---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: 'first: We recommend self-help books on conflict resolution, available in
    our office library, as supplemental resources. second: Our company conducts regular
    surveys to identify and address recurring disputes.'
- text: 'first: Conflict Resolution Apps: We offer technology solutions for reporting
    and tracking conflicts. second: Employees can request a mediator to assist in
    resolving issues with their supervisor, ensuring fair dispute resolution.'
- text: 'first: Our organization encourages employees to participate in leadership
    development programs, enhancing their ability to interact with supervisors. second:
    Conflict Simulation Exercises: Role-playing helps employees practice resolving
    conflicts.'
- text: 'first: Mediation sessions are scheduled outside of regular working hours
    for convenience. second: Employee Conflict Coaches: Coaches work one-on-one with
    employees to resolve disputes.'
- text: 'first: We provide conflict resolution pamphlets in the breakroom, offering
    helpful tips. second: We provide resources for employees to seek external mediation
    or counseling services if disputes with supervisors persist.'
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: 0.4090909090909091
      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** | 0.4091   |

## 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("diegofiggie/fairness_model")
# Run inference
preds = model("first: Mediation sessions are scheduled outside of regular working hours for convenience. second: Employee Conflict Coaches: Coaches work one-on-one with employees to resolve disputes.")
```

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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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0333 | 1    | 0.2322        | -               |

### Framework Versions
- Python: 3.10.9
- SetFit: 1.0.3
- Sentence Transformers: 2.4.0
- Transformers: 4.38.1
- PyTorch: 2.2.1+cpu
- 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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