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setfit-all-mpnet-base-v2-non-augmented_dataset-133-shot-just_transition-v1.4.1

Text Classification
setfit
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sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
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setfit-all-mpnet-base-v2-non-augmented_dataset-133-shot-just_transition-v1.4.1 / README.md
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---
base_model: sentence-transformers/all-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: The transformation of production systems towards more sustainable models must
be accompanied by social policies aimed at reducing inequalities and promoting
social cohesion.
- text: The protection of protected areas and nature reserves is essential to conserve
biodiversity and preserve wild habitats.
- text: Immigration and asylum policies are at the center of political debate, with
divergent opinions on how to manage migratory flows and the integration of new
arrivals.
- text: The transition towards renewable energy sources requires a concrete commitment
to combat the climate emergency and guarantee a sustainable future for generations
to come.
- text: Promoting social justice and the redistribution of resources is essential
to ensure a fair transition to a sustainable economy.
inference: true
model-index:
- name: SetFit with sentence-transformers/all-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9375
name: Accuracy
---
# SetFit with sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 384 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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1 | <ul><li>'Does that mean that a fair transition must be ensured through taxation, including with a capital tax for the most wealthy?'</li><li>'In fact, there are alternatives, there is a need for motivation to create reasonable parallel opportunities for job creation during a gradual transition.'</li><li>'We show that it is possible to combine ecological sustainability with welfare, justice and development.'</li></ul> |
| 0 | <ul><li>'As a representative of the Center Party, I am convinced that a transition to a fossil-independent transport sector and the fleet of vehicles is both necessary and possible.'</li><li>'Natural solutions supporting the green digital transition aim to mitigate and adapt to climate change.'</li><li>'Such a project is at the heart of the ecological transition: the Government, as well as parliamentarians and all the actors involved in this concession, have shown their commitment to this model, which is very innovative, and their ambition to accompany projects at the crossroads of these various issues.'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9375 |
## 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("Francesco-A/setfit-all-mpnet-base-v2-non-augmented_dataset-133-shot-just_transition-v1.4.1")
# Run inference
preds = model("The protection of protected areas and nature reserves is essential to conserve biodiversity and preserve wild habitats.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 5 | 31.4436 | 120 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 133 |
| 1 | 133 |
### Training Hyperparameters
- batch_size: (16, 16)
- 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: 1234
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0009 | 1 | 0.2933 | - |
| 0.0449 | 50 | 0.2605 | - |
| 0.0898 | 100 | 0.2551 | - |
| 0.1346 | 150 | 0.2467 | - |
| 0.1795 | 200 | 0.233 | - |
| 0.2244 | 250 | 0.1117 | - |
| 0.2693 | 300 | 0.0049 | - |
| 0.3142 | 350 | 0.0007 | - |
| 0.3591 | 400 | 0.0004 | - |
| 0.4039 | 450 | 0.0003 | - |
| 0.4488 | 500 | 0.0002 | - |
| 0.4937 | 550 | 0.0002 | - |
| 0.5386 | 600 | 0.0002 | - |
| 0.5835 | 650 | 0.0002 | - |
| 0.6284 | 700 | 0.0001 | - |
| 0.6732 | 750 | 0.0001 | - |
| 0.7181 | 800 | 0.0001 | - |
| 0.7630 | 850 | 0.0001 | - |
| 0.8079 | 900 | 0.0001 | - |
| 0.8528 | 950 | 0.0001 | - |
| 0.8977 | 1000 | 0.0001 | - |
| 0.9425 | 1050 | 0.0001 | - |
| 0.9874 | 1100 | 0.0001 | - |
| 1.0 | 1114 | - | 0.0938 |
| 1.0323 | 1150 | 0.0001 | - |
| 1.0772 | 1200 | 0.0001 | - |
| 1.1221 | 1250 | 0.0001 | - |
| 1.1670 | 1300 | 0.0001 | - |
| 1.2118 | 1350 | 0.0001 | - |
| 1.2567 | 1400 | 0.0001 | - |
| 1.3016 | 1450 | 0.0001 | - |
| 1.3465 | 1500 | 0.0001 | - |
| 1.3914 | 1550 | 0.0001 | - |
| 1.4363 | 1600 | 0.0 | - |
| 1.4811 | 1650 | 0.0 | - |
| 1.5260 | 1700 | 0.0 | - |
| 1.5709 | 1750 | 0.0 | - |
| 1.6158 | 1800 | 0.0 | - |
| 1.6607 | 1850 | 0.0 | - |
| 1.7056 | 1900 | 0.0 | - |
| 1.7504 | 1950 | 0.0 | - |
| 1.7953 | 2000 | 0.0 | - |
| 1.8402 | 2050 | 0.0 | - |
| 1.8851 | 2100 | 0.0 | - |
| 1.9300 | 2150 | 0.0 | - |
| 1.9749 | 2200 | 0.0 | - |
| 2.0 | 2228 | - | 0.0951 |
| 2.0197 | 2250 | 0.0003 | - |
| 2.0646 | 2300 | 0.0012 | - |
| 2.1095 | 2350 | 0.0005 | - |
| 2.1544 | 2400 | 0.001 | - |
| 2.1993 | 2450 | 0.0001 | - |
| 2.2442 | 2500 | 0.0001 | - |
| 2.2890 | 2550 | 0.0001 | - |
| 2.3339 | 2600 | 0.0001 | - |
| 2.3788 | 2650 | 0.0001 | - |
| 2.4237 | 2700 | 0.0001 | - |
| 2.4686 | 2750 | 0.0001 | - |
| 2.5135 | 2800 | 0.0 | - |
| 2.5583 | 2850 | 0.0001 | - |
| 2.6032 | 2900 | 0.0 | - |
| 2.6481 | 2950 | 0.0 | - |
| 2.6930 | 3000 | 0.0 | - |
| 2.7379 | 3050 | 0.0 | - |
| 2.7828 | 3100 | 0.0 | - |
| 2.8276 | 3150 | 0.0 | - |
| 2.8725 | 3200 | 0.0 | - |
| 2.9174 | 3250 | 0.0 | - |
| 2.9623 | 3300 | 0.0 | - |
| 3.0 | 3342 | - | 0.0964 |
### Framework Versions
- Python: 3.10.14
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.44.0
- PyTorch: 2.4.0
- Datasets: 2.21.0
- 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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