---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
metrics:
- accuracy
widget:
- text: Comment rédiger un bon CV?
- text: What is the address of Microsoft's headquarters?
- text: Where is the nearest gas station?
- text: How to create a mobile application?
- text: Comment calculer le retour sur investissement (ROI)?
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 1.0
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-mpnet-base-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-mpnet-base-v2](https://huggingface.co./sentence-transformers/paraphrase-mpnet-base-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-mpnet-base-v2](https://huggingface.co./sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 6 classes
### 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 |
|:----------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| very_semantic_queries |
- 'Quels sont les principes fondamentaux du corps humain?'
- "Comment améliorer l'efficacité énergétique dans les bâtiments?"
- 'Combien de calories dans une pomme?'
|
| very_lexical | - "Quelle est la capitale de l'Italie?"
- "Qui est l'auteur de '1984'?"
- 'What is the current unemployment rate in France?'
|
| semantic_queries | - "Quels sont les avantages de l'apprentissage machine dans le secteur de la santé?"
- 'Comment puis-je optimiser les performances de mon site web?'
- 'What are the main challenges in cybersecurity?'
|
| lexical | - 'Quel est le numéro de téléphone du service client ou du customer suport?'
- 'How can I reset my user password?'
- 'What is the zip code for New York?'
|
| lexical_queries | - 'Comment fonctionne la blockchain?'
|
| lexical_query | - 'Who won the Nobel Peace Prize in 2021?'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 1.0 |
## 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("yaniseuranova/setfit-paraphrase-mpnet-base-v2-sst2")
# Run inference
preds = model("Comment rédiger un bon CV?")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 4 | 7.1667 | 13 |
| Label | Training Sample Count |
|:----------------------|:----------------------|
| very_semantic_queries | 16 |
| semantic_queries | 18 |
| lexical_queries | 1 |
| very_lexical | 15 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0059 | 1 | 0.4006 | - |
| 0.2941 | 50 | 0.1896 | - |
| 0.5882 | 100 | 0.052 | - |
| 0.8824 | 150 | 0.0042 | - |
| 1.0 | 170 | - | 0.0023 |
| 1.1765 | 200 | 0.0011 | - |
| 1.4706 | 250 | 0.0006 | - |
| 1.7647 | 300 | 0.0007 | - |
| 2.0 | 340 | - | 0.0003 |
| 2.0588 | 350 | 0.0004 | - |
| 2.3529 | 400 | 0.0004 | - |
| 2.6471 | 450 | 0.0004 | - |
| 2.9412 | 500 | 0.0009 | - |
| 3.0 | 510 | - | 0.0003 |
| 3.2353 | 550 | 0.0003 | - |
| 3.5294 | 600 | 0.0004 | - |
| 3.8235 | 650 | 0.0003 | - |
| **4.0** | **680** | **-** | **0.0002** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.6.1
- Transformers: 4.39.0
- PyTorch: 2.3.0+cu121
- Datasets: 2.18.0
- Tokenizers: 0.15.2
## 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}
}
```