
# SetFit with BAAI/bge-small-en-v1.5

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [SetFit/sst2](https://huggingface.co/datasets/SetFit/sst2) dataset that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
- **Training Dataset:** [SetFit/sst2](https://huggingface.co/datasets/SetFit/sst2)
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### 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                                                                                                                                                                                                                                                                                                                                                                                                                                        |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>'a stirring , funny and finally transporting re-imagining of beauty and the beast and 1930s horror films'</li><li>'this is a visually stunning rumination on love , memory , history and the war between art and commerce .'</li><li>"jonathan parker 's bartleby should have been the be-all-end-all of the modern-office anomie films ."</li></ul>                                                                                    |
| 0     | <ul><li>'apparently reassembled from the cutting-room floor of any given daytime soap .'</li><li>"they presume their audience wo n't sit still for a sociology lesson , however entertainingly presented , so they trot out the conventional science-fiction elements of bug-eyed monsters and futuristic women in skimpy clothes ."</li><li>'a fan film that for the uninitiated plays better on video with the sound turned down .'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8842   |

## 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("dkorat/bge-small-en-v1.5_setfit-sst2-english")
# Run inference
preds = model("a noble failure .")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 19.591 | 46  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 479                   |
| 1     | 521                   |

### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 1
- body_learning_rate: (2e-05, 1e-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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-----:|:----:|:-------------:|:---------------:|
| 0.008 | 1    | 0.241         | -               |
| 0.4   | 50   | 0.2525        | -               |
| 0.8   | 100  | 0.0607        | -               |

### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.3.0
- Transformers: 4.37.2
- PyTorch: 2.1.2+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.1

## 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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setfit pytorch safetensors bert sentence-transformers text-classification generated_from_setfit_trainer dataset:SetFit/sst2 arxiv:2209.11055 base_model:BAAI/bge-small-en-v1.5 model-index region:us