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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
- precision
- recall
- f1
widget:
- text: 'Some women are more alive than others. '
- text: ': Session'
- text: '. Manage your cookie preferences:'
- text: Download for Mac
- text: HTI Haiti
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
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: 0.8625
name: Accuracy
- type: precision
value: 0.825
name: Precision
- type: recall
value: 0.8918918918918919
name: Recall
- type: f1
value: 0.8571428571428571
name: F1
---
# 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:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/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 |
|:------|:-------------------------------------------------------------------------------------------------------------------|
| True | <ul><li>'715-462-3626 Open Daily @ 7am '</li><li>': HTTP'</li><li>'Zmywarka modutowa. Pasuje wszedzie. '</li></ul> |
| False | <ul><li>'(retencja w dniach: 180)'</li><li>'Bosnia and Herzegovina'</li><li>'Arruda dos Vinhos'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy | Precision | Recall | F1 |
|:--------|:---------|:----------|:-------|:-------|
| **all** | 0.8625 | 0.825 | 0.8919 | 0.8571 |
## 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("setfit_model_id")
# Run inference
preds = model(": Session")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 1 | 8.5094 | 146 |
| Label | Training Sample Count |
|:------|:----------------------|
| False | 157 |
| True | 163 |
### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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
- run_name: PG-OCR-test-2
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0013 | 1 | 0.2507 | - |
| 0.0625 | 50 | 0.0961 | - |
| 0.125 | 100 | 0.2456 | - |
| 0.1875 | 150 | 0.0709 | - |
| 0.25 | 200 | 0.0213 | - |
| 0.3125 | 250 | 0.0193 | - |
| 0.375 | 300 | 0.0827 | - |
| 0.4375 | 350 | 0.015 | - |
| 0.5 | 400 | 0.0039 | - |
| 0.5625 | 450 | 0.0087 | - |
| 0.625 | 500 | 0.0064 | - |
| 0.6875 | 550 | 0.001 | - |
| 0.75 | 600 | 0.0236 | - |
| 0.8125 | 650 | 0.0553 | - |
| 0.875 | 700 | 0.0661 | - |
| 0.9375 | 750 | 0.0006 | - |
| 1.0 | 800 | 0.0604 | - |
### Framework Versions
- Python: 3.11.0
- SetFit: 1.0.3
- Sentence Transformers: 2.3.0
- Transformers: 4.37.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.1
## 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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