
# SetFit with sentence-transformers/paraphrase-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A ClassifierChain 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 ClassifierChain instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **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)

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.7340 |

## 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("CrisisNarratives/setfit-8classes-multi_label")
# Run inference
preds = model("Im sorry.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max  |
|:-------------|:----|:--------|:-----|
| Word count   | 1   | 25.3789 | 1681 |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- body_learning_rate: (1.752e-05, 1.752e-05)
- head_learning_rate: 1.752e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 30
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0004 | 1    | 0.4024        | -               |
| 0.0185 | 50   | 0.2502        | -               |
| 0.0370 | 100  | 0.2222        | -               |
| 0.0555 | 150  | 0.2279        | -               |
| 0.0739 | 200  | 0.2556        | -               |
| 0.0924 | 250  | 0.2444        | -               |
| 0.1109 | 300  | 0.2441        | -               |
| 0.1294 | 350  | 0.2538        | -               |
| 0.1479 | 400  | 0.2245        | -               |
| 0.1664 | 450  | 0.2111        | -               |
| 0.1848 | 500  | 0.1554        | -               |
| 0.2033 | 550  | 0.1361        | -               |
| 0.2218 | 600  | 0.1712        | -               |
| 0.2403 | 650  | 0.1506        | -               |
| 0.2588 | 700  | 0.1175        | -               |
| 0.2773 | 750  | 0.0695        | -               |
| 0.2957 | 800  | 0.0916        | -               |
| 0.3142 | 850  | 0.0884        | -               |
| 0.3327 | 900  | 0.0412        | -               |
| 0.3512 | 950  | 0.1189        | -               |
| 0.3697 | 1000 | 0.0485        | -               |
| 0.3882 | 1050 | 0.1098        | -               |
| 0.4067 | 1100 | 0.0303        | -               |
| 0.4251 | 1150 | 0.0244        | -               |
| 0.4436 | 1200 | 0.0429        | -               |
| 0.4621 | 1250 | 0.034         | -               |
| 0.4806 | 1300 | 0.0725        | -               |
| 0.4991 | 1350 | 0.0438        | -               |
| 0.5176 | 1400 | 0.0124        | -               |
| 0.5360 | 1450 | 0.1603        | -               |
| 0.5545 | 1500 | 0.1134        | -               |
| 0.5730 | 1550 | 0.098         | -               |
| 0.5915 | 1600 | 0.0343        | -               |
| 0.6100 | 1650 | 0.0354        | -               |
| 0.6285 | 1700 | 0.0892        | -               |
| 0.6470 | 1750 | 0.0137        | -               |
| 0.6654 | 1800 | 0.071         | -               |
| 0.6839 | 1850 | 0.0317        | -               |
| 0.7024 | 1900 | 0.0285        | -               |
| 0.7209 | 1950 | 0.0311        | -               |
| 0.7394 | 2000 | 0.0755        | -               |
| 0.7579 | 2050 | 0.09          | -               |
| 0.7763 | 2100 | 0.0565        | -               |
| 0.7948 | 2150 | 0.0099        | -               |
| 0.8133 | 2200 | 0.0236        | -               |
| 0.8318 | 2250 | 0.0663        | -               |
| 0.8503 | 2300 | 0.1391        | -               |
| 0.8688 | 2350 | 0.0176        | -               |
| 0.8872 | 2400 | 0.0645        | -               |
| 0.9057 | 2450 | 0.0318        | -               |
| 0.9242 | 2500 | 0.0186        | -               |
| 0.9427 | 2550 | 0.0514        | -               |
| 0.9612 | 2600 | 0.0261        | -               |
| 0.9797 | 2650 | 0.0535        | -               |
| 0.9982 | 2700 | 0.018         | -               |
| 1.0166 | 2750 | 0.0218        | -               |
| 1.0351 | 2800 | 0.0351        | -               |
| 1.0536 | 2850 | 0.0704        | -               |
| 1.0721 | 2900 | 0.0251        | -               |
| 1.0906 | 2950 | 0.0156        | -               |
| 1.1091 | 3000 | 0.0821        | -               |
| 1.1275 | 3050 | 0.0273        | -               |
| 1.1460 | 3100 | 0.0719        | -               |
| 1.1645 | 3150 | 0.0496        | -               |
| 1.1830 | 3200 | 0.0124        | -               |
| 1.2015 | 3250 | 0.0576        | -               |
| 1.2200 | 3300 | 0.0453        | -               |
| 1.2384 | 3350 | 0.0236        | -               |
| 1.2569 | 3400 | 0.013         | -               |
| 1.2754 | 3450 | 0.0909        | -               |
| 1.2939 | 3500 | 0.024         | -               |
| 1.3124 | 3550 | 0.0264        | -               |
| 1.3309 | 3600 | 0.0397        | -               |
| 1.3494 | 3650 | 0.0484        | -               |
| 1.3678 | 3700 | 0.0301        | -               |
| 1.3863 | 3750 | 0.0512        | -               |
| 1.4048 | 3800 | 0.0625        | -               |
| 1.4233 | 3850 | 0.0583        | -               |
| 1.4418 | 3900 | 0.0506        | -               |
| 1.4603 | 3950 | 0.0561        | -               |
| 1.4787 | 4000 | 0.0295        | -               |
| 1.4972 | 4050 | 0.1352        | -               |
| 1.5157 | 4100 | 0.0101        | -               |
| 1.5342 | 4150 | 0.0221        | -               |
| 1.5527 | 4200 | 0.057         | -               |
| 1.5712 | 4250 | 0.0389        | -               |
| 1.5896 | 4300 | 0.0173        | -               |
| 1.6081 | 4350 | 0.0605        | -               |
| 1.6266 | 4400 | 0.0187        | -               |
| 1.6451 | 4450 | 0.0401        | -               |
| 1.6636 | 4500 | 0.0571        | -               |
| 1.6821 | 4550 | 0.0612        | -               |
| 1.7006 | 4600 | 0.03          | -               |
| 1.7190 | 4650 | 0.0299        | -               |
| 1.7375 | 4700 | 0.0583        | -               |
| 1.7560 | 4750 | 0.0279        | -               |
| 1.7745 | 4800 | 0.027         | -               |
| 1.7930 | 4850 | 0.0343        | -               |
| 1.8115 | 4900 | 0.0634        | -               |
| 1.8299 | 4950 | 0.0748        | -               |
| 1.8484 | 5000 | 0.0699        | -               |
| 1.8669 | 5050 | 0.0678        | -               |
| 1.8854 | 5100 | 0.0724        | -               |
| 1.9039 | 5150 | 0.0211        | -               |
| 1.9224 | 5200 | 0.037         | -               |
| 1.9409 | 5250 | 0.0891        | -               |
| 1.9593 | 5300 | 0.0235        | -               |
| 1.9778 | 5350 | 0.0339        | -               |
| 1.9963 | 5400 | 0.029         | -               |
| 2.0148 | 5450 | 0.1292        | -               |
| 2.0333 | 5500 | 0.0457        | -               |
| 2.0518 | 5550 | 0.0577        | -               |
| 2.0702 | 5600 | 0.063         | -               |
| 2.0887 | 5650 | 0.0198        | -               |
| 2.1072 | 5700 | 0.0367        | -               |
| 2.1257 | 5750 | 0.0475        | -               |
| 2.1442 | 5800 | 0.0368        | -               |
| 2.1627 | 5850 | 0.0401        | -               |
| 2.1811 | 5900 | 0.0353        | -               |
| 2.1996 | 5950 | 0.0387        | -               |
| 2.2181 | 6000 | 0.0325        | -               |
| 2.2366 | 6050 | 0.046         | -               |
| 2.2551 | 6100 | 0.03          | -               |
| 2.2736 | 6150 | 0.0338        | -               |
| 2.2921 | 6200 | 0.0374        | -               |
| 2.3105 | 6250 | 0.0206        | -               |
| 2.3290 | 6300 | 0.031         | -               |
| 2.3475 | 6350 | 0.0493        | -               |
| 2.3660 | 6400 | 0.0182        | -               |
| 2.3845 | 6450 | 0.0352        | -               |
| 2.4030 | 6500 | 0.0622        | -               |
| 2.4214 | 6550 | 0.0682        | -               |
| 2.4399 | 6600 | 0.0227        | -               |
| 2.4584 | 6650 | 0.0401        | -               |
| 2.4769 | 6700 | 0.0348        | -               |
| 2.4954 | 6750 | 0.0417        | -               |
| 2.5139 | 6800 | 0.0232        | -               |
| 2.5323 | 6850 | 0.0603        | -               |
| 2.5508 | 6900 | 0.0981        | -               |
| 2.5693 | 6950 | 0.0433        | -               |
| 2.5878 | 7000 | 0.0187        | -               |
| 2.6063 | 7050 | 0.0099        | -               |
| 2.6248 | 7100 | 0.0276        | -               |
| 2.6433 | 7150 | 0.0516        | -               |
| 2.6617 | 7200 | 0.0211        | -               |
| 2.6802 | 7250 | 0.0191        | -               |
| 2.6987 | 7300 | 0.1152        | -               |
| 2.7172 | 7350 | 0.0442        | -               |
| 2.7357 | 7400 | 0.0226        | -               |
| 2.7542 | 7450 | 0.0429        | -               |
| 2.7726 | 7500 | 0.0313        | -               |
| 2.7911 | 7550 | 0.0601        | -               |
| 2.8096 | 7600 | 0.0156        | -               |
| 2.8281 | 7650 | 0.039         | -               |
| 2.8466 | 7700 | 0.0239        | -               |
| 2.8651 | 7750 | 0.1159        | -               |
| 2.8835 | 7800 | 0.0223        | -               |
| 2.9020 | 7850 | 0.0442        | -               |
| 2.9205 | 7900 | 0.0254        | -               |
| 2.9390 | 7950 | 0.0268        | -               |
| 2.9575 | 8000 | 0.0415        | -               |
| 2.9760 | 8050 | 0.0235        | -               |
| 2.9945 | 8100 | 0.0177        | -               |

### Framework Versions
- Python: 3.9.16
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.0
- PyTorch: 2.1.0+cu121
- Datasets: 2.14.6
- Tokenizers: 0.14.1

## 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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{"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["metric"], "widget": [{"text": "Damn, my condolences to you bro"}, {"text": "No Friday Im booked all day"}, {"text": "Im sorry."}, {"text": "Hiding in the bush"}, {"text": "*\"The conservative party is a cult.\" Says the group that bans words and follows socialism.??*"}], "pipeline_tag": "text-classification", "inference": false, "base_model": "sentence-transformers/paraphrase-mpnet-base-v2", "model-index": [{"name": "SetFit with sentence-transformers/paraphrase-mpnet-base-v2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "Unknown", "type": "unknown", "split": "test"}, "metrics": [{"type": "metric", "value": 0.7340375623557441, "name": "Metric"}]}]}]}
setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-mpnet-base-v2 model-index region:us