---
base_model: BAAI/bge-small-en-v1.5
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: I think we’re ready to address another issue.
- text: Repeat the question for me please
- text: I think we can switch to a new discussion.
- text: I believe I’ve handled this, what’s the next topic?
- text: That’s the last thing I wanted to cover, I’m done.
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: accuracy
      value: 0.918918918918919
      name: Accuracy
---

# 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:** 4 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)

### Model Labels
| Label         | Examples                                                                                                                                                                                                                                                            |
|:--------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| end_question  | <ul><li>'I’m done, we can close the session now.'</li><li>'I’ve addressed everything I wanted to, let’s wrap it up.'</li><li>'I’ve covered everything I wanted to say and I’m done.'</li></ul>                                                                      |
| nothing       | <ul><li>'I think Aaron Rodgers is better than Tom Brady'</li><li>'I’ve just thought of something else I want to add.'</li><li>'There’s another detail I want to bring up.'</li></ul>                                                                                |
| wrap_question | <ul><li>'I’ve gone through everything relevant, let me know if there’s more to cover.'</li><li>'That’s my take on this, feel free to ask if anything’s unclear.'</li><li>'I think I’ve addressed the question in full, but let me know if you need more.'</li></ul> |
| next_question | <ul><li>'I’ve finished answering this, time for a new topic.'</li><li>'I’m ready to explore a different question.'</li><li>'I think we’ve exhausted this, what’s next on the agenda?'</li></ul>                                                                     |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9189   |

## 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("nksk/Intent_bge-small-en-v1.5_v1.0")
# Run inference
preds = model("Repeat the question for me please")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 8.6319 | 16  |

| Label         | Training Sample Count |
|:--------------|:----------------------|
| end_question  | 31                    |
| next_question | 33                    |
| nothing       | 47                    |
| wrap_question | 33                    |

### Training Hyperparameters
- batch_size: (32, 16)
- num_epochs: (3, 10)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.0005
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: True
- use_amp: True
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0021 | 1    | 0.2202        | -               |
| 0.1040 | 50   | 0.2429        | -               |
| 0.2079 | 100  | 0.1651        | -               |
| 0.3119 | 150  | 0.0268        | -               |
| 0.4158 | 200  | 0.0079        | -               |
| 0.5198 | 250  | 0.0033        | -               |
| 0.6237 | 300  | 0.0031        | -               |
| 0.7277 | 350  | 0.002         | -               |
| 0.8316 | 400  | 0.0022        | -               |
| 0.9356 | 450  | 0.0022        | -               |
| 1.0395 | 500  | 0.002         | -               |
| 1.1435 | 550  | 0.0017        | -               |
| 1.2474 | 600  | 0.0014        | -               |
| 1.3514 | 650  | 0.001         | -               |
| 1.4553 | 700  | 0.0013        | -               |
| 1.5593 | 750  | 0.0013        | -               |
| 1.6632 | 800  | 0.0011        | -               |
| 1.7672 | 850  | 0.0011        | -               |
| 1.8711 | 900  | 0.0014        | -               |
| 1.9751 | 950  | 0.001         | -               |
| 2.0790 | 1000 | 0.0009        | -               |
| 2.1830 | 1050 | 0.001         | -               |
| 2.2869 | 1100 | 0.0009        | -               |
| 2.3909 | 1150 | 0.0008        | -               |
| 2.4948 | 1200 | 0.0009        | -               |
| 2.5988 | 1250 | 0.0011        | -               |
| 2.7027 | 1300 | 0.0009        | -               |
| 2.8067 | 1350 | 0.0009        | -               |
| 2.9106 | 1400 | 0.0009        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.1.1
- Transformers: 4.39.0
- PyTorch: 2.4.1+cu121
- Datasets: 3.0.0
- Tokenizers: 0.15.2

## 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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