---
base_model: csarron/mobilebert-uncased-squad-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: I can't believe how much time has flown by since we last talked.
- text: Have you completed the assignment?
- text: What's the total budget for the campaign?
- text: What's new with you?
- text: Have a good day!
inference: true
---

# SetFit with csarron/mobilebert-uncased-squad-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [csarron/mobilebert-uncased-squad-v2](https://huggingface.co/csarron/mobilebert-uncased-squad-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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:** [csarron/mobilebert-uncased-squad-v2](https://huggingface.co/csarron/mobilebert-uncased-squad-v2)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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
| Label | Examples                                                                                                                                                           |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>"How's the family?"</li><li>'Thanks a million.'</li><li>'I appreciate your kindness.'</li></ul>                                                            |
| 0     | <ul><li>'What is the next step in the process?'</li><li>'Please complete the review by the end of the week.'</li><li>'I feel disconnected from reality.'</li></ul> |

## 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-mobile-bert-phatic")
# Run inference
preds = model("Have a good day!")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 8.2394 | 184 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 143                   |
| 1     | 116                   |

### Training Hyperparameters
- batch_size: (16, 16)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0009 | 1    | 0.3528        | -               |
| 1.0    | 1068 | 0.0252        | 0.0729          |
| 2.0    | 2136 | 0.0001        | 0.0544          |
| 0.0015 | 1    | 0.0           | -               |
| 0.0772 | 50   | 0.001         | -               |
| 0.1543 | 100  | 0.0           | -               |
| 0.2315 | 150  | 0.0           | -               |
| 0.3086 | 200  | 0.0           | -               |
| 0.3858 | 250  | 0.0015        | -               |
| 0.4630 | 300  | 0.001         | -               |
| 0.5401 | 350  | 0.0           | -               |
| 0.6173 | 400  | 0.0           | -               |
| 0.6944 | 450  | 0.0           | -               |
| 0.7716 | 500  | 0.0           | -               |
| 0.8488 | 550  | 0.0           | -               |
| 0.9259 | 600  | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.2.1
- Transformers: 4.39.0
- PyTorch: 2.0.1+cu117
- Datasets: 3.1.0
- 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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