Add SetFit model
Browse files- README.md +92 -124
- model.safetensors +1 -1
- model_head.pkl +1 -1
README.md
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@@ -9,35 +9,20 @@ 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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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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@@ -67,13 +52,6 @@ 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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## Evaluation
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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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## Uses
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### Direct Use for Inference
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@@ -92,7 +70,7 @@ from setfit import SetFitModel
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("Chernoffface/fs-setfit-multilable-model")
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# Run inference
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preds = model("
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```
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<!--
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:------:|:----:|:-------------:|:---------------:|
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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
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metrics:
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- accuracy
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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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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
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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
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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 Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:------:|:----:|:-------------:|:---------------:|
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| 0.0003 | 1 | 0.2958 | - |
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| 0.0127 | 50 | 0.2471 | - |
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| 0.0254 | 100 | 0.1602 | - |
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| 0.0380 | 150 | 0.0884 | - |
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| 0.0507 | 200 | 0.056 | - |
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### Framework Versions
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- Python: 3.12.3
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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 sha256:811d8ab058fabb812582c131b009b30efe27fc347ec28ed8ac66ca2d4aa545a3
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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 72196
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version https://git-lfs.github.com/spec/v1
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oid sha256:94bf01f7f5276d5ebade043aca79182edc77166ec3ecc2c5487306d4f23ccb25
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size 72196
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