README.md

---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: setfit
metrics:
- f1
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget: []
inference: true
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: f1
      value: 0.12903225806451613
      name: F1
---

# 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)

## Evaluation

### Metrics
| Label   | F1     |
|:--------|:-------|
| **all** | 0.1290 |

## 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("Zlovoblachko/dimension2_w_thesis_setfit")
# Run inference
preds = model("I loved the spiderman movie!")
```

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## Training Details

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (0.00031763046129120506, 0.00031763046129120506)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0007 | 1    | 0.304         | -               |
| 0.0347 | 50   | 0.2656        | -               |
| 0.0694 | 100  | 0.2733        | -               |
| 0.1042 | 150  | 0.268         | -               |
| 0.1389 | 200  | 0.2712        | -               |
| 0.1736 | 250  | 0.2726        | -               |
| 0.2083 | 300  | 0.2758        | -               |
| 0.2431 | 350  | 0.2807        | -               |
| 0.2778 | 400  | 0.2877        | -               |
| 0.3125 | 450  | 0.2641        | -               |
| 0.3472 | 500  | 0.2761        | -               |
| 0.3819 | 550  | 0.2739        | -               |
| 0.4167 | 600  | 0.2565        | -               |
| 0.4514 | 650  | 0.2813        | -               |
| 0.4861 | 700  | 0.2761        | -               |
| 0.5208 | 750  | 0.2749        | -               |
| 0.5556 | 800  | 0.2585        | -               |
| 0.5903 | 850  | 0.2737        | -               |
| 0.625  | 900  | 0.2807        | -               |
| 0.6597 | 950  | 0.2782        | -               |
| 0.6944 | 1000 | 0.2736        | -               |
| 0.7292 | 1050 | 0.28          | -               |
| 0.7639 | 1100 | 0.2821        | -               |
| 0.7986 | 1150 | 0.2755        | -               |
| 0.8333 | 1200 | 0.2743        | -               |
| 0.8681 | 1250 | 0.2634        | -               |
| 0.9028 | 1300 | 0.2779        | -               |
| 0.9375 | 1350 | 0.2744        | -               |
| 0.9722 | 1400 | 0.2816        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.5.0+cu121
- Datasets: 3.0.2
- 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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