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 SetFitHead 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 Sources
Model Labels
Label |
Examples |
1 |
- 'Gone are the days when they led the world in recession-busting'
- 'Who so mean that he will not himself be taxed, who so mindful of wealth that he will not favor increasing the popular taxes, in aid of these defective children?'
- 'That state has sixty-two counties and sixty cities … In addition there are 932 towns, 507 villages, and, at the last count, 9,600 school districts … Just try to render efficient service … amid the diffused identities and inevitable jealousies of, roughly, 11,000 independent administrative officers or boards!'
|
0 |
- 'Is this a warning of what’s to come?'
- 'This unique set of circumstances has brought PCL back into focus as the safe haven of choice for global players seeking somewhere to stash their cash.'
- 'Socialists believe that, if everyone cannot have something, no one shall.'
|
Evaluation
Metrics
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
model = SetFitModel.from_pretrained("SOUMYADEEPSAR/Setfit_subj_all-mpnet-base-v2")
preds = model("That can happen again.")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
3 |
36.5327 |
97 |
Label |
Training Sample Count |
0 |
100 |
1 |
114 |
Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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 |
0.0003 |
1 |
0.3816 |
- |
1.0 |
2902 |
0.0 |
0.2172 |
2.0 |
5804 |
0.0 |
0.2248 |
0.0003 |
1 |
0.5764 |
- |
0.0467 |
50 |
0.0009 |
- |
0.0935 |
100 |
0.0011 |
- |
0.1402 |
150 |
0.0001 |
- |
0.1869 |
200 |
0.0001 |
- |
0.2336 |
250 |
0.0001 |
- |
0.2804 |
300 |
0.0 |
- |
0.3271 |
350 |
0.0 |
- |
0.3738 |
400 |
0.0 |
- |
0.4206 |
450 |
0.0001 |
- |
0.4673 |
500 |
0.0 |
- |
0.5140 |
550 |
0.0 |
- |
0.5607 |
600 |
0.0 |
- |
0.6075 |
650 |
0.0 |
- |
0.6542 |
700 |
0.0 |
- |
0.7009 |
750 |
0.0 |
- |
0.7477 |
800 |
0.0 |
- |
0.7944 |
850 |
0.0 |
- |
0.8411 |
900 |
0.0 |
- |
0.8879 |
950 |
0.0001 |
- |
0.9346 |
1000 |
0.0 |
- |
0.9813 |
1050 |
0.0 |
- |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.1
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.1
- 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}
}