---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- metric
widget:
- text: A combined 20 million people per year die of smoking and hunger,  so authorities
    can't seem to feed people and they allow you to buy cigarettes but we are facing
    another lockdown for a virus that has a 99.5% survival rate!!! THINK PEOPLE. LOOK
    AT IT LOGICALLY WITH YOUR OWN EYES.
- text: Scientists do not agree on the consequences of climate change, nor is there
    any consensus on that subject. The predictions on that from are just ascientific
    speculation. Bring on the warming."
- text: If Tam is our "top doctor"....I am going back to leaches and voodoo...just
    as much science in that as the crap she spouts
- text: "Can she skip school by herself and sit infront of parliament? \r\n Fake emotions\
    \ and just a good actor."
- text: my dad had huge ones..so they may be real..
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: 0.688144336139226
      name: Metric
---

# 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** | 0.6881 |

## 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-9classes-multi_label")
# Run inference
preds = model("my dad had huge ones..so they may be real..")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max  |
|:-------------|:----|:--------|:-----|
| Word count   | 1   | 25.8891 | 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    | 0.3395        | -               |
| 0.0185 | 50   | 0.3628        | -               |
| 0.0370 | 100  | 0.2538        | -               |
| 0.0555 | 150  | 0.2044        | -               |
| 0.0739 | 200  | 0.1831        | -               |
| 0.0924 | 250  | 0.2218        | -               |
| 0.1109 | 300  | 0.2014        | -               |
| 0.1294 | 350  | 0.2405        | -               |
| 0.1479 | 400  | 0.1238        | -               |
| 0.1664 | 450  | 0.1658        | -               |
| 0.1848 | 500  | 0.1974        | -               |
| 0.2033 | 550  | 0.1565        | -               |
| 0.2218 | 600  | 0.1131        | -               |
| 0.2403 | 650  | 0.0994        | -               |
| 0.2588 | 700  | 0.0743        | -               |
| 0.2773 | 750  | 0.0259        | -               |
| 0.2957 | 800  | 0.1852        | -               |
| 0.3142 | 850  | 0.1896        | -               |
| 0.3327 | 900  | 0.1102        | -               |
| 0.3512 | 950  | 0.0951        | -               |
| 0.3697 | 1000 | 0.0619        | -               |
| 0.3882 | 1050 | 0.0227        | -               |
| 0.4067 | 1100 | 0.0986        | -               |
| 0.4251 | 1150 | 0.0375        | -               |
| 0.4436 | 1200 | 0.1151        | -               |
| 0.4621 | 1250 | 0.1128        | -               |
| 0.4806 | 1300 | 0.0334        | -               |
| 0.4991 | 1350 | 0.1012        | -               |
| 0.5176 | 1400 | 0.0895        | -               |
| 0.5360 | 1450 | 0.072         | -               |
| 0.5545 | 1500 | 0.0619        | -               |
| 0.5730 | 1550 | 0.0852        | -               |
| 0.5915 | 1600 | 0.0611        | -               |
| 0.6100 | 1650 | 0.0679        | -               |
| 0.6285 | 1700 | 0.0238        | -               |
| 0.6470 | 1750 | 0.1776        | -               |
| 0.6654 | 1800 | 0.081         | -               |
| 0.6839 | 1850 | 0.1059        | -               |
| 0.7024 | 1900 | 0.045         | -               |
| 0.7209 | 1950 | 0.0664        | -               |
| 0.7394 | 2000 | 0.0666        | -               |
| 0.7579 | 2050 | 0.0714        | -               |
| 0.7763 | 2100 | 0.0312        | -               |
| 0.7948 | 2150 | 0.0461        | -               |
| 0.8133 | 2200 | 0.0946        | -               |
| 0.8318 | 2250 | 0.047         | -               |
| 0.8503 | 2300 | 0.0906        | -               |
| 0.8688 | 2350 | 0.0186        | -               |
| 0.8872 | 2400 | 0.0937        | -               |
| 0.9057 | 2450 | 0.1674        | -               |
| 0.9242 | 2500 | 0.0311        | -               |
| 0.9427 | 2550 | 0.0884        | -               |
| 0.9612 | 2600 | 0.0787        | -               |
| 0.9797 | 2650 | 0.192         | -               |
| 0.9982 | 2700 | 0.0689        | -               |
| 1.0166 | 2750 | 0.0945        | -               |
| 1.0351 | 2800 | 0.066         | -               |
| 1.0536 | 2850 | 0.0592        | -               |
| 1.0721 | 2900 | 0.068         | -               |
| 1.0906 | 2950 | 0.0619        | -               |
| 1.1091 | 3000 | 0.0329        | -               |
| 1.1275 | 3050 | 0.0986        | -               |
| 1.1460 | 3100 | 0.0468        | -               |
| 1.1645 | 3150 | 0.0717        | -               |
| 1.1830 | 3200 | 0.0721        | -               |
| 1.2015 | 3250 | 0.0345        | -               |
| 1.2200 | 3300 | 0.0317        | -               |
| 1.2384 | 3350 | 0.0476        | -               |
| 1.2569 | 3400 | 0.122         | -               |
| 1.2754 | 3450 | 0.0576        | -               |
| 1.2939 | 3500 | 0.0375        | -               |
| 1.3124 | 3550 | 0.1074        | -               |
| 1.3309 | 3600 | 0.113         | -               |
| 1.3494 | 3650 | 0.0564        | -               |
| 1.3678 | 3700 | 0.0437        | -               |
| 1.3863 | 3750 | 0.0623        | -               |
| 1.4048 | 3800 | 0.0213        | -               |
| 1.4233 | 3850 | 0.0629        | -               |
| 1.4418 | 3900 | 0.059         | -               |
| 1.4603 | 3950 | 0.0807        | -               |
| 1.4787 | 4000 | 0.0946        | -               |
| 1.4972 | 4050 | 0.0381        | -               |
| 1.5157 | 4100 | 0.0451        | -               |
| 1.5342 | 4150 | 0.0742        | -               |
| 1.5527 | 4200 | 0.0899        | -               |
| 1.5712 | 4250 | 0.0722        | -               |
| 1.5896 | 4300 | 0.1022        | -               |
| 1.6081 | 4350 | 0.0446        | -               |
| 1.6266 | 4400 | 0.022         | -               |
| 1.6451 | 4450 | 0.0586        | -               |
| 1.6636 | 4500 | 0.0585        | -               |
| 1.6821 | 4550 | 0.0409        | -               |
| 1.7006 | 4600 | 0.0253        | -               |
| 1.7190 | 4650 | 0.0363        | -               |
| 1.7375 | 4700 | 0.0492        | -               |
| 1.7560 | 4750 | 0.0154        | -               |
| 1.7745 | 4800 | 0.0427        | -               |
| 1.7930 | 4850 | 0.0284        | -               |
| 1.8115 | 4900 | 0.022         | -               |
| 1.8299 | 4950 | 0.0335        | -               |
| 1.8484 | 5000 | 0.0222        | -               |
| 1.8669 | 5050 | 0.0291        | -               |
| 1.8854 | 5100 | 0.0824        | -               |
| 1.9039 | 5150 | 0.0563        | -               |
| 1.9224 | 5200 | 0.0355        | -               |
| 1.9409 | 5250 | 0.064         | -               |
| 1.9593 | 5300 | 0.0596        | -               |
| 1.9778 | 5350 | 0.0789        | -               |
| 1.9963 | 5400 | 0.0901        | -               |
| 2.0148 | 5450 | 0.0388        | -               |
| 2.0333 | 5500 | 0.0738        | -               |
| 2.0518 | 5550 | 0.0712        | -               |
| 2.0702 | 5600 | 0.0825        | -               |
| 2.0887 | 5650 | 0.0406        | -               |
| 2.1072 | 5700 | 0.0623        | -               |
| 2.1257 | 5750 | 0.0423        | -               |
| 2.1442 | 5800 | 0.0566        | -               |
| 2.1627 | 5850 | 0.0745        | -               |
| 2.1811 | 5900 | 0.0271        | -               |
| 2.1996 | 5950 | 0.0257        | -               |
| 2.2181 | 6000 | 0.0347        | -               |
| 2.2366 | 6050 | 0.0291        | -               |
| 2.2551 | 6100 | 0.0401        | -               |
| 2.2736 | 6150 | 0.0222        | -               |
| 2.2921 | 6200 | 0.0217        | -               |
| 2.3105 | 6250 | 0.0589        | -               |
| 2.3290 | 6300 | 0.0685        | -               |
| 2.3475 | 6350 | 0.1191        | -               |
| 2.3660 | 6400 | 0.0626        | -               |
| 2.3845 | 6450 | 0.0615        | -               |
| 2.4030 | 6500 | 0.0327        | -               |
| 2.4214 | 6550 | 0.0431        | -               |
| 2.4399 | 6600 | 0.1037        | -               |
| 2.4584 | 6650 | 0.0318        | -               |
| 2.4769 | 6700 | 0.062         | -               |
| 2.4954 | 6750 | 0.0183        | -               |
| 2.5139 | 6800 | 0.0568        | -               |
| 2.5323 | 6850 | 0.0581        | -               |
| 2.5508 | 6900 | 0.0363        | -               |
| 2.5693 | 6950 | 0.0413        | -               |
| 2.5878 | 7000 | 0.076         | -               |
| 2.6063 | 7050 | 0.046         | -               |
| 2.6248 | 7100 | 0.0401        | -               |
| 2.6433 | 7150 | 0.0552        | -               |
| 2.6617 | 7200 | 0.0767        | -               |
| 2.6802 | 7250 | 0.0167        | -               |
| 2.6987 | 7300 | 0.0459        | -               |
| 2.7172 | 7350 | 0.0306        | -               |
| 2.7357 | 7400 | 0.0559        | -               |
| 2.7542 | 7450 | 0.0688        | -               |
| 2.7726 | 7500 | 0.0417        | -               |
| 2.7911 | 7550 | 0.033         | -               |
| 2.8096 | 7600 | 0.0404        | -               |
| 2.8281 | 7650 | 0.0391        | -               |
| 2.8466 | 7700 | 0.0254        | -               |
| 2.8651 | 7750 | 0.0635        | -               |
| 2.8835 | 7800 | 0.0739        | -               |
| 2.9020 | 7850 | 0.0274        | -               |
| 2.9205 | 7900 | 0.0394        | -               |
| 2.9390 | 7950 | 0.0606        | -               |
| 2.9575 | 8000 | 0.0098        | -               |
| 2.9760 | 8050 | 0.0997        | -               |
| 2.9945 | 8100 | 0.0369        | -               |

### 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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