---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: '"I think this might be the solution."'
- text: '"Oh no, I apologize!"'
- text: Could you repeat that, please?
- text: Oh, this is so disappointing.
- text: Uhh, clear.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
datasets:
- rbojja/zero-shot-intent-classification
base_model: BAAI/bge-small-en-v1.5
---

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

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [rbojja/zero-shot-intent-classification](https://huggingface.co/datasets/rbojja/zero-shot-intent-classification) 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 [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:** 18 classes
- **Training Dataset:** [rbojja/zero-shot-intent-classification](https://huggingface.co/datasets/rbojja/zero-shot-intent-classification)
<!-- - **Language:** Unknown -->
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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                                                                                                                                                 |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------|
| 7     | <ul><li>'Oh my, this is great!'</li><li>'Oh, this is fantastic!'</li><li>'Hmm, I’m so delighted!'</li></ul>                                              |
| 3     | <ul><li>"Oh, absolutely, that's it!"</li><li>"Oh, absolutely, that's it!"</li><li>"Yep, that's exactly what I meant."</li></ul>                          |
| 15    | <ul><li>'Really, no way?'</li><li>'Oh, that’s quite something!'</li><li>'Oh, that’s quite something!'</li></ul>                                          |
| 8     | <ul><li>"Gotcha... oh, that's clear!"</li><li>'Hmm, I see... perfect!'</li><li>'Oh, I see... clear!'</li></ul>                                           |
| 12    | <ul><li>'Uhh, fine.'</li><li>'Oh, clear.'</li><li>'Uhh, noted.'</li></ul>                                                                                |
| 9     | <ul><li>'Uhh, take care!'</li><li>'Hmm, see you!'</li><li>'Uhh, see you!'</li></ul>                                                                      |
| 17    | <ul><li>'"Umm, this could be a decent plan."'</li><li>'"I think this might be the solution."'</li><li>'"Maybe this will work out, I suppose."'</li></ul> |
| 0     | <ul><li>"Why can't you just work?!"</li><li>'Seriously, this is a joke!'</li><li>'Ugh, this is so frustrating!'</li></ul>                                |
| 6     | <ul><li>'"Oh, what if I\'m a dream?"'</li><li>'"Oh, do you speak dolphin?"'</li><li>'"Uhh, do you have a wish?"'</li></ul>                               |
| 11    | <ul><li>"Uh-huh, that's a valid point."</li><li>'Like, I get it.'</li><li>'Right, I understand.'</li></ul>                                               |
| 16    | <ul><li>'Thank you!'</li><li>'"Hmmm, thanks, you\'re great!"'</li><li>'"Oh, fantastic, thanks a lot!"'</li></ul>                                         |
| 4     | <ul><li>"Sorry, I'm not sure."</li><li>"Well, I'm lost."</li><li>"Hmm, I'm not sure."</li></ul>                                                          |
| 10    | <ul><li>'Oh, hi!'</li><li>"Hello! What's new?"</li><li>"Hi! How's life?"</li></ul>                                                                       |
| 13    | <ul><li>'Oh, gotcha.'</li><li>'Hmmm, okay.'</li><li>'Alright, thanks.'</li></ul>                                                                         |
| 2     | <ul><li>'What’s the context behind that?'</li><li>'Could you simplify that for me?'</li><li>'Can you explain that concept?'</li></ul>                    |
| 1     | <ul><li>'"Oh, I didn’t mean to."'</li><li>'"Oops, sorry for the oversight."'</li><li>'"Oops, I’m really sorry."'</li></ul>                               |
| 5     | <ul><li>'Oh, this is not what I wanted.'</li><li>'Oh no, this is not right.'</li><li>'Seriously, this is a failure.'</li></ul>                           |
| 14    | <ul><li>'Uhh, superb choice!'</li><li>'Uhh, amazing decision!'</li><li>'Oh, superb performance!'</li></ul>                                               |

## 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("rbojja/intent-classification-small")
# Run inference
preds = model("Uhh, clear.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 4.2224 | 9   |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 40                    |
| 1     | 40                    |
| 2     | 37                    |
| 3     | 40                    |
| 4     | 41                    |
| 5     | 38                    |
| 6     | 42                    |
| 7     | 38                    |
| 8     | 35                    |
| 9     | 39                    |
| 10    | 42                    |
| 11    | 41                    |
| 12    | 42                    |
| 13    | 44                    |
| 14    | 38                    |
| 15    | 43                    |
| 16    | 47                    |
| 17    | 37                    |

### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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
- 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    | 0.149         | -               |
| 0.0276 | 50   | 0.1836        | -               |
| 0.0552 | 100  | 0.1408        | -               |
| 0.0829 | 150  | 0.0978        | -               |
| 0.1105 | 200  | 0.0805        | -               |
| 0.1381 | 250  | 0.0684        | -               |
| 0.1657 | 300  | 0.0594        | -               |
| 0.1934 | 350  | 0.051         | -               |
| 0.2210 | 400  | 0.0383        | -               |
| 0.2486 | 450  | 0.0379        | -               |
| 0.2762 | 500  | 0.035         | -               |
| 0.3039 | 550  | 0.0334        | -               |
| 0.3315 | 600  | 0.0306        | -               |
| 0.3591 | 650  | 0.0266        | -               |
| 0.3867 | 700  | 0.0264        | -               |
| 0.4144 | 750  | 0.018         | -               |
| 0.4420 | 800  | 0.0193        | -               |
| 0.4696 | 850  | 0.0166        | -               |
| 0.4972 | 900  | 0.0165        | -               |
| 0.5249 | 950  | 0.016         | -               |
| 0.5525 | 1000 | 0.0177        | -               |
| 0.5801 | 1050 | 0.0202        | -               |
| 0.6077 | 1100 | 0.0133        | -               |
| 0.6354 | 1150 | 0.014         | -               |
| 0.6630 | 1200 | 0.013         | -               |
| 0.6906 | 1250 | 0.0161        | -               |
| 0.7182 | 1300 | 0.0119        | -               |
| 0.7459 | 1350 | 0.0132        | -               |
| 0.7735 | 1400 | 0.0131        | -               |
| 0.8011 | 1450 | 0.0123        | -               |
| 0.8287 | 1500 | 0.0115        | -               |
| 0.8564 | 1550 | 0.0111        | -               |
| 0.8840 | 1600 | 0.011         | -               |
| 0.9116 | 1650 | 0.01          | -               |
| 0.9392 | 1700 | 0.0098        | -               |
| 0.9669 | 1750 | 0.0142        | -               |
| 0.9945 | 1800 | 0.0132        | -               |

### Framework Versions
- Python: 3.11.11
- SetFit: 1.1.1
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu121
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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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