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---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- f1
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: I'm so excited for the weekend, I get to spend time with my friends and family.
We're planning a hike and then having a BBQ. I love days like this!
- text: You're just a stupid white person, you'll never understand what it's like
to be a minority. You're so privileged, you have no idea how much racism you've
experienced in your life. Get out of here with your entitled attitude.
- text: Are you f***ing kidding me?! This is the worst customer service I've ever
experienced. I've been on hold for 45 minutes and no one has even bothered to
answer my call. Unbelievable.
- text: You're such a f***ing idiot, how dare you even try to tell me what to do.
I swear to god, you're the most annoying person I've ever met. Just f*** off and
leave me alone.
- text: 'Just got the cutest puppy and I''m so in love with him! He''s already stolen
my heart and I''m sure he''ll bring so much joy to our family. Anyone else have
a furry friend at home? #puppylove #dogsofinstagram #loveofmylife'
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: f1
value: 0.8648435963013968
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
**This dataset may contain racism, sexuality, or other undesired content.**
| Label | Examples |
|:----------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Toxic | <ul><li>"Can't believe this black guy thinks he can be president. What a joke. He's only in office because of affirmative action. Get over it, America!"</li><li>"I'm so sick of you, you think you're so much better than everyone else just because you have a few more followers. Newsflash, you're just as toxic as the rest of us. Go cry to your mom."</li><li>"I'm going to smash your car window if you don't give me money right now. You're going to regret messing with me."</li></ul> |
| Non toxic | <ul><li>"I just wanted to say thank you to the amazing team at @CompanyX for their incredible customer service. They went above and beyond to resolve my issue and I'm so grateful for their help. #CustomerLove #Respect"</li><li>"I'm having a great day, thanks for asking! How about you? I just got back from a lovely hike in the mountains and I'm feeling refreshed."</li><li>"I'm feeling really overwhelmed with my coursework. Do you have any tips on how to manage my time effectively?"</li></ul> |
## Evaluation
### Metrics
| Label | F1 |
|:--------|:-------|
| **all** | 0.8648 |
## 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("I'm so excited for the weekend, I get to spend time with my friends and family. We're planning a hike and then having a BBQ. I love days like this!")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 14 | 27.5 | 42 |
| Label | Training Sample Count |
|:----------|:----------------------|
| Non toxic | 12 |
| Toxic | 20 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (5, 5)
- 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 |
|:-------:|:------:|:-------------:|:---------------:|
| 0.0278 | 1 | 0.2873 | - |
| 1.0 | 36 | - | 0.1098 |
| 1.3889 | 50 | 0.0013 | - |
| **2.0** | **72** | **-** | **0.0981** |
| 2.7778 | 100 | 0.0003 | - |
| 3.0 | 108 | - | 0.112 |
| 4.0 | 144 | - | 0.1174 |
| 4.1667 | 150 | 0.0001 | - |
| 5.0 | 180 | - | 0.1075 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.9.19
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.4.0
- Datasets: 2.20.0
- 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}
}
```
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