---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: 'first: We recommend employees start a support group to share and address
    workplace concerns. second: Grievance Resolution Committee: A committee addresses
    formal grievances and ensures a fair resolution process.'
- text: 'first: Supervisors are encouraged to watch TED talks on communication to
    enhance their skills. second: Progressive Discipline: Disciplinary actions are
    proportionate and follow a structured process.'
- text: 'first: Grievance Resolution Committee: A committee addresses formal grievances
    and ensures a fair resolution process. second: We provide employees with a comprehensive
    handbook outlining our dispute resolution process for clarity.'
- text: 'first: We recommend employees seek advice from their peers and mentors to
    navigate workplace issues. second: We use technology-based solutions to facilitate
    virtual conflict resolution discussions.'
- text: 'first: We''ve introduced a complaint of the month contest to highlight and
    address concerns effectively. second: Our company has a clear conflict resolution
    policy that all employees must follow.'
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/all-MiniLM-L6-v2
model-index:
- name: SetFit with sentence-transformers/all-MiniLM-L6-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.7272727272727273
      name: Accuracy
---

# SetFit with sentence-transformers/all-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) 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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 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                                                                                                                                                                                                                                               |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>'first: Employee Support Groups: Peer-led support groups for employees facing similar issues. second: We offer conflict resolution workshops to provide employees with valuable skills.'</li></ul>                                             |
| 1     | <ul><li>'first: Conflict Resolution Peer Mentoring: Experienced employees mentor newcomers in conflict resolution. second: Diversity and Inclusion Training: Programs that promote understanding and reduce conflicts related to diversity.'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.7273   |

## 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("sijan1/setfit-finetuned-fairness")
# Run inference
preds = model("first: We've introduced a complaint of the month contest to highlight and address concerns effectively. second: Our company has a clear conflict resolution policy that all employees must follow.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 24  | 25.5   | 27  |

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

### Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 30
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.1    | 1    | 0.0141        | -               |
| 5.0    | 50   | 0.0012        | -               |
| 10.0   | 100  | 0.0006        | -               |
| 0.1    | 1    | 0.0005        | -               |
| 5.0    | 50   | 0.0005        | -               |
| 10.0   | 100  | 0.0002        | -               |
| 15.0   | 150  | 0.0002        | -               |
| 20.0   | 200  | 0.0001        | -               |
| 25.0   | 250  | 0.0001        | -               |
| 30.0   | 300  | 0.0001        | -               |
| 35.0   | 350  | 0.0002        | -               |
| 40.0   | 400  | 0.0           | -               |
| 45.0   | 450  | 0.0           | -               |
| 50.0   | 500  | 0.0           | -               |
| 55.0   | 550  | 0.0           | -               |
| 60.0   | 600  | 0.0           | -               |
| 65.0   | 650  | 0.0001        | -               |
| 70.0   | 700  | 0.0           | -               |
| 75.0   | 750  | 0.0           | -               |
| 80.0   | 800  | 0.0           | -               |
| 85.0   | 850  | 0.0           | -               |
| 90.0   | 900  | 0.0           | -               |
| 95.0   | 950  | 0.0           | -               |
| 100.0  | 1000 | 0.0           | -               |
| 0.0667 | 1    | 0.0           | -               |
| 0.8333 | 50   | 0.0           | -               |
| 0.0222 | 1    | 0.0           | -               |
| 0.8333 | 50   | 0.0           | -               |
| 0.0111 | 1    | 0.0001        | -               |
| 0.5556 | 50   | 0.0           | -               |
| 0.0333 | 1    | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.37.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.1
- 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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