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fs-setfit-multilable-model

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
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Chernoffface commited on
Commit
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Add SetFit model

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Files changed (4) hide show
  1. README.md +125 -12
  2. config_setfit.json +2 -2
  3. model.safetensors +1 -1
  4. model_head.pkl +2 -2
README.md CHANGED
@@ -9,15 +9,35 @@ 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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- - text: The seminar offers a well-balanced mix of theory and case studies with a focus
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- on mitigation and adaptation within the context of climate change and economics.
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- - text: 'Juni Fachsprachliche Lehrwerke: Anspruche und Realitat 01.'
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- - text: Grundlagen der Fachdidaktik Padagogik
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- - text: Probleme losen lernen
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- - text: Im Rahmen dieses Praxisseminars sollen Studierende einen fundierten Einblick
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- in Ablaufe der Fragebogenkonstruktion, Datenerhebung und Datenanalyse erhalten.
 
 
 
 
 
 
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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-mpnet-base-v2
@@ -47,6 +67,13 @@ The model has been trained using an efficient few-shot learning technique that i
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  - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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  - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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  ## Uses
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  ### Direct Use for Inference
@@ -65,7 +92,7 @@ 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("Probleme losen lernen")
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  ```
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  <!--
@@ -97,7 +124,7 @@ preds = model("Probleme losen lernen")
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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 | 2 | 18.2917 | 74 |
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  ### Training Hyperparameters
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  - batch_size: (16, 16)
@@ -121,9 +148,95 @@ preds = model("Probleme losen lernen")
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  ### Training Results
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  | Epoch | Step | Training Loss | Validation Loss |
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  |:------:|:----:|:-------------:|:---------------:|
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- | 0.0083 | 1 | 0.1978 | - |
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- | 0.4167 | 50 | 0.1134 | - |
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- | 0.8333 | 100 | 0.0439 | - |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: In diesem Seminar an unserer Hochschule erhalten Sie eine umfassende Einführung
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+ in die SCRUM-Methode und ihre Anwendung im agilen Arbeiten.
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+ - text: 'Dazu untersuchen wir grundlegende Programmierkonzepte: wozu sie dienen und
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+ wie sie funtionieren.'
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+ - text: 'Modeling plays a very important role in reconstructing (as far as possible)
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+ the complete and complex picture of the surroundings water systems and offers
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+ a unique way to predict behavior of such multifaceted systems.
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+
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+
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+ '
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+ - text: Build an IT-based artifact using recent IoT, robotics, and applied AI technologies.
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+ - text: Sie bietet eine Plattform, um neuartige auf Blockchain Technologie basierende
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+ Anwendungen auf ihre Umsetzbarkeit und Sinnhaftigkeit zu überprüfen.
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  pipeline_tag: text-classification
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  inference: false
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+ model-index:
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+ - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.5225165562913907
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+ name: Accuracy
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  ---
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  # SetFit with sentence-transformers/paraphrase-mpnet-base-v2
 
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  - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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  - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.5225 |
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+
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  ## Uses
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  ### Direct Use for Inference
 
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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("Build an IT-based artifact using recent IoT, robotics, and applied AI technologies.")
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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 | 12.9119 | 131 |
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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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+ | 0.0045 | 1 | 0.1871 | - |
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+ | 0.2273 | 50 | 0.1771 | - |
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+ | 0.4545 | 100 | 0.0978 | - |
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+ | 0.6818 | 150 | 0.0675 | - |
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+ | 0.9091 | 200 | 0.0479 | - |
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+ | 0.0045 | 1 | 0.0274 | - |
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+ | 0.2273 | 50 | 0.0426 | - |
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+ | 0.4545 | 100 | 0.0346 | - |
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+ | 0.6818 | 150 | 0.0375 | - |
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+ | 0.9091 | 200 | 0.0322 | - |
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+ | 0.0003 | 1 | 0.2265 | - |
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+ | 0.0127 | 50 | 0.1776 | - |
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+ | 0.0254 | 100 | 0.1138 | - |
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+ | 0.0380 | 150 | 0.0816 | - |
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+ | 0.0507 | 200 | 0.055 | - |
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+ | 0.0634 | 250 | 0.04 | - |
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+ | 0.0761 | 300 | 0.031 | - |
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+ | 0.0888 | 350 | 0.0227 | - |
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+ | 0.1014 | 400 | 0.0222 | - |
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+ | 0.1141 | 450 | 0.0206 | - |
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+ | 0.1268 | 500 | 0.0191 | - |
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+ | 0.1395 | 550 | 0.0239 | - |
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+ | 0.1522 | 600 | 0.0181 | - |
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+ | 0.1648 | 650 | 0.0263 | - |
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+ | 0.1775 | 700 | 0.0175 | - |
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+ | 0.1902 | 750 | 0.0157 | - |
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+ | 0.2029 | 800 | 0.0178 | - |
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+ | 0.2156 | 850 | 0.0189 | - |
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+ | 0.2283 | 900 | 0.0121 | - |
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+ | 0.2409 | 950 | 0.0203 | - |
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+ | 0.2536 | 1000 | 0.0206 | - |
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+ | 0.2663 | 1050 | 0.013 | - |
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+ | 0.2790 | 1100 | 0.0167 | - |
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+ | 0.2917 | 1150 | 0.0143 | - |
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+ | 0.3043 | 1200 | 0.0151 | - |
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+ | 0.3170 | 1250 | 0.0135 | - |
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+ | 0.3297 | 1300 | 0.0109 | - |
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+ | 0.3424 | 1350 | 0.011 | - |
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+ | 0.3551 | 1400 | 0.0165 | - |
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+ | 0.3677 | 1450 | 0.0133 | - |
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+ | 0.3804 | 1500 | 0.0197 | - |
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+ | 0.3931 | 1550 | 0.012 | - |
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+ | 0.4058 | 1600 | 0.0137 | - |
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+ | 0.4185 | 1650 | 0.0163 | - |
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+ | 0.4311 | 1700 | 0.0089 | - |
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+ | 0.4438 | 1750 | 0.0117 | - |
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+ | 0.4565 | 1800 | 0.0139 | - |
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+ | 0.4692 | 1850 | 0.01 | - |
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+ | 0.4819 | 1900 | 0.0142 | - |
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+ | 0.4945 | 1950 | 0.0094 | - |
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+ | 0.5072 | 2000 | 0.0111 | - |
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+ | 0.5199 | 2050 | 0.0119 | - |
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+ | 0.5326 | 2100 | 0.0089 | - |
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+ | 0.5453 | 2150 | 0.0087 | - |
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+ | 0.5580 | 2200 | 0.007 | - |
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+ | 0.5706 | 2250 | 0.0081 | - |
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+ | 0.5833 | 2300 | 0.016 | - |
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+ | 0.5960 | 2350 | 0.0105 | - |
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+ | 0.6087 | 2400 | 0.0124 | - |
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+ | 0.6214 | 2450 | 0.0059 | - |
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+ | 0.6340 | 2500 | 0.01 | - |
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+ | 0.6467 | 2550 | 0.0054 | - |
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+ | 0.6594 | 2600 | 0.0059 | - |
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+ | 0.6721 | 2650 | 0.0091 | - |
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+ | 0.6848 | 2700 | 0.0133 | - |
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+ | 0.6974 | 2750 | 0.0065 | - |
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+ | 0.7101 | 2800 | 0.0081 | - |
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+ | 0.7228 | 2850 | 0.0078 | - |
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+ | 0.7355 | 2900 | 0.0079 | - |
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+ | 0.7482 | 2950 | 0.01 | - |
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+ | 0.7608 | 3000 | 0.0083 | - |
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+ | 0.7735 | 3050 | 0.0114 | - |
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+ | 0.7862 | 3100 | 0.0076 | - |
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+ | 0.7989 | 3150 | 0.0083 | - |
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+ | 0.8116 | 3200 | 0.0097 | - |
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+ | 0.8242 | 3250 | 0.0077 | - |
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+ | 0.8369 | 3300 | 0.0066 | - |
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+ | 0.8496 | 3350 | 0.0113 | - |
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+ | 0.8623 | 3400 | 0.0065 | - |
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+ | 0.8750 | 3450 | 0.01 | - |
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+ | 0.8876 | 3500 | 0.0098 | - |
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+ | 0.9003 | 3550 | 0.0115 | - |
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+ | 0.9130 | 3600 | 0.0073 | - |
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+ | 0.9257 | 3650 | 0.0104 | - |
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+ | 0.9384 | 3700 | 0.0059 | - |
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+ | 0.9511 | 3750 | 0.006 | - |
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+ | 0.9637 | 3800 | 0.0071 | - |
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+ | 0.9764 | 3850 | 0.0061 | - |
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+ | 0.9891 | 3900 | 0.0076 | - |
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  ### Framework Versions
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  - Python: 3.12.3
config_setfit.json CHANGED
@@ -1,4 +1,4 @@
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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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+ "labels": null,
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+ "normalize_embeddings": false
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