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Chernoffface
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fs-setfit-multilable-model

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
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Add SetFit model

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  1. README.md +347 -139
  2. config.json +1 -1
  3. config_sentence_transformers.json +3 -3
  4. config_setfit.json +9 -2
  5. model.safetensors +1 -1
  6. model_head.pkl +2 -2
  7. tokenizer_config.json +1 -1
README.md CHANGED
@@ -1,104 +1,33 @@
1
  ---
 
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  library_name: setfit
 
 
 
3
  tags:
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  - setfit
5
  - sentence-transformers
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  - text-classification
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  - generated_from_setfit_trainer
8
- 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: Mitarbeit am wissenschaftlichen Arbeitsplatz (LV0125)
13
- - 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
15
- umfassen unter anderem: • Einführung in Serious Games • Game Development, Game
16
- Design • Game Technology, Tools und Engines • Personalisierung und Adaption •
17
- Interactive Digital Storytelling • Authoring und Content Generation • Multiplayer
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- Games • Game Interfaces und Sensor Technology • Effects, Affects und User Experience
19
- • Mobile Games • Serious Games Anwendungsbereiche und Best-Practice Beispiele
20
- Die Übungen enthalten Theorie- und Praxisanteile. Dabei wird die Verwendung einer
21
- Game Engine gelehrt.'
22
- - text: 'Aerobotics Seminar: - Einführung in die Aufgabenstellung, die vorhandene
23
- Infrastruktur und den zu durchlaufenden Entwicklungsprozess
24
-
25
-
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- - Entwurf und Implementierung von Algorithmen zur Flugregelung in Gruppenarbeit
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-
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-
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- - Diskussion des Fortschritts in regelmäßigen Progress-Meetings
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-
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-
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- - Flugdemonstration
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-
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-
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- - Abschließende Präsentation und Dokumentation'
36
- - text: "Digital Transformations, Consumer Well-Being, and Sustainability: Physical\
37
- \ consumption, and individual ownership of material products in particular, has\
38
- \ traditionally been the default mode of consumption and its extent has long been\
39
- \ considered a measure of personal and societal prosperity. However, our daily\
40
- \ 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,\
42
- \ cultivate the prevalence of virtual living worlds through fictional experiences,\
43
- \ and alter our relations to material possessions by an increasing availability\
44
- \ of digital solutions.\n\n\n\nThe course exposes you to state-of-the-art research\
45
- \ on consumer research and digital transformations in fields such as virtual reality,\
46
- \ sharing economy, and blockchain technologies. You will be guided through background\
47
- \ information of consumer behavior and consumer psychology. By creating a collaborative\
48
- \ learning environment, we will explore and critically discuss how digital transformations\
49
- \ affect consumer well-being and sustainability. \n\n\n\nYour role is to be an\
50
- \ active contributor in the class. This course consists of a lectures, discussion\
51
- \ sessions and group presentations. Generally, analysis of readings will be used\
52
- \ to guide our discussion. \n\n\n\nThe main objective of the course is to critically\
53
- \ reflect upon current technological advancements that increasingly permeate everyday\
54
- \ lives. Students will be engaged in exploring technological-social issues in\
55
- \ marketing and be guided into a critical approach on technology-brands-consumers\
56
- \ relationships behind digital transformations."
57
- - text: 'Grundlagen der IT-Sicherheit: Um einen Überblick der IT-Sicherheit zu vermitteln
58
- werden folgende Themen behandelt:
59
-
60
-
61
- Motivation für IT-Sicherheit
62
-
63
-
64
- Grundbegriffe der IT-Sicherheit
65
-
66
-
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- Computer Malware
68
-
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-
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- Kryptographische Grundlagen
71
-
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-
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- Authentisierung
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-
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-
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- Biometrie
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-
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-
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- Zugriffskontrolle
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-
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-
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- Netzwerkund
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-
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-
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- Internetsicherheit
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-
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-
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- Physikalische Sicherheit / Physikalische Angriffe
89
-
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-
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- Sicherheitsevaluierung und Zertifizierung
92
-
93
-
94
- Einführung in den Datenschutz'
95
- pipeline_tag: text-classification
96
  inference: false
97
  ---
98
 
99
  # SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
100
 
101
- 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 OneVsRestClassifier instance is used for classification.
102
 
103
  The model has been trained using an efficient few-shot learning technique that involves:
104
 
@@ -110,9 +39,9 @@ The model has been trained using an efficient few-shot learning technique that i
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  ### Model Description
111
  - **Model Type:** SetFit
112
  - **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
113
- - **Classification head:** a OneVsRestClassifier instance
114
  - **Maximum Sequence Length:** 128 tokens
115
- <!-- - **Number of Classes:** Unknown -->
116
  <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
117
  <!-- - **Language:** Unknown -->
118
  <!-- - **License:** Unknown -->
@@ -141,7 +70,7 @@ from setfit import SetFitModel
141
  # Download from the 🤗 Hub
142
  model = SetFitModel.from_pretrained("Chernoffface/fs-setfit-multilable-model")
143
  # Run inference
144
- preds = model("Mitarbeit am wissenschaftlichen Arbeitsplatz (LV0125)")
145
  ```
146
 
147
  <!--
@@ -171,16 +100,16 @@ preds = model("Mitarbeit am wissenschaftlichen Arbeitsplatz (LV0125)")
171
  ## Training Details
172
 
173
  ### Training Set Metrics
174
- | Training set | Min | Median | Max |
175
- |:-------------|:----|:---------|:----|
176
- | Word count | 1 | 120.1215 | 537 |
177
 
178
  ### Training Hyperparameters
179
  - batch_size: (16, 16)
180
  - num_epochs: (2, 2)
181
  - max_steps: -1
182
  - sampling_strategy: oversampling
183
- - num_iterations: 20
184
  - body_learning_rate: (2e-05, 2e-05)
185
  - head_learning_rate: 2e-05
186
  - loss: CosineSimilarityLoss
@@ -195,54 +124,333 @@ preds = model("Mitarbeit am wissenschaftlichen Arbeitsplatz (LV0125)")
195
  - load_best_model_at_end: False
196
 
197
  ### Training Results
198
- | Epoch | Step | Training Loss | Validation Loss |
199
- |:------:|:----:|:-------------:|:---------------:|
200
- | 0.0011 | 1 | 0.2964 | - |
201
- | 0.0552 | 50 | 0.2042 | - |
202
- | 0.1105 | 100 | 0.1643 | - |
203
- | 0.1657 | 150 | 0.1376 | - |
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- | 0.2210 | 200 | 0.1232 | - |
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- | 0.2762 | 250 | 0.1125 | - |
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- | 0.3315 | 300 | 0.1079 | - |
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- | 0.3867 | 350 | 0.0951 | - |
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- | 0.4420 | 400 | 0.0847 | - |
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- | 0.4972 | 450 | 0.0917 | - |
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- | 0.5525 | 500 | 0.085 | - |
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- | 0.6077 | 550 | 0.0758 | - |
212
- | 0.6630 | 600 | 0.0743 | - |
213
- | 0.7182 | 650 | 0.0671 | - |
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- | 0.7735 | 700 | 0.0743 | - |
215
- | 0.8287 | 750 | 0.0571 | - |
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- | 0.8840 | 800 | 0.0625 | - |
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- | 0.9392 | 850 | 0.0607 | - |
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- | 0.9945 | 900 | 0.0686 | - |
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- | 1.0497 | 950 | 0.0541 | - |
220
- | 1.1050 | 1000 | 0.0553 | - |
221
- | 1.1602 | 1050 | 0.0565 | - |
222
- | 1.2155 | 1100 | 0.0558 | - |
223
- | 1.2707 | 1150 | 0.0578 | - |
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- | 1.3260 | 1200 | 0.0525 | - |
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- | 1.3812 | 1250 | 0.0541 | - |
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- | 1.4365 | 1300 | 0.049 | - |
227
- | 1.4917 | 1350 | 0.0485 | - |
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- | 1.5470 | 1400 | 0.0475 | - |
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- | 1.6022 | 1450 | 0.0479 | - |
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- | 1.6575 | 1500 | 0.0514 | - |
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- | 1.7127 | 1550 | 0.0509 | - |
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- | 1.7680 | 1600 | 0.0517 | - |
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- | 1.8232 | 1650 | 0.0455 | - |
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- | 1.8785 | 1700 | 0.0493 | - |
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- | 1.9337 | 1750 | 0.0501 | - |
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- | 1.9890 | 1800 | 0.0492 | - |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
237
 
238
  ### Framework Versions
239
  - Python: 3.12.3
240
  - SetFit: 1.1.0
241
- - Sentence Transformers: 3.0.0
242
- - Transformers: 4.43.1
243
- - PyTorch: 2.3.1+cu121
244
- - Datasets: 2.20.0
245
- - Tokenizers: 0.19.1
246
 
247
  ## Citation
248
 
 
1
  ---
2
+ base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
3
  library_name: setfit
4
+ metrics:
5
+ - accuracy
6
+ pipeline_tag: text-classification
7
  tags:
8
  - setfit
9
  - sentence-transformers
10
  - text-classification
11
  - generated_from_setfit_trainer
 
 
 
12
  widget:
13
+ - text: How much should I invest in communication activities?
14
+ - text: In addition, we will consider public reactions and reviews of these works.
15
+ - text: Grundlagen der Fachdidaktik Pädagogik
16
+ - 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
20
+ for Games * Multiplayer-Networking * Procedural Content Creation * Integration
21
+ of Scripting Languages * Optimization and parallelization of CPU and GPU Code
22
+ Die Übungen enthalten Theorie- und Praxisanteile.'
23
+ - text: 'Wie entsteht überhaupt eine Ausstellung und in diesem Fall: eine, die weniger
24
+ auf den Wert des Originals als die Kreativität ihrer Besucher setzt?'
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
25
  inference: false
26
  ---
27
 
28
  # SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
29
 
30
+ 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.
31
 
32
  The model has been trained using an efficient few-shot learning technique that involves:
33
 
 
39
  ### Model Description
40
  - **Model Type:** SetFit
41
  - **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
42
+ - **Classification head:** a MultiOutputClassifier instance
43
  - **Maximum Sequence Length:** 128 tokens
44
+ - **Number of Classes:** 6 classes
45
  <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
46
  <!-- - **Language:** Unknown -->
47
  <!-- - **License:** Unknown -->
 
70
  # Download from the 🤗 Hub
71
  model = SetFitModel.from_pretrained("Chernoffface/fs-setfit-multilable-model")
72
  # Run inference
73
+ preds = model("Grundlagen der Fachdidaktik Pädagogik")
74
  ```
75
 
76
  <!--
 
100
  ## Training Details
101
 
102
  ### Training Set Metrics
103
+ | Training set | Min | Median | Max |
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 | - |
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+ | 0.0127 | 100 | 0.1774 | - |
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+ | 0.0190 | 150 | 0.136 | - |
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+ | 0.0254 | 200 | 0.1061 | - |
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+ | 0.0317 | 250 | 0.0779 | - |
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+ | 0.0380 | 300 | 0.0671 | - |
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+ | 0.0444 | 350 | 0.0482 | - |
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+ | 0.0507 | 400 | 0.0444 | - |
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+ | 0.0571 | 450 | 0.0427 | - |
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+ | 0.0634 | 500 | 0.0323 | - |
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+ | 0.0698 | 550 | 0.0274 | - |
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+ | 0.0761 | 600 | 0.0301 | - |
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+ | 0.0824 | 650 | 0.0259 | - |
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+ | 0.0888 | 700 | 0.0274 | - |
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+ | 0.0951 | 750 | 0.0305 | - |
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+ | 0.1015 | 800 | 0.0221 | - |
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+ | 0.1078 | 850 | 0.0185 | - |
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+ | 0.1141 | 900 | 0.0208 | - |
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+ | 0.1205 | 950 | 0.0198 | - |
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+ | 0.1268 | 1000 | 0.0107 | - |
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+ | 0.1332 | 1050 | 0.0149 | - |
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+ | 0.1395 | 1100 | 0.0162 | - |
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+ | 0.1458 | 1150 | 0.0119 | - |
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+ | 0.1522 | 1200 | 0.0162 | - |
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+ | 0.1585 | 1250 | 0.0133 | - |
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+ | 0.1649 | 1300 | 0.0177 | - |
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+ | 0.1712 | 1350 | 0.0102 | - |
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+ | 0.1776 | 1400 | 0.0224 | - |
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+ | 0.1839 | 1450 | 0.0107 | - |
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+ | 0.1902 | 1500 | 0.0182 | - |
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+ | 0.1966 | 1550 | 0.0137 | - |
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+ | 0.2029 | 1600 | 0.0158 | - |
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+ | 0.2093 | 1650 | 0.0142 | - |
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+ | 0.2156 | 1700 | 0.0117 | - |
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+ | 0.2219 | 1750 | 0.0161 | - |
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+ | 0.2283 | 1800 | 0.0128 | - |
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+ | 0.2346 | 1850 | 0.0118 | - |
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+ | 0.2410 | 1900 | 0.0125 | - |
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+ | 0.2473 | 1950 | 0.0135 | - |
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+ | 0.2536 | 2000 | 0.0123 | - |
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+ | 0.2600 | 2050 | 0.0128 | - |
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+ | 0.2663 | 2100 | 0.0119 | - |
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+ | 0.2727 | 2150 | 0.0074 | - |
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+ | 0.2790 | 2200 | 0.0116 | - |
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+ | 0.2854 | 2250 | 0.0088 | - |
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+ | 0.2917 | 2300 | 0.008 | - |
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+ | 0.2980 | 2350 | 0.0137 | - |
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+ | 0.3044 | 2400 | 0.0087 | - |
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+ | 0.3107 | 2450 | 0.0107 | - |
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+ | 0.3171 | 2500 | 0.0118 | - |
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+ | 0.3234 | 2550 | 0.0096 | - |
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+ | 0.3297 | 2600 | 0.0073 | - |
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+ | 0.3361 | 2650 | 0.0125 | - |
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+ | 0.3424 | 2700 | 0.0085 | - |
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+ | 0.3488 | 2750 | 0.0081 | - |
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+ | 0.3551 | 2800 | 0.0097 | - |
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+ | 0.3614 | 2850 | 0.0104 | - |
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+ | 0.3678 | 2900 | 0.0062 | - |
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+ | 0.3741 | 2950 | 0.0124 | - |
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+ | 0.3805 | 3000 | 0.0115 | - |
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+ | 0.3868 | 3050 | 0.012 | - |
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+ | 0.3932 | 3100 | 0.0147 | - |
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+ | 0.3995 | 3150 | 0.0097 | - |
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+ | 0.4058 | 3200 | 0.0107 | - |
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+ | 0.4122 | 3250 | 0.0074 | - |
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+ | 0.4185 | 3300 | 0.013 | - |
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+ | 0.4249 | 3350 | 0.0115 | - |
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+ | 0.4312 | 3400 | 0.008 | - |
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+ | 0.4375 | 3450 | 0.0087 | - |
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+ | 0.4439 | 3500 | 0.0099 | - |
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+ | 0.4502 | 3550 | 0.0076 | - |
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+ | 0.4566 | 3600 | 0.0118 | - |
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+ | 0.4629 | 3650 | 0.013 | - |
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+ | 0.4692 | 3700 | 0.0107 | - |
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+ | 0.4756 | 3750 | 0.0123 | - |
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+ | 0.4819 | 3800 | 0.0101 | - |
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+ | 0.4883 | 3850 | 0.0095 | - |
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+ | 0.4946 | 3900 | 0.01 | - |
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+ | 0.5010 | 3950 | 0.0068 | - |
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+ | 0.5073 | 4000 | 0.0064 | - |
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+ | 0.5136 | 4050 | 0.0096 | - |
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+ | 0.5200 | 4100 | 0.0063 | - |
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+ | 0.5263 | 4150 | 0.0083 | - |
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+ | 0.5327 | 4200 | 0.0067 | - |
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+ | 0.5390 | 4250 | 0.0095 | - |
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+ | 0.5453 | 4300 | 0.0097 | - |
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+ | 0.5517 | 4350 | 0.0057 | - |
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+ | 0.5580 | 4400 | 0.0101 | - |
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+ | 0.5644 | 4450 | 0.0101 | - |
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+ | 0.5707 | 4500 | 0.0043 | - |
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+ | 0.5770 | 4550 | 0.0099 | - |
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+ | 0.5834 | 4600 | 0.0091 | - |
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+ | 0.5897 | 4650 | 0.0065 | - |
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+ | 0.5961 | 4700 | 0.0071 | - |
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+ | 0.6024 | 4750 | 0.0035 | - |
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+ | 0.6088 | 4800 | 0.0088 | - |
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+ | 0.6151 | 4850 | 0.0079 | - |
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+ | 0.6214 | 4900 | 0.0094 | - |
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+ | 0.6278 | 4950 | 0.0105 | - |
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+ | 0.6341 | 5000 | 0.0091 | - |
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+ | 0.6405 | 5050 | 0.0109 | - |
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+ | 0.6468 | 5100 | 0.0081 | - |
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+ | 0.6531 | 5150 | 0.0087 | - |
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+ | 0.6595 | 5200 | 0.0091 | - |
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+ | 0.6658 | 5250 | 0.0071 | - |
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+ | 0.6722 | 5300 | 0.0072 | - |
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+ | 0.6785 | 5350 | 0.0084 | - |
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+ | 0.6848 | 5400 | 0.0099 | - |
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+ | 0.6912 | 5450 | 0.004 | - |
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+ | 0.6975 | 5500 | 0.0038 | - |
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+ | 0.7039 | 5550 | 0.0072 | - |
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+ | 0.7102 | 5600 | 0.0084 | - |
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+ | 0.7166 | 5650 | 0.004 | - |
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+ | 0.7229 | 5700 | 0.0077 | - |
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+ | 0.7292 | 5750 | 0.0066 | - |
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+ | 0.7356 | 5800 | 0.0043 | - |
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+ | 0.7419 | 5850 | 0.0054 | - |
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+ | 0.7483 | 5900 | 0.0107 | - |
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+ | 0.7546 | 5950 | 0.0046 | - |
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+ | 0.7609 | 6000 | 0.0075 | - |
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+ | 0.7673 | 6050 | 0.0106 | - |
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+ | 0.7736 | 6100 | 0.0063 | - |
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+ | 0.7800 | 6150 | 0.007 | - |
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+ | 0.7863 | 6200 | 0.0066 | - |
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+ | 0.7926 | 6250 | 0.0067 | - |
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+ | 0.7990 | 6300 | 0.0078 | - |
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+ | 0.8053 | 6350 | 0.0093 | - |
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+ | 0.8117 | 6400 | 0.0055 | - |
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+ | 0.8180 | 6450 | 0.0074 | - |
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+ | 0.8244 | 6500 | 0.0115 | - |
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+ | 0.8307 | 6550 | 0.0058 | - |
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+ | 0.8370 | 6600 | 0.005 | - |
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+ | 0.8434 | 6650 | 0.007 | - |
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+ | 0.8497 | 6700 | 0.0053 | - |
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+ | 0.8561 | 6750 | 0.0086 | - |
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+ | 0.8624 | 6800 | 0.0054 | - |
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+ | 0.8687 | 6850 | 0.0055 | - |
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+ | 0.8751 | 6900 | 0.006 | - |
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+ | 0.8814 | 6950 | 0.0068 | - |
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+ | 0.8878 | 7000 | 0.0103 | - |
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+ | 0.8941 | 7050 | 0.0054 | - |
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+ | 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
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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
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322
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323
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324
+ | 1.2365 | 9750 | 0.0055 | - |
325
+ | 1.2429 | 9800 | 0.0064 | - |
326
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327
+ | 1.2555 | 9900 | 0.0086 | - |
328
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329
+ | 1.2682 | 10000 | 0.0047 | - |
330
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331
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332
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333
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334
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335
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336
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337
+ | 1.3190 | 10400 | 0.006 | - |
338
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339
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340
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341
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342
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343
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344
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345
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346
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347
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348
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349
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350
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351
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352
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353
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354
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355
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356
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357
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358
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359
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360
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361
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362
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363
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364
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365
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366
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367
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368
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369
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370
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371
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372
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373
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374
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376
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377
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378
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379
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380
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381
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382
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383
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384
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385
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386
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387
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388
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389
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390
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391
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392
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393
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394
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395
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396
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397
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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411
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412
+ | 1.7945 | 14150 | 0.0051 | - |
413
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414
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415
+ | 1.8136 | 14300 | 0.0044 | - |
416
+ | 1.8199 | 14350 | 0.0071 | - |
417
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418
+ | 1.8326 | 14450 | 0.007 | - |
419
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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
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430
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431
+ | 1.9150 | 15100 | 0.0093 | - |
432
+ | 1.9214 | 15150 | 0.0036 | - |
433
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434
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435
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436
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437
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439
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441
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442
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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
 
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  "pad_token_id": 0,
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  "position_embedding_type": "absolute",
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config_sentence_transformers.json CHANGED
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config_setfit.json CHANGED
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+ "Hardware-/Robotikentwicklung",
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+ "Softwareentwicklung",
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