SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-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 Sources
Model Labels
Label |
Examples |
Positive |
- 'Enfim,Bonfim 🥳🥳🥳🥳🥳'
- '👏👏👏👏'
- 'Pequenas ações fazem sonhos realidades #OhBrabo 💙💙💙'
|
Negative |
- '@jeronimorodriguesba quando terá uma segunda convocação do concurso SECBA?'
- 'Cadê a MP do piso da enfermagem ministro'
- 'Sim !! A escola municipal aqui do bairro liberdade,30 crianças esperando até hoje as profissionais ADI para crianças que necessita acompanhamento..'
|
Evaluation
Metrics
Label |
Accuracy |
all |
0.9043 |
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("Tarssio/modelo_setfit_politica_BA")
preds = model("👏👏👏")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
1 |
19.4813 |
313 |
Label |
Training Sample Count |
Negative |
175 |
Positive |
199 |
Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (4, 4)
- 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 |
0.0011 |
1 |
0.3616 |
- |
0.0535 |
50 |
0.3129 |
- |
0.1070 |
100 |
0.2912 |
- |
0.1604 |
150 |
0.191 |
- |
0.2139 |
200 |
0.0907 |
- |
0.2674 |
250 |
0.0086 |
- |
0.3209 |
300 |
0.0042 |
- |
0.3743 |
350 |
0.0161 |
- |
0.4278 |
400 |
0.0007 |
- |
0.4813 |
450 |
0.0403 |
- |
0.5348 |
500 |
0.0055 |
- |
0.5882 |
550 |
0.0057 |
- |
0.6417 |
600 |
0.0002 |
- |
0.6952 |
650 |
0.0002 |
- |
0.7487 |
700 |
0.0 |
- |
0.8021 |
750 |
0.0026 |
- |
0.8556 |
800 |
0.0002 |
- |
0.9091 |
850 |
0.0002 |
- |
0.9626 |
900 |
0.0004 |
- |
1.0 |
935 |
- |
0.1724 |
1.0160 |
950 |
0.0001 |
- |
1.0695 |
1000 |
0.0006 |
- |
1.1230 |
1050 |
0.0001 |
- |
1.1765 |
1100 |
0.0008 |
- |
1.2299 |
1150 |
0.0002 |
- |
1.2834 |
1200 |
0.0001 |
- |
1.3369 |
1250 |
0.0002 |
- |
1.3904 |
1300 |
0.0002 |
- |
1.4439 |
1350 |
0.0002 |
- |
1.4973 |
1400 |
0.0002 |
- |
1.5508 |
1450 |
0.0 |
- |
1.6043 |
1500 |
0.0002 |
- |
1.6578 |
1550 |
0.2178 |
- |
1.7112 |
1600 |
0.0002 |
- |
1.7647 |
1650 |
0.0001 |
- |
1.8182 |
1700 |
0.0001 |
- |
1.8717 |
1750 |
0.0003 |
- |
1.9251 |
1800 |
0.0359 |
- |
1.9786 |
1850 |
0.0001 |
- |
2.0 |
1870 |
- |
0.1601 |
2.0321 |
1900 |
0.0001 |
- |
2.0856 |
1950 |
0.0002 |
- |
2.1390 |
2000 |
0.0001 |
- |
2.1925 |
2050 |
0.0001 |
- |
2.2460 |
2100 |
0.0002 |
- |
2.2995 |
2150 |
0.0002 |
- |
2.3529 |
2200 |
0.0003 |
- |
2.4064 |
2250 |
0.0001 |
- |
2.4599 |
2300 |
0.0002 |
- |
2.5134 |
2350 |
0.0001 |
- |
2.5668 |
2400 |
0.0 |
- |
2.6203 |
2450 |
0.0001 |
- |
2.6738 |
2500 |
0.0 |
- |
2.7273 |
2550 |
0.0001 |
- |
2.7807 |
2600 |
0.0001 |
- |
2.8342 |
2650 |
0.0 |
- |
2.8877 |
2700 |
0.0 |
- |
2.9412 |
2750 |
0.0 |
- |
2.9947 |
2800 |
0.0001 |
- |
3.0 |
2805 |
- |
0.1568 |
3.0481 |
2850 |
0.0001 |
- |
3.1016 |
2900 |
0.0001 |
- |
3.1551 |
2950 |
0.0001 |
- |
3.2086 |
3000 |
0.0001 |
- |
3.2620 |
3050 |
0.0001 |
- |
3.3155 |
3100 |
0.0045 |
- |
3.3690 |
3150 |
0.0 |
- |
3.4225 |
3200 |
0.0001 |
- |
3.4759 |
3250 |
0.0002 |
- |
3.5294 |
3300 |
0.0 |
- |
3.5829 |
3350 |
0.0002 |
- |
3.6364 |
3400 |
0.0 |
- |
3.6898 |
3450 |
0.0 |
- |
3.7433 |
3500 |
0.0002 |
- |
3.7968 |
3550 |
0.0 |
- |
3.8503 |
3600 |
0.0 |
- |
3.9037 |
3650 |
0.0005 |
- |
3.9572 |
3700 |
0.0001 |
- |
4.0 |
3740 |
- |
0.1574 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- 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}
}