README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget: []
metrics:
- f1
pipeline_tag: text-classification
library_name: setfit
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: f1
      value: 0.8181818181818182
      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** | 0.8182 |

## 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/dim1_setfit")
# Run inference
preds = model("I loved the spiderman movie!")
```

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## Training Details

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (0.00023323617397037305, 0.00023323617397037305)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0011 | 1    | 0.2497        | -               |
| 0.0541 | 50   | 0.2784        | -               |
| 0.1081 | 100  | 0.2797        | -               |
| 0.1622 | 150  | 0.2886        | -               |
| 0.2162 | 200  | 0.2863        | -               |
| 0.2703 | 250  | 0.2751        | -               |
| 0.3243 | 300  | 0.2934        | -               |
| 0.3784 | 350  | 0.2857        | -               |
| 0.4324 | 400  | 0.293         | -               |
| 0.4865 | 450  | 0.2791        | -               |
| 0.5405 | 500  | 0.2985        | -               |
| 0.5946 | 550  | 0.2998        | -               |
| 0.6486 | 600  | 0.2822        | -               |
| 0.7027 | 650  | 0.2849        | -               |
| 0.7568 | 700  | 0.2877        | -               |
| 0.8108 | 750  | 0.2818        | -               |
| 0.8649 | 800  | 0.2854        | -               |
| 0.9189 | 850  | 0.2986        | -               |
| 0.9730 | 900  | 0.2956        | -               |
| 1.0270 | 950  | 0.292         | -               |
| 1.0811 | 1000 | 0.2881        | -               |
| 1.1351 | 1050 | 0.2894        | -               |
| 1.1892 | 1100 | 0.29          | -               |
| 1.2432 | 1150 | 0.2783        | -               |
| 1.2973 | 1200 | 0.2601        | -               |
| 1.3514 | 1250 | 0.3014        | -               |
| 1.4054 | 1300 | 0.2877        | -               |
| 1.4595 | 1350 | 0.2998        | -               |
| 1.5135 | 1400 | 0.2822        | -               |
| 1.5676 | 1450 | 0.3072        | -               |
| 1.6216 | 1500 | 0.2739        | -               |
| 1.6757 | 1550 | 0.2797        | -               |
| 1.7297 | 1600 | 0.2751        | -               |
| 1.7838 | 1650 | 0.2912        | -               |
| 1.8378 | 1700 | 0.292         | -               |
| 1.8919 | 1750 | 0.3024        | -               |
| 1.9459 | 1800 | 0.299         | -               |
| 2.0    | 1850 | 0.2898        | -               |

### Framework Versions
- Python: 3.11.13
- SetFit: 1.1.2
- Sentence Transformers: 4.1.0
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1

## 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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