README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 'Wonderful person aboard!'
- text: jan o lukin ala e pilin sina.
- text: Nothing…I’m just loudly complaining, I’ll get over it tomorrow.
- text: HEY THERE
- text: Pizza cutter 2
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-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/paraphrase-multilingual-MiniLM-L12-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 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)

### Model Labels
| Label     | Examples                                                                                                                                                                                                                                  |
|:----------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| toki pona | <ul><li>'ona li toki "toki" tawa meli.'</li><li>'toki li pona tawa mi.'</li><li>'mi toki e ni tawa ona: "o kama tawa tomo mi."'</li></ul>                                                                                                 |
| other     | <ul><li>'No te puedo creer el grado de precisión 🤣'</li><li>'i can’t deny i’m invested in the aspect of things :’)'</li><li>"I'm live on #twitch, and speedrunning EarthBound!"</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("johnpaulbin/toki-pona-classifier-v2")
# Run inference
preds = model(["Hello!", "toki!"])
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 10.5705 | 61  |

| Label     | Training Sample Count |
|:----------|:----------------------|
| other     | 2035                  |
| toki pona | 2000                  |

### Training Hyperparameters
- batch_size: (12, 12)
- num_epochs: (2, 2)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0015 | 1    | 0.3252        | -               |
| 0.0743 | 50   | 0.2704        | -               |
| 0.1486 | 100  | 0.2257        | -               |
| 0.2229 | 150  | 0.0567        | -               |
| 0.2972 | 200  | 0.0063        | -               |
| 0.3715 | 250  | 0.0015        | -               |
| 0.4458 | 300  | 0.0034        | -               |
| 0.5201 | 350  | 0.0026        | -               |
| 0.5944 | 400  | 0.0036        | -               |
| 0.6686 | 450  | 0.0005        | -               |
| 0.7429 | 500  | 0.0021        | -               |
| 0.8172 | 550  | 0.0021        | -               |
| 0.8915 | 600  | 0.0003        | -               |
| 0.9658 | 650  | 0.0002        | -               |
| 1.0401 | 700  | 0.0002        | -               |
| 1.1144 | 750  | 0.0018        | -               |
| 1.1887 | 800  | 0.0003        | -               |
| 1.2630 | 850  | 0.0002        | -               |
| 1.3373 | 900  | 0.0001        | -               |
| 1.4116 | 950  | 0.0015        | -               |
| 1.4859 | 1000 | 0.0004        | -               |
| 1.5602 | 1050 | 0.0001        | -               |
| 1.6345 | 1100 | 0.0001        | -               |
| 1.7088 | 1150 | 0.0019        | -               |
| 1.7831 | 1200 | 0.0001        | -               |
| 1.8574 | 1250 | 0.0001        | -               |
| 1.9316 | 1300 | 0.0001        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.2.1
- Transformers: 4.42.2
- PyTorch: 2.5.1+cu121
- Datasets: 3.1.0
- 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->