---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: The tech giant announced today the appointment of a new CTO to lead their
    innovative projects in AI.
- text: During the recent annual meeting, the board of directors at HealthCorp discussed
    various operational strategies but did not announce any changes to their leadership
    team.
- text: Tech giant Innovatech has announced the appointment of Jane Doe as their new
    Chief Technology Officer, effective immediately. This change aims to drive the
    company's focus on artificial intelligence.
- text: The political landscape in the country shifted dramatically with a recent
    election, leading to new party leadership.
- text: In a recent interview, the founder of Foodies Co. expressed his frustration
    over market competition but reassured that no changes in leadership were imminent.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: dunzhang/stella_en_400M_v5
model-index:
- name: SetFit with dunzhang/stella_en_400M_v5
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.9666666666666667
      name: Accuracy
---

# SetFit with dunzhang/stella_en_400M_v5

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [dunzhang/stella_en_400M_v5](https://huggingface.co/dunzhang/stella_en_400M_v5) 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:** [dunzhang/stella_en_400M_v5](https://huggingface.co/dunzhang/stella_en_400M_v5)
- **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) -->
<!-- - **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                                                                                                                                                                                                                                                                                |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| True  | <ul><li>'Tech giant ABC Corp appoints a new CTO to drive innovation.'</li><li>'The CEO of DEF Inc. steps down after a decade at the helm, marking a change in leadership.'</li><li>'GHI Industries promotes its Chief Marketing Officer to take over as CEO.'</li></ul>                 |
| False | <ul><li>'XYZ Ltd. announced a strategic shift but no change in leadership positions.'</li><li>'New government regulations might prompt shifts in corporate governance.'</li><li>'The recent quarterly report highlighted performance issues but indicated stable management.'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9667   |

## 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("amplyfi/leadership-change")
# Run inference
preds = model("The tech giant announced today the appointment of a new CTO to lead their innovative projects in AI.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 9   | 14.9963 | 23  |

| Label | Training Sample Count |
|:------|:----------------------|
| False | 136                   |
| True  | 135                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 5
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0059 | 1    | 0.2381        | -               |
| 0.2941 | 50   | 0.1096        | -               |
| 0.5882 | 100  | 0.0006        | -               |
| 0.8824 | 150  | 0.0003        | -               |
| 1.1765 | 200  | 0.0001        | -               |
| 1.4706 | 250  | 0.0           | -               |
| 1.7647 | 300  | 0.0           | -               |
| 2.0588 | 350  | 0.0           | -               |
| 2.3529 | 400  | 0.0           | -               |
| 2.6471 | 450  | 0.0           | -               |
| 2.9412 | 500  | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.42.2
- PyTorch: 2.5.1+cu124
- Datasets: 3.1.0
- 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}
}
```

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