SetFit with sentence-transformers/all-mpnet-base-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-mpnet-base-v2 as the Sentence Transformer embedding model. A MultiOutputClassifier 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/all-mpnet-base-v2
- Classification head: a MultiOutputClassifier instance
- Maximum Sequence Length: 384 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
Evaluation
Metrics
Label | Accuracy | Precision | Recall | F1 |
---|---|---|---|---|
all | 0.5 | 0.8 | 0.8889 | 0.8421 |
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("setfit_model_id")
# Run inference
preds = model("Well done on orchestrating such a seamless event!")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 6 | 10.75 | 16 |
Training Hyperparameters
- batch_size: (32, 2)
- num_epochs: (10, 10)
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0769 | 1 | 0.3115 | - |
1.0 | 13 | - | 0.1928 |
2.0 | 26 | - | 0.1831 |
3.0 | 39 | - | 0.1724 |
3.8462 | 50 | 0.08 | - |
4.0 | 52 | - | 0.1614 |
5.0 | 65 | - | 0.1695 |
6.0 | 78 | - | 0.1837 |
7.0 | 91 | - | 0.1904 |
7.6923 | 100 | 0.0364 | - |
8.0 | 104 | - | 0.1997 |
9.0 | 117 | - | 0.1994 |
10.0 | 130 | - | 0.1967 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.12.1
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.37.2
- PyTorch: 2.2.0
- Datasets: 2.19.1
- Tokenizers: 0.15.1
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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Model tree for lucienbaumgartner/PAG-annotation
Base model
sentence-transformers/all-mpnet-base-v2
Finetuned
this model
Evaluation results
- Accuracy on Unknowntest set self-reported0.500
- Precision on Unknowntest set self-reported0.800
- Recall on Unknowntest set self-reported0.889
- F1 on Unknowntest set self-reported0.842