README.md

---
base_model: BAAI/bge-small-en-v1.5
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 BAAI/bge-small-en-v1.5
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: f1
      value: 0.49504950495049505
      name: F1
---

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

This is a [SetFit](https://github.com/huggingface/setfit) model 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 [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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 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** | 0.4950 |

## 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/dimension3_setfit_BAAI")
# Run inference
preds = model("I loved the spiderman movie!")
```

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## Training Details

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (0.0003728764106052876, 0.0003728764106052876)
- 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    | 0.3158        | -               |
| 0.0353 | 50   | 0.2596        | -               |
| 0.0706 | 100  | 0.2583        | -               |
| 0.1059 | 150  | 0.259         | -               |
| 0.1412 | 200  | 0.268         | -               |
| 0.1766 | 250  | 0.2594        | -               |
| 0.2119 | 300  | 0.2606        | -               |
| 0.2472 | 350  | 0.2628        | -               |
| 0.2825 | 400  | 0.2643        | -               |
| 0.3178 | 450  | 0.2594        | -               |
| 0.3531 | 500  | 0.2579        | -               |
| 0.3884 | 550  | 0.2632        | -               |
| 0.4237 | 600  | 0.2583        | -               |
| 0.4590 | 650  | 0.2575        | -               |
| 0.4944 | 700  | 0.2636        | -               |
| 0.5297 | 750  | 0.2579        | -               |
| 0.5650 | 800  | 0.2652        | -               |
| 0.6003 | 850  | 0.2599        | -               |
| 0.6356 | 900  | 0.2592        | -               |
| 0.6709 | 950  | 0.264         | -               |
| 0.7062 | 1000 | 0.2625        | -               |
| 0.7415 | 1050 | 0.2568        | -               |
| 0.7768 | 1100 | 0.2651        | -               |
| 0.8121 | 1150 | 0.2586        | -               |
| 0.8475 | 1200 | 0.2636        | -               |
| 0.8828 | 1250 | 0.2614        | -               |
| 0.9181 | 1300 | 0.2594        | -               |
| 0.9534 | 1350 | 0.2614        | -               |
| 0.9887 | 1400 | 0.2621        | -               |

### 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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