---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: FacebookAI/roberta-Large
metrics:
- accuracy
widget:
- text: How to write a science fiction novel
- text: Overcoming social anxiety and fear of public speaking
- text: Supporting a family member with depression
- text: Understanding stock market trends
- text: Recipes for homemade Italian pasta
pipeline_tag: text-classification
inference: true
---
# SetFit with FacebookAI/roberta-Large
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [FacebookAI/roberta-Large](https://huggingface.co/FacebookAI/roberta-Large) 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:** [FacebookAI/roberta-Large](https://huggingface.co/FacebookAI/roberta-Large)
- **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
### 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 |
- 'Exploring historical landmarks in Europe'
- 'How to create an effective resume'
- 'Exercises to improve core strength'
|
| False | - 'Feeling sad or empty for long periods without any specific reason'
- 'Dealing with the emotional impact of chronic illness'
- 'Understanding and coping with panic attacks'
|
## 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("richie-ghost/setfit-FacebookAI-roberta-Large-MentalHealth-Topic-Check")
# Run inference
preds = model("Understanding stock market trends")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 4 | 6.4583 | 11 |
| Label | Training Sample Count |
|:------|:----------------------|
| True | 22 |
| False | 26 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (8, 8)
- 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 |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0132 | 1 | 0.4868 | - |
| 0.6579 | 50 | 0.0286 | - |
| 1.0 | 76 | - | 0.0079 |
| 1.3158 | 100 | 0.0028 | - |
| 1.9737 | 150 | 0.0005 | - |
| 2.0 | 152 | - | 0.0015 |
| 2.6316 | 200 | 0.0003 | - |
| 3.0 | 228 | - | 0.001 |
| 3.2895 | 250 | 0.0006 | - |
| 3.9474 | 300 | 0.0002 | - |
| 4.0 | 304 | - | 0.0009 |
| 4.6053 | 350 | 0.0001 | - |
| **5.0** | **380** | **-** | **0.0004** |
| 5.2632 | 400 | 0.0002 | - |
| 5.9211 | 450 | 0.0001 | - |
| 6.0 | 456 | - | 0.0005 |
| 6.5789 | 500 | 0.0001 | - |
| 7.0 | 532 | - | 0.0006 |
| 7.2368 | 550 | 0.0001 | - |
| 7.8947 | 600 | 0.0002 | - |
| 8.0 | 608 | - | 0.0008 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.0
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- 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}
}
```