SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a SetFit model trained on the Kevinger/hub-report-dataset dataset that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. 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
- Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2
- Classification head: a OneVsRestClassifier instance
- Maximum Sequence Length: 512 tokens
- Training Dataset: Kevinger/hub-report-dataset
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 |
---|---|
all | 0.6529 |
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("Kevinger/setfit-hub-multilabel-example")
# Run inference
preds = model("LEVERETT — Dakin Humane Society announced Wednesday that it has sold its former animal shelter at 63 Montague Road in Leverett to Better Together Dog Rescue.
The news release didn’t include a sales price for the 3,480-square-foot building on 5 acres of land.
But records at the Franklin County Registry of Deeds show the sale was for $575,000.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 53 | 386.3906 | 2161 |
Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 75
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0008 | 1 | 0.1304 | - |
0.0417 | 50 | 0.1596 | - |
0.0833 | 100 | 0.132 | - |
0.125 | 150 | 0.0064 | - |
0.1667 | 200 | 0.0017 | - |
0.2083 | 250 | 0.0004 | - |
0.25 | 300 | 0.0001 | - |
0.2917 | 350 | 0.0002 | - |
0.3333 | 400 | 0.0003 | - |
0.375 | 450 | 0.0002 | - |
0.4167 | 500 | 0.0001 | - |
0.4583 | 550 | 0.0002 | - |
0.5 | 600 | 0.0002 | - |
0.5417 | 650 | 0.0002 | - |
0.5833 | 700 | 0.0001 | - |
0.625 | 750 | 0.0001 | - |
0.6667 | 800 | 0.0001 | - |
0.7083 | 850 | 0.0001 | - |
0.75 | 900 | 0.0 | - |
0.7917 | 950 | 0.0001 | - |
0.8333 | 1000 | 0.0001 | - |
0.875 | 1050 | 0.0001 | - |
0.9167 | 1100 | 0.0001 | - |
0.9583 | 1150 | 0.0 | - |
1.0 | 1200 | 0.0001 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.0
- Tokenizers: 0.15.2
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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Dataset used to train Kevinger/setfit-hub-multilabel-example
Evaluation results
- Accuracy on Kevinger/hub-report-datasettest set self-reported0.653