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---
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
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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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 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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