---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Enable audio recorder app
- text: Open video camera mode
- text: Show recent chats
- text: Switch to instant camera usage mode
- text: Could you switch to video camera mode?
inference: true
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: accuracy
      value: 1.0
      name: Accuracy
---

# 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:** 3 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                                                                                                                          |
|:-----------|:----------------------------------------------------------------------------------------------------------------------------------|
| microphone | <ul><li>'Launch microphone app'</li><li>'Launch recording app'</li><li>'Access mic app'</li></ul>                                 |
| history    | <ul><li>'View chat logs'</li><li>'Display conversation details'</li><li>'Show history'</li></ul>                                  |
| camera     | <ul><li>'Switch to webcam mode please'</li><li>'Could you switch to video camera mode?'</li><li>'Open the photo webcam'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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("porxelek/word-classification")
# Run inference
preds = model("Show recent chats")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 4.1364 | 10  |

| Label      | Training Sample Count |
|:-----------|:----------------------|
| camera     | 250                   |
| history    | 150                   |
| microphone | 150                   |

### Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- 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: True

### Training Results
| Epoch   | Step     | Training Loss | Validation Loss |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0003  | 1        | 0.1209        | -               |
| 0.0164  | 50       | 0.1449        | -               |
| 0.0328  | 100      | 0.046         | -               |
| 0.0492  | 150      | 0.0099        | -               |
| 0.0656  | 200      | 0.0049        | -               |
| 0.0820  | 250      | 0.0036        | -               |
| 0.0985  | 300      | 0.0022        | -               |
| 0.1149  | 350      | 0.0015        | -               |
| 0.1313  | 400      | 0.0011        | -               |
| 0.1477  | 450      | 0.001         | -               |
| 0.1641  | 500      | 0.0009        | -               |
| 0.1805  | 550      | 0.0009        | -               |
| 0.1969  | 600      | 0.0009        | -               |
| 0.2133  | 650      | 0.0008        | -               |
| 0.2297  | 700      | 0.0007        | -               |
| 0.2461  | 750      | 0.0006        | -               |
| 0.2626  | 800      | 0.0006        | -               |
| 0.2790  | 850      | 0.0006        | -               |
| 0.2954  | 900      | 0.0006        | -               |
| 0.3118  | 950      | 0.0005        | -               |
| 0.3282  | 1000     | 0.0004        | -               |
| 0.3446  | 1050     | 0.0005        | -               |
| 0.3610  | 1100     | 0.0005        | -               |
| 0.3774  | 1150     | 0.0004        | -               |
| 0.3938  | 1200     | 0.0004        | -               |
| 0.4102  | 1250     | 0.0004        | -               |
| 0.4266  | 1300     | 0.0005        | -               |
| 0.4431  | 1350     | 0.0004        | -               |
| 0.4595  | 1400     | 0.0003        | -               |
| 0.4759  | 1450     | 0.0003        | -               |
| 0.4923  | 1500     | 0.0003        | -               |
| 0.5087  | 1550     | 0.0003        | -               |
| 0.5251  | 1600     | 0.0003        | -               |
| 0.5415  | 1650     | 0.0003        | -               |
| 0.5579  | 1700     | 0.0003        | -               |
| 0.5743  | 1750     | 0.0003        | -               |
| 0.5907  | 1800     | 0.0003        | -               |
| 0.6072  | 1850     | 0.0002        | -               |
| 0.6236  | 1900     | 0.0003        | -               |
| 0.6400  | 1950     | 0.0002        | -               |
| 0.6564  | 2000     | 0.0002        | -               |
| 0.6728  | 2050     | 0.0002        | -               |
| 0.6892  | 2100     | 0.0003        | -               |
| 0.7056  | 2150     | 0.0002        | -               |
| 0.7220  | 2200     | 0.0002        | -               |
| 0.7384  | 2250     | 0.0002        | -               |
| 0.7548  | 2300     | 0.0002        | -               |
| 0.7713  | 2350     | 0.0002        | -               |
| 0.7877  | 2400     | 0.0002        | -               |
| 0.8041  | 2450     | 0.0002        | -               |
| 0.8205  | 2500     | 0.0002        | -               |
| 0.8369  | 2550     | 0.0002        | -               |
| 0.8533  | 2600     | 0.0002        | -               |
| 0.8697  | 2650     | 0.0002        | -               |
| 0.8861  | 2700     | 0.0002        | -               |
| 0.9025  | 2750     | 0.0002        | -               |
| 0.9189  | 2800     | 0.0002        | -               |
| 0.9353  | 2850     | 0.0002        | -               |
| 0.9518  | 2900     | 0.0002        | -               |
| 0.9682  | 2950     | 0.0002        | -               |
| 0.9846  | 3000     | 0.0002        | -               |
| **1.0** | **3047** | **-**         | **0.0**         |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.3.1+cu121
- Datasets: 2.20.0
- 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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