---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
datasets:
- go_emotions
metrics:
- accuracy
widget:
- text: Curious as to why he's been passed up so many times now.
- text: I think you mean the announcement
- text: try to attract the guy that i like. other than that i love gaming drawing
    writing and watching tv.
- text: I thought that phrase was only used for memes now lol at least that's what
    I got from Vic deals
- text: 'Fantastic read, thanks for the insights! '
pipeline_tag: text-classification
inference: false
---

# SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier 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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
- **Training Dataset:** [go_emotions](https://huggingface.co/datasets/go_emotions)
<!-- - **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)

## 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("bhaskars113/go-emotions-multilabel")
# Run inference
preds = model("I think you mean the announcement")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 13.6060 | 30  |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0004 | 1    | 0.3873        | -               |
| 0.0223 | 50   | 0.2243        | -               |
| 0.0446 | 100  | 0.2305        | -               |
| 0.0670 | 150  | 0.2297        | -               |
| 0.0893 | 200  | 0.2758        | -               |
| 0.1116 | 250  | 0.2197        | -               |
| 0.1339 | 300  | 0.1984        | -               |
| 0.1562 | 350  | 0.1729        | -               |
| 0.1786 | 400  | 0.1244        | -               |
| 0.2009 | 450  | 0.164         | -               |
| 0.2232 | 500  | 0.1587        | -               |
| 0.2455 | 550  | 0.2272        | -               |
| 0.2679 | 600  | 0.3367        | -               |
| 0.2902 | 650  | 0.1715        | -               |
| 0.3125 | 700  | 0.2213        | -               |
| 0.3348 | 750  | 0.2394        | -               |
| 0.3571 | 800  | 0.1275        | -               |
| 0.3795 | 850  | 0.1919        | -               |
| 0.4018 | 900  | 0.143         | -               |
| 0.4241 | 950  | 0.2431        | -               |
| 0.4464 | 1000 | 0.1747        | -               |
| 0.4688 | 1050 | 0.1567        | -               |
| 0.4911 | 1100 | 0.194         | -               |
| 0.5134 | 1150 | 0.1895        | -               |
| 0.5357 | 1200 | 0.1601        | -               |
| 0.5580 | 1250 | 0.1042        | -               |
| 0.5804 | 1300 | 0.0553        | -               |
| 0.6027 | 1350 | 0.1614        | -               |
| 0.625  | 1400 | 0.1854        | -               |
| 0.6473 | 1450 | 0.1259        | -               |
| 0.6696 | 1500 | 0.138         | -               |
| 0.6920 | 1550 | 0.2181        | -               |
| 0.7143 | 1600 | 0.1144        | -               |
| 0.7366 | 1650 | 0.1987        | -               |
| 0.7589 | 1700 | 0.0859        | -               |
| 0.7812 | 1750 | 0.1665        | -               |
| 0.8036 | 1800 | 0.1628        | -               |
| 0.8259 | 1850 | 0.2296        | -               |
| 0.8482 | 1900 | 0.1892        | -               |
| 0.8705 | 1950 | 0.2033        | -               |
| 0.8929 | 2000 | 0.1507        | -               |
| 0.9152 | 2050 | 0.1592        | -               |
| 0.9375 | 2100 | 0.1077        | -               |
| 0.9598 | 2150 | 0.1415        | -               |
| 0.9821 | 2200 | 0.1561        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.0
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- 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}
}
```

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