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---
base_model: projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Esteu tots millor callats, no us puc ni veure!
- text: Puc canviar el meu idioma preferit?
- text: No serveixes per res, és un sistema de merda!
- text: Com va tot, com estàs? Quin és l'objecte de la convocatòria de subvencions
de l'Ajuntament de Sant Boi de Llobregat?
- text: Quin és el millor lloc per comprar un regal?
inference: true
---
# SetFit with projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base) 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:** [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base)
- **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:** 3 classes
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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 |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1 | <ul><li>'Sou uns fills de puta, no valen res, et feu fora, sou un inútil!'</li><li>'Quin és el seu propòsit?'</li><li>"Aquest text és Ofensiu o fora del domini per a un cercador de tràmits d'un ajuntament"</li></ul> |
| 2 | <ul><li>'Ei, què tal? Com va tot?'</li><li>'Bona tarda! Què tal?'</li><li>'Què tal, com va?'</li></ul> |
| 0 | <ul><li>"Hola Necessito saber si la modificació no substancial que faré a la meva activitat sotmesa a comunicació prèvia ambiental ha de ser comunicada a l'Ajuntament i no ha de figurar a les actes de control periòdic"</li><li>"Quin és l'objectiu de la Llei 11/2009?"</li><li>'Quin és el benefici de la matrícula?'</li></ul> |
## 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("adriansanz/gret6")
# Run inference
preds = model("Puc canviar el meu idioma preferit?")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 1 | 9.3443 | 36 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 70 |
| 1 | 71 |
| 2 | 71 |
### Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (3, 3)
- 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
- l2_weight: 0.01
- seed: 42
- evaluation_strategy: epoch
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0021 | 1 | 0.1891 | - |
| 0.1066 | 50 | 0.1719 | - |
| 0.2132 | 100 | 0.0455 | - |
| 0.3198 | 150 | 0.0013 | - |
| 0.4264 | 200 | 0.0004 | - |
| 0.5330 | 250 | 0.0002 | - |
| 0.6397 | 300 | 0.0002 | - |
| 0.7463 | 350 | 0.0001 | - |
| 0.8529 | 400 | 0.0001 | - |
| 0.9595 | 450 | 0.0001 | - |
| 1.0 | 469 | - | 0.0062 |
| 1.0661 | 500 | 0.0001 | - |
| 1.1727 | 550 | 0.0001 | - |
| 1.2793 | 600 | 0.0001 | - |
| 1.3859 | 650 | 0.0001 | - |
| 1.4925 | 700 | 0.0001 | - |
| 1.5991 | 750 | 0.0001 | - |
| 1.7058 | 800 | 0.0001 | - |
| 1.8124 | 850 | 0.0001 | - |
| 1.9190 | 900 | 0.0001 | - |
| 2.0 | 938 | - | 0.0042 |
| 2.0256 | 950 | 0.0 | - |
| 2.1322 | 1000 | 0.0 | - |
| 2.2388 | 1050 | 0.0 | - |
| 2.3454 | 1100 | 0.0 | - |
| 2.4520 | 1150 | 0.0 | - |
| 2.5586 | 1200 | 0.0 | - |
| 2.6652 | 1250 | 0.0 | - |
| 2.7719 | 1300 | 0.0 | - |
| 2.8785 | 1350 | 0.0 | - |
| 2.9851 | 1400 | 0.0 | - |
| 3.0 | 1407 | - | 0.0034 |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.2.1
- Transformers: 4.42.2
- PyTorch: 2.5.0+cu121
- Datasets: 3.1.0
- Tokenizers: 0.19.1
## 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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