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: 2 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 |
---|---|
positive |
|
negative |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.89 |
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("DrGwin/setfit-paraphrase-mpnet-base-v2-sst2A")
# Run inference
preds = model("i had to look away - this was god awful . ")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 2 | 9.55 | 46 |
Label | Training Sample Count |
---|---|
negative | 40 |
positive | 60 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- 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: True
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0030 | 1 | 0.4181 | - |
0.1506 | 50 | 0.2514 | - |
0.3012 | 100 | 0.0932 | - |
0.4518 | 150 | 0.0029 | - |
0.6024 | 200 | 0.001 | - |
0.7530 | 250 | 0.0006 | - |
0.9036 | 300 | 0.0006 | - |
1.0 | 332 | - | 0.1722 |
1.0542 | 350 | 0.0014 | - |
1.2048 | 400 | 0.0004 | - |
1.3554 | 450 | 0.0004 | - |
1.5060 | 500 | 0.0095 | - |
1.6566 | 550 | 0.0003 | - |
1.8072 | 600 | 0.0003 | - |
1.9578 | 650 | 0.0003 | - |
2.0 | 664 | - | 0.1820 |
2.1084 | 700 | 0.0003 | - |
2.2590 | 750 | 0.0023 | - |
2.4096 | 800 | 0.0003 | - |
2.5602 | 850 | 0.0002 | - |
2.7108 | 900 | 0.0002 | - |
2.8614 | 950 | 0.0002 | - |
3.0 | 996 | - | 0.1970 |
3.0120 | 1000 | 0.0002 | - |
3.1627 | 1050 | 0.0003 | - |
3.3133 | 1100 | 0.0012 | - |
3.4639 | 1150 | 0.0002 | - |
3.6145 | 1200 | 0.0002 | - |
3.7651 | 1250 | 0.0003 | - |
3.9157 | 1300 | 0.001 | - |
4.0 | 1328 | - | 0.1810 |
Framework Versions
- Python: 3.11.11
- SetFit: 1.1.1
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Datasets: 3.3.2
- Tokenizers: 0.21.0
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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