SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 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: sentence-transformers/paraphrase-mpnet-base-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 6 classes
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 |
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3 |
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6 |
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1 |
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2 |
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5 |
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4 |
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Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.8828 |
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("Chernoffface/fs-setfit-model")
# Run inference
preds = model("Proseminar: Mathematischer Programmierkurs")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 1 | 32.4872 | 368 |
Label | Training Sample Count |
---|---|
1 | 69 |
2 | 7 |
3 | 200 |
4 | 1 |
5 | 9 |
6 | 223 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0016 | 1 | 0.2519 | - |
0.0785 | 50 | 0.2065 | - |
0.1570 | 100 | 0.1206 | - |
0.2355 | 150 | 0.062 | - |
0.3140 | 200 | 0.0311 | - |
0.3925 | 250 | 0.0124 | - |
0.4710 | 300 | 0.0085 | - |
0.5495 | 350 | 0.0022 | - |
0.6279 | 400 | 0.0043 | - |
0.7064 | 450 | 0.001 | - |
0.7849 | 500 | 0.0012 | - |
0.8634 | 550 | 0.0009 | - |
0.9419 | 600 | 0.0012 | - |
Framework Versions
- Python: 3.12.3
- SetFit: 1.1.0
- Sentence Transformers: 3.0.0
- Transformers: 4.43.1
- PyTorch: 2.3.1+cu121
- Datasets: 2.20.0
- Tokenizers: 0.19.1
Citation
BibTeX
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
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