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: 0.7727272727272727
      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** | 0.7727 |

## 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/dimension1_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: (4.4226261631087265e-05, 4.4226261631087265e-05)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0006 | 1    | 0.2748        | -               |
| 0.0280 | 50   | 0.2678        | -               |
| 0.0559 | 100  | 0.2688        | -               |
| 0.0839 | 150  | 0.2709        | -               |
| 0.1119 | 200  | 0.2656        | -               |
| 0.1398 | 250  | 0.259         | -               |
| 0.1678 | 300  | 0.2565        | -               |
| 0.1957 | 350  | 0.2655        | -               |
| 0.2237 | 400  | 0.2737        | -               |
| 0.2517 | 450  | 0.2501        | -               |
| 0.2796 | 500  | 0.2512        | -               |
| 0.3076 | 550  | 0.2381        | -               |
| 0.3356 | 600  | 0.2568        | -               |
| 0.3635 | 650  | 0.2642        | -               |
| 0.3915 | 700  | 0.2743        | -               |
| 0.4195 | 750  | 0.2635        | -               |
| 0.4474 | 800  | 0.263         | -               |
| 0.4754 | 850  | 0.2541        | -               |
| 0.5034 | 900  | 0.2492        | -               |
| 0.5313 | 950  | 0.26          | -               |
| 0.5593 | 1000 | 0.257         | -               |
| 0.5872 | 1050 | 0.2525        | -               |
| 0.6152 | 1100 | 0.2594        | -               |
| 0.6432 | 1150 | 0.2656        | -               |
| 0.6711 | 1200 | 0.2737        | -               |
| 0.6991 | 1250 | 0.2683        | -               |
| 0.7271 | 1300 | 0.259         | -               |
| 0.7550 | 1350 | 0.2617        | -               |
| 0.7830 | 1400 | 0.294         | -               |
| 0.8110 | 1450 | 0.2446        | -               |
| 0.8389 | 1500 | 0.2618        | -               |
| 0.8669 | 1550 | 0.2562        | -               |
| 0.8949 | 1600 | 0.264         | -               |
| 0.9228 | 1650 | 0.2534        | -               |
| 0.9508 | 1700 | 0.2484        | -               |
| 0.9787 | 1750 | 0.2666        | -               |

### 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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