| 232 |
+
| 0.6531 | 5150 | 0.0087 | - |
|
| 233 |
+
| 0.6595 | 5200 | 0.0091 | - |
|
| 234 |
+
| 0.6658 | 5250 | 0.0071 | - |
|
| 235 |
+
| 0.6722 | 5300 | 0.0072 | - |
|
| 236 |
+
| 0.6785 | 5350 | 0.0084 | - |
|
| 237 |
+
| 0.6848 | 5400 | 0.0099 | - |
|
| 238 |
+
| 0.6912 | 5450 | 0.004 | - |
|
| 239 |
+
| 0.6975 | 5500 | 0.0038 | - |
|
| 240 |
+
| 0.7039 | 5550 | 0.0072 | - |
|
| 241 |
+
| 0.7102 | 5600 | 0.0084 | - |
|
| 242 |
+
| 0.7166 | 5650 | 0.004 | - |
|
| 243 |
+
| 0.7229 | 5700 | 0.0077 | - |
|
| 244 |
+
| 0.7292 | 5750 | 0.0066 | - |
|
| 245 |
+
| 0.7356 | 5800 | 0.0043 | - |
|
| 246 |
+
| 0.7419 | 5850 | 0.0054 | - |
|
| 247 |
+
| 0.7483 | 5900 | 0.0107 | - |
|
| 248 |
+
| 0.7546 | 5950 | 0.0046 | - |
|
| 249 |
+
| 0.7609 | 6000 | 0.0075 | - |
|
| 250 |
+
| 0.7673 | 6050 | 0.0106 | - |
|
| 251 |
+
| 0.7736 | 6100 | 0.0063 | - |
|
| 252 |
+
| 0.7800 | 6150 | 0.007 | - |
|
| 253 |
+
| 0.7863 | 6200 | 0.0066 | - |
|
| 254 |
+
| 0.7926 | 6250 | 0.0067 | - |
|
| 255 |
+
| 0.7990 | 6300 | 0.0078 | - |
|
| 256 |
+
| 0.8053 | 6350 | 0.0093 | - |
|
| 257 |
+
| 0.8117 | 6400 | 0.0055 | - |
|
| 258 |
+
| 0.8180 | 6450 | 0.0074 | - |
|
| 259 |
+
| 0.8244 | 6500 | 0.0115 | - |
|
| 260 |
+
| 0.8307 | 6550 | 0.0058 | - |
|
| 261 |
+
| 0.8370 | 6600 | 0.005 | - |
|
| 262 |
+
| 0.8434 | 6650 | 0.007 | - |
|
| 263 |
+
| 0.8497 | 6700 | 0.0053 | - |
|
| 264 |
+
| 0.8561 | 6750 | 0.0086 | - |
|
| 265 |
+
| 0.8624 | 6800 | 0.0054 | - |
|
| 266 |
+
| 0.8687 | 6850 | 0.0055 | - |
|
| 267 |
+
| 0.8751 | 6900 | 0.006 | - |
|
| 268 |
+
| 0.8814 | 6950 | 0.0068 | - |
|
| 269 |
+
| 0.8878 | 7000 | 0.0103 | - |
|
| 270 |
+
| 0.8941 | 7050 | 0.0054 | - |
|
| 271 |
+
| 0.9004 | 7100 | 0.007 | - |
|
| 272 |
+
| 0.9068 | 7150 | 0.0047 | - |
|
| 273 |
+
| 0.9131 | 7200 | 0.0076 | - |
|
| 274 |
+
| 0.9195 | 7250 | 0.0077 | - |
|
| 275 |
+
| 0.9258 | 7300 | 0.0058 | - |
|
| 276 |
+
| 0.9321 | 7350 | 0.0056 | - |
|
| 277 |
+
| 0.9385 | 7400 | 0.0041 | - |
|
| 278 |
+
| 0.9448 | 7450 | 0.0062 | - |
|
| 279 |
+
| 0.9512 | 7500 | 0.0044 | - |
|
| 280 |
+
| 0.9575 | 7550 | 0.0042 | - |
|
| 281 |
+
| 0.9639 | 7600 | 0.0095 | - |
|
| 282 |
+
| 0.9702 | 7650 | 0.0045 | - |
|
| 283 |
+
| 0.9765 | 7700 | 0.0062 | - |
|
| 284 |
+
| 0.9829 | 7750 | 0.0036 | - |
|
| 285 |
+
| 0.9892 | 7800 | 0.0086 | - |
|
| 286 |
+
| 0.9956 | 7850 | 0.0071 | - |
|
| 287 |
+
| 1.0019 | 7900 | 0.0103 | - |
|
| 288 |
+
| 1.0082 | 7950 | 0.004 | - |
|
| 289 |
+
| 1.0146 | 8000 | 0.0059 | - |
|
| 290 |
+
| 1.0209 | 8050 | 0.0053 | - |
|
| 291 |
+
| 1.0273 | 8100 | 0.0079 | - |
|
| 292 |
+
| 1.0336 | 8150 | 0.0078 | - |
|
| 293 |
+
| 1.0399 | 8200 | 0.0077 | - |
|
| 294 |
+
| 1.0463 | 8250 | 0.0062 | - |
|
| 295 |
+
| 1.0526 | 8300 | 0.005 | - |
|
| 296 |
+
| 1.0590 | 8350 | 0.0071 | - |
|
| 297 |
+
| 1.0653 | 8400 | 0.0042 | - |
|
| 298 |
+
| 1.0717 | 8450 | 0.0054 | - |
|
| 299 |
+
| 1.0780 | 8500 | 0.0048 | - |
|
| 300 |
+
| 1.0843 | 8550 | 0.0045 | - |
|
| 301 |
+
| 1.0907 | 8600 | 0.0062 | - |
|
| 302 |
+
| 1.0970 | 8650 | 0.0094 | - |
|
| 303 |
+
| 1.1034 | 8700 | 0.0043 | - |
|
| 304 |
+
| 1.1097 | 8750 | 0.004 | - |
|
| 305 |
+
| 1.1160 | 8800 | 0.003 | - |
|
| 306 |
+
| 1.1224 | 8850 | 0.0026 | - |
|
| 307 |
+
| 1.1287 | 8900 | 0.0051 | - |
|
| 308 |
+
| 1.1351 | 8950 | 0.0046 | - |
|
| 309 |
+
| 1.1414 | 9000 | 0.0046 | - |
|
| 310 |
+
| 1.1477 | 9050 | 0.0075 | - |
|
| 311 |
+
| 1.1541 | 9100 | 0.0066 | - |
|
| 312 |
+
| 1.1604 | 9150 | 0.0078 | - |
|
| 313 |
+
| 1.1668 | 9200 | 0.0069 | - |
|
| 314 |
+
| 1.1731 | 9250 | 0.0087 | - |
|
| 315 |
+
| 1.1795 | 9300 | 0.0047 | - |
|
| 316 |
+
| 1.1858 | 9350 | 0.0037 | - |
|
| 317 |
+
| 1.1921 | 9400 | 0.007 | - |
|
| 318 |
+
| 1.1985 | 9450 | 0.0069 | - |
|
| 319 |
+
| 1.2048 | 9500 | 0.0061 | - |
|
| 320 |
+
| 1.2112 | 9550 | 0.0047 | - |
|
| 321 |
+
| 1.2175 | 9600 | 0.0065 | - |
|
| 322 |
+
| 1.2238 | 9650 | 0.0058 | - |
|
| 323 |
+
| 1.2302 | 9700 | 0.0061 | - |
|
| 324 |
+
| 1.2365 | 9750 | 0.0055 | - |
|
| 325 |
+
| 1.2429 | 9800 | 0.0064 | - |
|
| 326 |
+
| 1.2492 | 9850 | 0.0041 | - |
|
| 327 |
+
| 1.2555 | 9900 | 0.0086 | - |
|
| 328 |
+
| 1.2619 | 9950 | 0.0053 | - |
|
| 329 |
+
| 1.2682 | 10000 | 0.0047 | - |
|
| 330 |
+
| 1.2746 | 10050 | 0.0053 | - |
|
| 331 |
+
| 1.2809 | 10100 | 0.003 | - |
|
| 332 |
+
| 1.2873 | 10150 | 0.0046 | - |
|
| 333 |
+
| 1.2936 | 10200 | 0.0052 | - |
|
| 334 |
+
| 1.2999 | 10250 | 0.0056 | - |
|
| 335 |
+
| 1.3063 | 10300 | 0.0052 | - |
|
| 336 |
+
| 1.3126 | 10350 | 0.0079 | - |
|
| 337 |
+
| 1.3190 | 10400 | 0.006 | - |
|
| 338 |
+
| 1.3253 | 10450 | 0.0055 | - |
|
| 339 |
+
| 1.3316 | 10500 | 0.0066 | - |
|
| 340 |
+
| 1.3380 | 10550 | 0.0076 | - |
|
| 341 |
+
| 1.3443 | 10600 | 0.0037 | - |
|
| 342 |
+
| 1.3507 | 10650 | 0.0066 | - |
|
| 343 |
+
| 1.3570 | 10700 | 0.0059 | - |
|
| 344 |
+
| 1.3633 | 10750 | 0.0057 | - |
|
| 345 |
+
| 1.3697 | 10800 | 0.0038 | - |
|
| 346 |
+
| 1.3760 | 10850 | 0.0044 | - |
|
| 347 |
+
| 1.3824 | 10900 | 0.0059 | - |
|
| 348 |
+
| 1.3887 | 10950 | 0.0073 | - |
|
| 349 |
+
| 1.3951 | 11000 | 0.0055 | - |
|
| 350 |
+
| 1.4014 | 11050 | 0.0039 | - |
|
| 351 |
+
| 1.4077 | 11100 | 0.0054 | - |
|
| 352 |
+
| 1.4141 | 11150 | 0.0068 | - |
|
| 353 |
+
| 1.4204 | 11200 | 0.0067 | - |
|
| 354 |
+
| 1.4268 | 11250 | 0.0041 | - |
|
| 355 |
+
| 1.4331 | 11300 | 0.0076 | - |
|
| 356 |
+
| 1.4394 | 11350 | 0.0071 | - |
|
| 357 |
+
| 1.4458 | 11400 | 0.0044 | - |
|
| 358 |
+
| 1.4521 | 11450 | 0.0061 | - |
|
| 359 |
+
| 1.4585 | 11500 | 0.0039 | - |
|
| 360 |
+
| 1.4648 | 11550 | 0.006 | - |
|
| 361 |
+
| 1.4711 | 11600 | 0.0045 | - |
|
| 362 |
+
| 1.4775 | 11650 | 0.0044 | - |
|
| 363 |
+
| 1.4838 | 11700 | 0.0063 | - |
|
| 364 |
+
| 1.4902 | 11750 | 0.0061 | - |
|
| 365 |
+
| 1.4965 | 11800 | 0.0058 | - |
|
| 366 |
+
| 1.5029 | 11850 | 0.0039 | - |
|
| 367 |
+
| 1.5092 | 11900 | 0.0041 | - |
|
| 368 |
+
| 1.5155 | 11950 | 0.0052 | - |
|
| 369 |
+
| 1.5219 | 12000 | 0.0034 | - |
|
| 370 |
+
| 1.5282 | 12050 | 0.0078 | - |
|
| 371 |
+
| 1.5346 | 12100 | 0.0049 | - |
|
| 372 |
+
| 1.5409 | 12150 | 0.0064 | - |
|
| 373 |
+
| 1.5472 | 12200 | 0.0063 | - |
|
| 374 |
+
| 1.5536 | 12250 | 0.0068 | - |
|
| 375 |
+
| 1.5599 | 12300 | 0.008 | - |
|
| 376 |
+
| 1.5663 | 12350 | 0.0043 | - |
|
| 377 |
+
| 1.5726 | 12400 | 0.0057 | - |
|
| 378 |
+
| 1.5789 | 12450 | 0.0044 | - |
|
| 379 |
+
| 1.5853 | 12500 | 0.0048 | - |
|
| 380 |
+
| 1.5916 | 12550 | 0.0049 | - |
|
| 381 |
+
| 1.5980 | 12600 | 0.0052 | - |
|
| 382 |
+
| 1.6043 | 12650 | 0.0061 | - |
|
| 383 |
+
| 1.6107 | 12700 | 0.0066 | - |
|
| 384 |
+
| 1.6170 | 12750 | 0.0079 | - |
|
| 385 |
+
| 1.6233 | 12800 | 0.0047 | - |
|
| 386 |
+
| 1.6297 | 12850 | 0.005 | - |
|
| 387 |
+
| 1.6360 | 12900 | 0.0034 | - |
|
| 388 |
+
| 1.6424 | 12950 | 0.0051 | - |
|
| 389 |
+
| 1.6487 | 13000 | 0.006 | - |
|
| 390 |
+
| 1.6550 | 13050 | 0.0046 | - |
|
| 391 |
+
| 1.6614 | 13100 | 0.003 | - |
|
| 392 |
+
| 1.6677 | 13150 | 0.0055 | - |
|
| 393 |
+
| 1.6741 | 13200 | 0.0069 | - |
|
| 394 |
+
| 1.6804 | 13250 | 0.0033 | - |
|
| 395 |
+
| 1.6867 | 13300 | 0.0095 | - |
|
| 396 |
+
| 1.6931 | 13350 | 0.0043 | - |
|
| 397 |
+
| 1.6994 | 13400 | 0.0055 | - |
|
| 398 |
+
| 1.7058 | 13450 | 0.0081 | - |
|
| 399 |
+
| 1.7121 | 13500 | 0.0042 | - |
|
| 400 |
+
| 1.7185 | 13550 | 0.0081 | - |
|
| 401 |
+
| 1.7248 | 13600 | 0.0055 | - |
|
| 402 |
+
| 1.7311 | 13650 | 0.0043 | - |
|
| 403 |
+
| 1.7375 | 13700 | 0.0033 | - |
|
| 404 |
+
| 1.7438 | 13750 | 0.0044 | - |
|
| 405 |
+
| 1.7502 | 13800 | 0.0062 | - |
|
| 406 |
+
| 1.7565 | 13850 | 0.0032 | - |
|
| 407 |
+
| 1.7628 | 13900 | 0.0043 | - |
|
| 408 |
+
| 1.7692 | 13950 | 0.0079 | - |
|
| 409 |
+
| 1.7755 | 14000 | 0.0053 | - |
|
| 410 |
+
| 1.7819 | 14050 | 0.0044 | - |
|
| 411 |
+
| 1.7882 | 14100 | 0.0064 | - |
|
| 412 |
+
| 1.7945 | 14150 | 0.0051 | - |
|
| 413 |
+
| 1.8009 | 14200 | 0.0088 | - |
|
| 414 |
+
| 1.8072 | 14250 | 0.0048 | - |
|
| 415 |
+
| 1.8136 | 14300 | 0.0044 | - |
|
| 416 |
+
| 1.8199 | 14350 | 0.0071 | - |
|
| 417 |
+
| 1.8263 | 14400 | 0.0058 | - |
|
| 418 |
+
| 1.8326 | 14450 | 0.007 | - |
|
| 419 |
+
| 1.8389 | 14500 | 0.0028 | - |
|
| 420 |
+
| 1.8453 | 14550 | 0.0046 | - |
|
| 421 |
+
| 1.8516 | 14600 | 0.0061 | - |
|
| 422 |
+
| 1.8580 | 14650 | 0.0054 | - |
|
| 423 |
+
| 1.8643 | 14700 | 0.004 | - |
|
| 424 |
+
| 1.8706 | 14750 | 0.0034 | - |
|
| 425 |
+
| 1.8770 | 14800 | 0.0044 | - |
|
| 426 |
+
| 1.8833 | 14850 | 0.0033 | - |
|
| 427 |
+
| 1.8897 | 14900 | 0.007 | - |
|
| 428 |
+
| 1.8960 | 14950 | 0.0044 | - |
|
| 429 |
+
| 1.9023 | 15000 | 0.0045 | - |
|
| 430 |
+
| 1.9087 | 15050 | 0.0045 | - |
|
| 431 |
+
| 1.9150 | 15100 | 0.0093 | - |
|
| 432 |
+
| 1.9214 | 15150 | 0.0036 | - |
|
| 433 |
+
| 1.9277 | 15200 | 0.0055 | - |
|
| 434 |
+
| 1.9341 | 15250 | 0.0037 | - |
|
| 435 |
+
| 1.9404 | 15300 | 0.0043 | - |
|
| 436 |
+
| 1.9467 | 15350 | 0.0034 | - |
|
| 437 |
+
| 1.9531 | 15400 | 0.0068 | - |
|
| 438 |
+
| 1.9594 | 15450 | 0.0058 | - |
|
| 439 |
+
| 1.9658 | 15500 | 0.0069 | - |
|
| 440 |
+
| 1.9721 | 15550 | 0.0081 | - |
|
| 441 |
+
| 1.9784 | 15600 | 0.0061 | - |
|
| 442 |
+
| 1.9848 | 15650 | 0.0039 | - |
|
| 443 |
+
| 1.9911 | 15700 | 0.0065 | - |
|
| 444 |
+
| 1.9975 | 15750 | 0.0048 | - |
|
| 445 |
|
| 446 |
### Framework Versions
|
| 447 |
- Python: 3.12.3
|
| 448 |
- SetFit: 1.1.0
|
| 449 |
+
- Sentence Transformers: 3.2.0
|
| 450 |
+
- Transformers: 4.45.2
|
| 451 |
+
- PyTorch: 2.5.0+cu121
|
| 452 |
+
- Datasets: 3.0.1
|
| 453 |
+
- Tokenizers: 0.20.1
|
| 454 |
|
| 455 |
## Citation
|
| 456 |
|
config.json
CHANGED
|
@@ -19,7 +19,7 @@
|
|
| 19 |
"pad_token_id": 0,
|
| 20 |
"position_embedding_type": "absolute",
|
| 21 |
"torch_dtype": "float32",
|
| 22 |
-
"transformers_version": "4.
|
| 23 |
"type_vocab_size": 2,
|
| 24 |
"use_cache": true,
|
| 25 |
"vocab_size": 250037
|
|
|
|
| 19 |
"pad_token_id": 0,
|
| 20 |
"position_embedding_type": "absolute",
|
| 21 |
"torch_dtype": "float32",
|
| 22 |
+
"transformers_version": "4.45.2",
|
| 23 |
"type_vocab_size": 2,
|
| 24 |
"use_cache": true,
|
| 25 |
"vocab_size": 250037
|
config_sentence_transformers.json
CHANGED
|
@@ -1,8 +1,8 @@
|
|
| 1 |
{
|
| 2 |
"__version__": {
|
| 3 |
-
"sentence_transformers": "2.0
|
| 4 |
-
"transformers": "4.
|
| 5 |
-
"pytorch": "
|
| 6 |
},
|
| 7 |
"prompts": {},
|
| 8 |
"default_prompt_name": null,
|
|
|
|
| 1 |
{
|
| 2 |
"__version__": {
|
| 3 |
+
"sentence_transformers": "3.2.0",
|
| 4 |
+
"transformers": "4.45.2",
|
| 5 |
+
"pytorch": "2.5.0+cu121"
|
| 6 |
},
|
| 7 |
"prompts": {},
|
| 8 |
"default_prompt_name": null,
|
config_setfit.json
CHANGED
|
@@ -1,4 +1,11 @@
|
|
| 1 |
{
|
| 2 |
-
"
|
| 3 |
-
"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 4 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"normalize_embeddings": false,
|
| 3 |
+
"labels": [
|
| 4 |
+
"Hardware-/Robotikentwicklung",
|
| 5 |
+
"Softwareentwicklung",
|
| 6 |
+
"Nutzerzentriertes Design",
|
| 7 |
+
"Data Analytics & KI",
|
| 8 |
+
"Quantencomputing",
|
| 9 |
+
"IT-Architektur"
|
| 10 |
+
]
|
| 11 |
}
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 470637416
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:cb68e3a38d27f6bc16425cce11ae099d257e05424b0fc224747e41546e10608e
|
| 3 |
size 470637416
|
model_head.pkl
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:cd84bedbd040211b66cb2ea1c1b9c7031f697dc4f79f65021eddc6a6f2ed30f7
|
| 3 |
+
size 21473
|
tokenizer_config.json
CHANGED
|
@@ -42,7 +42,7 @@
|
|
| 42 |
}
|
| 43 |
},
|
| 44 |
"bos_token": "<s>",
|
| 45 |
-
"clean_up_tokenization_spaces":
|
| 46 |
"cls_token": "<s>",
|
| 47 |
"do_lower_case": true,
|
| 48 |
"eos_token": "</s>",
|
|
|
|
| 42 |
}
|
| 43 |
},
|
| 44 |
"bos_token": "<s>",
|
| 45 |
+
"clean_up_tokenization_spaces": false,
|
| 46 |
"cls_token": "<s>",
|
| 47 |
"do_lower_case": true,
|
| 48 |
"eos_token": "</s>",
|