---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: We think rain is reducing, rain reducing, so we need to do something about
Russell.
- text: And it's still a long stint to the end, many laps.
- text: Jacob will box soon, we believe, and has been asked to remove tire management.
- text: Don't go too crazy on the brake warm up.
- text: Degradation seems low on the time. Emetal started off in a 38.8 and is now
on a 39.1 after 16 laps.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
---
# SetFit
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 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
- **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:** 5 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 |
|:------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| INFORMATION | - 'We lose a lot of time on the streets, easy laptop.'
- 'Okay, Lucid Gap 1.2, nice work. Right, eight laps to go once you cross the line.'
- "Well done, Daniel. That's the checkered flag. P7, buddy. P7 is a good start. Well defended at the end. That was great driving last few laps. Cheers, guys. Good start. Obviously, I'll learn a bit from today. We'll keep getting better, but not a bad first weekend. Congrats."
|
| PROBLEM | - 'I think I have still some damage. Balance is quite a bit off. Understood. We do see from data.'
- 'So that floor damage looks like it picked up at turn 8, so right hand side.'
- 'Okay, so switch off the engine please Lewis.'
|
| ORDER | - 'Okay, mode race and focus on turn two preparation, turn two and three for this lap.'
- 'Okay, that last lap, same pace as signs. And mode seven. Mode seven.'
- 'Yuki, we are rear left limited, so watch we spin and we can push in the high speed. Push in the high speed.'
|
| WARNING | - 'Be careful, a lot of people in the pit lane, so obviously they will move, but watch yourself as well.'
- "The only concern I have, if the safety car comes out and we have to pit for that hard tyre and I can't get any temperature into it, we're in trouble. Yeah, copy Lewis, we're not concerned, we think everyone will be in the same boat, if not worse."
- "So, Max, for info, you've been given a 10 second penalty for forcing Lando off track at turn 4. So, head down. 10. That's quite impressive. That was a lot of whinging. A lot."
|
| QUESTION | - "Do you want to flap adjust Nico? Not really, but it's new sticky tires. Let's maybe try. Which way would you go? I would take off, yeah. I would try less, I guess, and see what it feels like. Okay, copy. Take like half percent down. Turn 10 feels pretty low in grip, like tailwind, I think. The rear is pretty unhappy there. Yes, on the tailwind into 10. Also, 1 and 6 and 7 are tailwind. Okay, so we've taken off 0.5. We'll get a feel. One more grid. Go to the grid after this. Close the radio rear, it's too loud."
- "That's it, mate. You are world champion. World champion. What a mate. I'm so proud. Lando, this is Zach from McLaren. Is this the world champion hotline? Yeah. You did it! You did it! Arthur! Woo! Thank you, guys. Oh my god. You made a kid's dream come true. Thank you so much. I love you guys. Thanks for everything. You deserve it. I love you, Mum. I love you, Dad. Thanks for everything. I'm not crying."
- "And Lando, do you think you can get past? Otherwise, what about plan B? Remember R switch, R switch. Yeah, if you've got the goblins, go for it."
|
## 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("Don't go too crazy on the brake warm up.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 2 | 27.2063 | 386 |
| Label | Training Sample Count |
|:------------|:----------------------|
| INFORMATION | 32 |
| PROBLEM | 32 |
| ORDER | 32 |
| WARNING | 32 |
| QUESTION | 32 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0013 | 1 | 0.1755 | - |
| 0.0625 | 50 | 0.3393 | - |
| 0.125 | 100 | 0.3649 | - |
| 0.1875 | 150 | 0.4159 | - |
| 0.25 | 200 | 0.3289 | - |
| 0.3125 | 250 | 0.3171 | - |
| 0.375 | 300 | 0.2952 | - |
| 0.4375 | 350 | 0.3004 | - |
| 0.5 | 400 | 0.3111 | - |
| 0.5625 | 450 | 0.3039 | - |
| 0.625 | 500 | 0.2702 | - |
| 0.6875 | 550 | 0.2891 | - |
| 0.75 | 600 | 0.2793 | - |
| 0.8125 | 650 | 0.2652 | - |
| 0.875 | 700 | 0.2761 | - |
| 0.9375 | 750 | 0.2603 | - |
| 1.0 | 800 | 0.2543 | 0.5 |
### Framework Versions
- Python: 3.12.13
- SetFit: 1.1.3
- Sentence Transformers: 5.4.1
- Transformers: 5.0.0
- PyTorch: 2.10.0+cu128
- Datasets: 5.0.0
- Tokenizers: 0.22.2
## 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}
}
```