SetFit
This is a SetFit model that can be used for Text Classification. A OneVsRestClassifier 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
- Classification head: a OneVsRestClassifier instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 3 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 |
---|---|
2 |
|
1 |
|
0 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.9987 |
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("anismahmahi/doubt_repetition_with_noPropaganda_multiclass_SetFit")
# Run inference
preds = model("The Twitter suspension caught me by surprise.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 1 | 20.4272 | 109 |
Label | Training Sample Count |
---|---|
0 | 131 |
1 | 129 |
2 | 2479 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 5
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0006 | 1 | 0.3869 | - |
0.0292 | 50 | 0.3352 | - |
0.0584 | 100 | 0.2235 | - |
0.0876 | 150 | 0.1518 | - |
0.1168 | 200 | 0.1967 | - |
0.1460 | 250 | 0.1615 | - |
0.1752 | 300 | 0.1123 | - |
0.2044 | 350 | 0.1493 | - |
0.2336 | 400 | 0.0039 | - |
0.2629 | 450 | 0.0269 | - |
0.2921 | 500 | 0.0024 | - |
0.3213 | 550 | 0.0072 | - |
0.3505 | 600 | 0.0649 | - |
0.3797 | 650 | 0.0005 | - |
0.4089 | 700 | 0.0008 | - |
0.4381 | 750 | 0.0041 | - |
0.4673 | 800 | 0.0009 | - |
0.4965 | 850 | 0.0004 | - |
0.5257 | 900 | 0.0013 | - |
0.5549 | 950 | 0.0013 | - |
0.5841 | 1000 | 0.0066 | - |
0.6133 | 1050 | 0.0355 | - |
0.6425 | 1100 | 0.0004 | - |
0.6717 | 1150 | 0.0013 | - |
0.7009 | 1200 | 0.0003 | - |
0.7301 | 1250 | 0.0002 | - |
0.7593 | 1300 | 0.0008 | - |
0.7886 | 1350 | 0.0002 | - |
0.8178 | 1400 | 0.0002 | - |
0.8470 | 1450 | 0.0004 | - |
0.8762 | 1500 | 0.1193 | - |
0.9054 | 1550 | 0.0002 | - |
0.9346 | 1600 | 0.0002 | - |
0.9638 | 1650 | 0.0002 | - |
0.9930 | 1700 | 0.0002 | - |
1.0 | 1712 | - | 0.0073 |
1.0222 | 1750 | 0.0002 | - |
1.0514 | 1800 | 0.0006 | - |
1.0806 | 1850 | 0.0005 | - |
1.1098 | 1900 | 0.0001 | - |
1.1390 | 1950 | 0.0012 | - |
1.1682 | 2000 | 0.0003 | - |
1.1974 | 2050 | 0.0344 | - |
1.2266 | 2100 | 0.0038 | - |
1.2558 | 2150 | 0.0001 | - |
1.2850 | 2200 | 0.0003 | - |
1.3143 | 2250 | 0.0114 | - |
1.3435 | 2300 | 0.0001 | - |
1.3727 | 2350 | 0.0001 | - |
1.4019 | 2400 | 0.0001 | - |
1.4311 | 2450 | 0.0001 | - |
1.4603 | 2500 | 0.0005 | - |
1.4895 | 2550 | 0.0086 | - |
1.5187 | 2600 | 0.0001 | - |
1.5479 | 2650 | 0.0002 | - |
1.5771 | 2700 | 0.0001 | - |
1.6063 | 2750 | 0.0002 | - |
1.6355 | 2800 | 0.0001 | - |
1.6647 | 2850 | 0.0001 | - |
1.6939 | 2900 | 0.0001 | - |
1.7231 | 2950 | 0.0001 | - |
1.7523 | 3000 | 0.0001 | - |
1.7815 | 3050 | 0.0001 | - |
1.8107 | 3100 | 0.0 | - |
1.8400 | 3150 | 0.0001 | - |
1.8692 | 3200 | 0.0001 | - |
1.8984 | 3250 | 0.0001 | - |
1.9276 | 3300 | 0.0 | - |
1.9568 | 3350 | 0.0001 | - |
1.9860 | 3400 | 0.0002 | - |
2.0 | 3424 | - | 0.0053 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.0
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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