setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/all-mpnet-base-v2 base_model:finetune:sentence-transformers/all-mpnet-base-v2 model-index region:us
---
base_model: sentence-transformers/all-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 hate seeing you struggle without help.
- text: I feel hopeless when things never go as planned.
- text: I hate the emptiness I feel inside.
- text: I feel lost and don't know where I'm headed.
- text: I can't shake off the feeling of regret.
inference: true
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: f1
      value: 1.0
      name: F1
---

# 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 [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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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                                                                                                                                                                       |
|:---------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| negative | <ul><li>"I can't bear to see the pain I've caused."</li><li>'I feel isolated and disconnected from everyone.'</li><li>'I feel broken when everything goes wrong.'</li></ul>    |
| positive | <ul><li>"I can't bear the idea of you being treated badly."</li><li>'I will never forgive those who harm you.'</li><li>'I feel disgusted by those who mistreat you.'</li></ul> |

## Evaluation

### Metrics
| Label   | F1  |
|:--------|:----|
| **all** | 1.0 |

## 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("Mohamedsheded33/setfit-generated-data-test-on-specific-cases")
# Run inference
preds = model("I hate the emptiness I feel inside.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 5   | 8.0    | 12  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| negative | 24                    |
| positive | 16                    |

### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (3, 3)
- 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: False

### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-----:|:----:|:-------------:|:---------------:|
| 0.01  | 1    | 0.3159        | -               |
| 0.5   | 50   | 0.0865        | -               |
| 1.0   | 100  | 0.0008        | -               |
| 1.5   | 150  | 0.0004        | -               |
| 2.0   | 200  | 0.0003        | -               |
| 2.5   | 250  | 0.0003        | -               |
| 3.0   | 300  | 0.0003        | -               |

### Framework Versions
- Python: 3.11.11
- SetFit: 1.1.1
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu121
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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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