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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Vem pra Irenil em Paratinga, bonitão
- text: Salve Salve Senhor Governador JERÔNIMO RODRIGUES olhando para as TRADIÇÕES
- text: Parabéns meu Governador! O foguete 🚀 não para . Muitas realizações entregue
em 7 meses , muito trabalho .
- text: 👏👏👏
- text: Bom demais governador sobre o piso da enfermagem o que o senhor diz para nos
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9042553191489362
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. 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-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:---------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Positive | <ul><li>'Enfim,Bonfim 🥳🥳🥳🥳🥳'</li><li>'👏👏👏👏'</li><li>'Pequenas ações fazem sonhos realidades #OhBrabo 💙💙💙'</li></ul> |
| Negative | <ul><li>'@jeronimorodriguesba quando terá uma segunda convocação do concurso SECBA?'</li><li>'Cadê a MP do piso da enfermagem ministro'</li><li>'Sim !! A escola municipal aqui do bairro liberdade,30 crianças esperando até hoje as profissionais ADI para crianças que necessita acompanhamento..'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9043 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("Tarssio/modelo_setfit_politica_BA")
# Run inference
preds = model("👏👏👏")
```
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## 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
```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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