---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: delivery and food preparation was suoer fast. nice
- text: Hard, stale cookies that were probably sitting out for days.
- text: I ordered an extra side of guacamole that never arrived with my meal.
- text: Kulang talaga ang mga sangkap, mukhang hindi kumpleto ang aking order.
- text: The steak was so overcooked and tough, I couldn't even cut through it with
    a knife.
pipeline_tag: text-classification
inference: true
base_model: meedan/paraphrase-filipino-mpnet-base-v2
---

# SetFit with meedan/paraphrase-filipino-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [meedan/paraphrase-filipino-mpnet-base-v2](https://huggingface.co/meedan/paraphrase-filipino-mpnet-base-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:** [meedan/paraphrase-filipino-mpnet-base-v2](https://huggingface.co/meedan/paraphrase-filipino-mpnet-base-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:** 6 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/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                                                                                                                                                                                                                                                                                            |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>'I specifically asked for no onions, yet my sandwich was loaded with them when delivered.'</li><li>'The delivery driver spilled half my order all over the bag. What a mess!'</li><li>'Two hour wait only for my pizza to arrive burnt on the bottom from sitting too long.'</li></ul>      |
| 2     | <ul><li>'Found a long strand of hair hanging out of my sealed takeout burger container.'</li><li>'Bits of plastic were baked into the crust of the takeout pizza I received.'</li><li>'The takeout container for my soup was leaking and left a trail of foul-smelling liquid.'</li></ul>           |
| 1     | <ul><li>'Sobrang luto at tigas na para bang kahoy ang aking karne.'</li><li>'Sobrang lata ng pagkaluto, hindi na makain ang aking litsong manok.'</li><li>'Pizza crust was burnt black on the bottom yet still doughy raw on top.'</li></ul>                                                        |
| 3     | <ul><li>'Half the ingredients were missing from my order like they forgot to include them.'</li><li>'Binayaran ko ang dami, pero napakaliit lang ng portion size na naibigay sa akin.'</li><li>'The plate looked full but it was all rice, with small paltry portions of the main items.'</li></ul> |
| 4     | <ul><li>'Bland, overcooked chicken, soggy vegetables and hard, stale naan bread.'</li><li>'Tiny portion sizes, freezing cold plates, and a hair baked into the bread.'</li><li>'Every single thing I tried to order was met with confusion, attitude and mistakes.'</li></ul>                       |
| 5     | <ul><li>'From the appetizer to dessert, everything was prepared flawlessly. 10/10!'</li><li>"The chilaquiles were authentic, flavor-packed and easily the best I've had."</li><li>'You can really taste the freshness of the local ingredients in every bite.'</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("bsen26/eyeR-classification-model-1.0")
# Run inference
preds = model("delivery and food preparation was suoer fast. nice")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 12.6833 | 17  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 20                    |
| 1     | 20                    |
| 2     | 20                    |
| 3     | 20                    |
| 4     | 20                    |
| 5     | 20                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0033 | 1    | 0.2048        | -               |
| 0.1667 | 50   | 0.048         | -               |
| 0.3333 | 100  | 0.0148        | -               |
| 0.5    | 150  | 0.0011        | -               |
| 0.6667 | 200  | 0.0009        | -               |
| 0.8333 | 250  | 0.0005        | -               |
| 1.0    | 300  | 0.0008        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.6.1
- Transformers: 4.38.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.18.0
- 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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