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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: The best thing about this is it drowned out the call from the guy angry cause
he hadn't gotten a tracking number... http://t.co/QYu8grOrQ1
- text: 'http://t.co/a0v1ybySOD Its the best time of day!! åÊ @Siren_Voice is #liveonstreamate!'
- text: 16yr old PKK suicide bomber who detonated bomb in Turkey Army trench released
http://t.co/mMkLapX2ok
- text: '#hot Reddit''s new content policy goes into effect many horrible subreddits
banned or quarantined http://t.co/HqdCZzdmbN #prebreak #best'
- text: Heat wave warning aa? Ayyo dei. Just when I plan to visit friends after a
year.
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/all-mpnet-base-v2
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: 0.8098990736900318
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) -->
<!-- - **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 |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | <ul><li>'peanut butter cookie dough blizzard is ??????????????????????'</li><li>'Free Ebay Sniping RT? http://t.co/B231Ul1O1K Lumbar Extender Back Stretcher Excellent Condition!! ?Please Favorite & Share'</li><li>"'13 M. Chapoutier Crozes Hermitage so much purple violets slate crushed gravel white pepper. Yum #france #wine #DC http://t.co/skvWN38HZ7"</li></ul> |
| 1 | <ul><li>'DUST IN THE WIND: @82ndABNDIV paratroopers move to a loading zone during a dust storm in support of Operation Fury: http://t.co/uGesKLCn8M'</li><li>'Delhi Government to Provide Free Treatment to Acid Attack Victims in Private Hospitals http://t.co/H6PM1W7elL'</li><li>'National Briefing | West: California: Spring Oil Spill Estimate Grows: Documents released on Wednesday disclos... http://t.co/wBi7Laq18E'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8099 |
## 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("pEpOo/catastrophy")
# Run inference
preds = model("Heat wave warning aa? Ayyo dei. Just when I plan to visit friends after a year.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 2 | 15.3737 | 31 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 222 |
| 1 | 158 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0005 | 1 | 0.3038 | - |
| 0.0263 | 50 | 0.1867 | - |
| 0.0526 | 100 | 0.2578 | - |
| 0.0789 | 150 | 0.2298 | - |
| 0.1053 | 200 | 0.1253 | - |
| 0.1316 | 250 | 0.0446 | - |
| 0.1579 | 300 | 0.1624 | - |
| 0.1842 | 350 | 0.0028 | - |
| 0.2105 | 400 | 0.0059 | - |
| 0.2368 | 450 | 0.0006 | - |
| 0.2632 | 500 | 0.0287 | - |
| 0.2895 | 550 | 0.003 | - |
| 0.3158 | 600 | 0.0004 | - |
| 0.3421 | 650 | 0.0014 | - |
| 0.3684 | 700 | 0.0002 | - |
| 0.3947 | 750 | 0.0001 | - |
| 0.4211 | 800 | 0.0002 | - |
| 0.4474 | 850 | 0.0002 | - |
| 0.4737 | 900 | 0.0002 | - |
| 0.5 | 950 | 0.0826 | - |
| 0.5263 | 1000 | 0.0002 | - |
| 0.5526 | 1050 | 0.0001 | - |
| 0.5789 | 1100 | 0.0003 | - |
| 0.6053 | 1150 | 0.0303 | - |
| 0.6316 | 1200 | 0.0001 | - |
| 0.6579 | 1250 | 0.0 | - |
| 0.6842 | 1300 | 0.0001 | - |
| 0.7105 | 1350 | 0.0 | - |
| 0.7368 | 1400 | 0.0001 | - |
| 0.7632 | 1450 | 0.0002 | - |
| 0.7895 | 1500 | 0.0434 | - |
| 0.8158 | 1550 | 0.0001 | - |
| 0.8421 | 1600 | 0.0 | - |
| 0.8684 | 1650 | 0.0001 | - |
| 0.8947 | 1700 | 0.0001 | - |
| 0.9211 | 1750 | 0.0001 | - |
| 0.9474 | 1800 | 0.0001 | - |
| 0.9737 | 1850 | 0.0001 | - |
| 1.0 | 1900 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.15.0
- 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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