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tmp_cv_model_2025_09_01_0
tmp-org/tmp_cv_model_2025_09_01_0
tmp_cv_model_2025_09_01_0 is a text classification model from tmp-org. Use it when you need a label for a piece of text. It is set up for setfit.
This is a SetFit model trained on the tmp-org/lidl-dataset-ctx-1 dataset that can be used for Text Classification. This SetFit model uses Alibaba-NLP/gte-multilingual-base as the Sentence Transformer embedding model.…
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At a glance
- Task
- Text Classification
- Library
- setfit
- Model type
- new
- Access
- Public
- Created
- Feb 23, 2026
- Updated
- Feb 23, 2026
- SHA
- a2fe7673
Try a prompt
Base models
Datasets
Model card
From the Hugging Face model README
SetFit with Alibaba-NLP/gte-multilingual-base
This is a SetFit model trained on the tmp-org/lidl-dataset-ctx-1 dataset that can be used for Text Classification. This SetFit model uses Alibaba-NLP/gte-multilingual-base as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: Alibaba-NLP/gte-multilingual-base
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 8192 tokens
- Number of Classes: 33 classes
- Training Dataset: tmp-org/lidl-dataset-ctx-1
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
| Label | Examples |
|---|---|
| Home_Loading | <ul><li>'Home () [TextView |
| Home_Home | <ul><li>'Gewinnspiel () [TextView |
| Other_Gewinnspiel | <ul><li>' (0 ungesendete Lose) [View |
| Other_Rubbellos | <ul><li>'Sofortgewinne () [TextView |
The model card is truncated. Read the rest on Hugging Face.
- Task
- Text Classification
- Library
- setfit
- Type
- new
- Created
- Feb 23, 2026
- Updated
- Feb 23, 2026