
# 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 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:** [Unknown](https://huggingface.co/datasets/unknown) -->
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<!-- - **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   | Accuracy |
|:--------|:---------|
| **all** | 0.6021   |

## 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("anismahmahi/G2_replace_Whata_repetition_with_noPropaganda_SetFit")
# Run inference
preds = model("Columbus police are investigating the shootings.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 23.1093 | 129 |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch   | Step     | Training Loss | Validation Loss |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0002  | 1        | 0.3592        | -               |
| 0.0121  | 50       | 0.2852        | -               |
| 0.0243  | 100      | 0.2694        | -               |
| 0.0364  | 150      | 0.2182        | -               |
| 0.0486  | 200      | 0.2224        | -               |
| 0.0607  | 250      | 0.2634        | -               |
| 0.0729  | 300      | 0.2431        | -               |
| 0.0850  | 350      | 0.2286        | -               |
| 0.0971  | 400      | 0.197         | -               |
| 0.1093  | 450      | 0.2466        | -               |
| 0.1214  | 500      | 0.2374        | -               |
| 0.1336  | 550      | 0.2134        | -               |
| 0.1457  | 600      | 0.2092        | -               |
| 0.1578  | 650      | 0.1987        | -               |
| 0.1700  | 700      | 0.2288        | -               |
| 0.1821  | 750      | 0.1562        | -               |
| 0.1943  | 800      | 0.27          | -               |
| 0.2064  | 850      | 0.1314        | -               |
| 0.2186  | 900      | 0.2144        | -               |
| 0.2307  | 950      | 0.184         | -               |
| 0.2428  | 1000     | 0.2069        | -               |
| 0.2550  | 1050     | 0.1773        | -               |
| 0.2671  | 1100     | 0.0704        | -               |
| 0.2793  | 1150     | 0.1139        | -               |
| 0.2914  | 1200     | 0.2398        | -               |
| 0.3035  | 1250     | 0.0672        | -               |
| 0.3157  | 1300     | 0.1321        | -               |
| 0.3278  | 1350     | 0.0803        | -               |
| 0.3400  | 1400     | 0.0589        | -               |
| 0.3521  | 1450     | 0.0428        | -               |
| 0.3643  | 1500     | 0.0886        | -               |
| 0.3764  | 1550     | 0.0839        | -               |
| 0.3885  | 1600     | 0.1843        | -               |
| 0.4007  | 1650     | 0.0375        | -               |
| 0.4128  | 1700     | 0.114         | -               |
| 0.4250  | 1750     | 0.1264        | -               |
| 0.4371  | 1800     | 0.0585        | -               |
| 0.4492  | 1850     | 0.0586        | -               |
| 0.4614  | 1900     | 0.0805        | -               |
| 0.4735  | 1950     | 0.0686        | -               |
| 0.4857  | 2000     | 0.0684        | -               |
| 0.4978  | 2050     | 0.0803        | -               |
| 0.5100  | 2100     | 0.076         | -               |
| 0.5221  | 2150     | 0.0888        | -               |
| 0.5342  | 2200     | 0.1091        | -               |
| 0.5464  | 2250     | 0.038         | -               |
| 0.5585  | 2300     | 0.0674        | -               |
| 0.5707  | 2350     | 0.0562        | -               |
| 0.5828  | 2400     | 0.0603        | -               |
| 0.5949  | 2450     | 0.0669        | -               |
| 0.6071  | 2500     | 0.0829        | -               |
| 0.6192  | 2550     | 0.1442        | -               |
| 0.6314  | 2600     | 0.0914        | -               |
| 0.6435  | 2650     | 0.0357        | -               |
| 0.6557  | 2700     | 0.0546        | -               |
| 0.6678  | 2750     | 0.0748        | -               |
| 0.6799  | 2800     | 0.0149        | -               |
| 0.6921  | 2850     | 0.1067        | -               |
| 0.7042  | 2900     | 0.0054        | -               |
| 0.7164  | 2950     | 0.0878        | -               |
| 0.7285  | 3000     | 0.0385        | -               |
| 0.7407  | 3050     | 0.036         | -               |
| 0.7528  | 3100     | 0.0902        | -               |
| 0.7649  | 3150     | 0.0734        | -               |
| 0.7771  | 3200     | 0.0369        | -               |
| 0.7892  | 3250     | 0.0031        | -               |
| 0.8014  | 3300     | 0.0113        | -               |
| 0.8135  | 3350     | 0.0862        | -               |
| 0.8256  | 3400     | 0.0549        | -               |
| 0.8378  | 3450     | 0.0104        | -               |
| 0.8499  | 3500     | 0.0072        | -               |
| 0.8621  | 3550     | 0.0546        | -               |
| 0.8742  | 3600     | 0.0579        | -               |
| 0.8864  | 3650     | 0.0789        | -               |
| 0.8985  | 3700     | 0.0711        | -               |
| 0.9106  | 3750     | 0.0361        | -               |
| 0.9228  | 3800     | 0.0292        | -               |
| 0.9349  | 3850     | 0.0121        | -               |
| 0.9471  | 3900     | 0.0066        | -               |
| 0.9592  | 3950     | 0.0091        | -               |
| 0.9713  | 4000     | 0.0027        | -               |
| 0.9835  | 4050     | 0.0891        | -               |
| 0.9956  | 4100     | 0.0186        | -               |
| **1.0** | **4118** | **-**         | **0.2746**      |
| 1.0078  | 4150     | 0.0246        | -               |
| 1.0199  | 4200     | 0.0154        | -               |
| 1.0321  | 4250     | 0.0056        | -               |
| 1.0442  | 4300     | 0.0343        | -               |
| 1.0563  | 4350     | 0.0375        | -               |
| 1.0685  | 4400     | 0.0106        | -               |
| 1.0806  | 4450     | 0.0025        | -               |
| 1.0928  | 4500     | 0.0425        | -               |
| 1.1049  | 4550     | 0.0019        | -               |
| 1.1170  | 4600     | 0.0014        | -               |
| 1.1292  | 4650     | 0.0883        | -               |
| 1.1413  | 4700     | 0.0176        | -               |
| 1.1535  | 4750     | 0.0204        | -               |
| 1.1656  | 4800     | 0.0011        | -               |
| 1.1778  | 4850     | 0.005         | -               |
| 1.1899  | 4900     | 0.0238        | -               |
| 1.2020  | 4950     | 0.0362        | -               |
| 1.2142  | 5000     | 0.0219        | -               |
| 1.2263  | 5050     | 0.0487        | -               |
| 1.2385  | 5100     | 0.0609        | -               |
| 1.2506  | 5150     | 0.0464        | -               |
| 1.2627  | 5200     | 0.0033        | -               |
| 1.2749  | 5250     | 0.0087        | -               |
| 1.2870  | 5300     | 0.0101        | -               |
| 1.2992  | 5350     | 0.0529        | -               |
| 1.3113  | 5400     | 0.0243        | -               |
| 1.3235  | 5450     | 0.001         | -               |
| 1.3356  | 5500     | 0.0102        | -               |
| 1.3477  | 5550     | 0.0047        | -               |
| 1.3599  | 5600     | 0.0034        | -               |
| 1.3720  | 5650     | 0.0118        | -               |
| 1.3842  | 5700     | 0.0742        | -               |
| 1.3963  | 5750     | 0.0538        | -               |
| 1.4085  | 5800     | 0.0162        | -               |
| 1.4206  | 5850     | 0.0079        | -               |
| 1.4327  | 5900     | 0.0027        | -               |
| 1.4449  | 5950     | 0.0035        | -               |
| 1.4570  | 6000     | 0.0581        | -               |
| 1.4692  | 6050     | 0.0813        | -               |
| 1.4813  | 6100     | 0.0339        | -               |
| 1.4934  | 6150     | 0.0312        | -               |
| 1.5056  | 6200     | 0.0323        | -               |
| 1.5177  | 6250     | 0.0521        | -               |
| 1.5299  | 6300     | 0.0016        | -               |
| 1.5420  | 6350     | 0.0009        | -               |
| 1.5542  | 6400     | 0.0967        | -               |
| 1.5663  | 6450     | 0.0009        | -               |
| 1.5784  | 6500     | 0.031         | -               |
| 1.5906  | 6550     | 0.0114        | -               |
| 1.6027  | 6600     | 0.0599        | -               |
| 1.6149  | 6650     | 0.0416        | -               |
| 1.6270  | 6700     | 0.0047        | -               |
| 1.6391  | 6750     | 0.0234        | -               |
| 1.6513  | 6800     | 0.0609        | -               |
| 1.6634  | 6850     | 0.022         | -               |
| 1.6756  | 6900     | 0.0042        | -               |
| 1.6877  | 6950     | 0.0336        | -               |
| 1.6999  | 7000     | 0.0592        | -               |
| 1.7120  | 7050     | 0.0536        | -               |
| 1.7241  | 7100     | 0.1198        | -               |
| 1.7363  | 7150     | 0.1035        | -               |
| 1.7484  | 7200     | 0.0549        | -               |
| 1.7606  | 7250     | 0.027         | -               |
| 1.7727  | 7300     | 0.0251        | -               |
| 1.7848  | 7350     | 0.0225        | -               |
| 1.7970  | 7400     | 0.0027        | -               |
| 1.8091  | 7450     | 0.0309        | -               |
| 1.8213  | 7500     | 0.024         | -               |
| 1.8334  | 7550     | 0.0355        | -               |
| 1.8456  | 7600     | 0.0239        | -               |
| 1.8577  | 7650     | 0.0377        | -               |
| 1.8698  | 7700     | 0.012         | -               |
| 1.8820  | 7750     | 0.0233        | -               |
| 1.8941  | 7800     | 0.0184        | -               |
| 1.9063  | 7850     | 0.0022        | -               |
| 1.9184  | 7900     | 0.0043        | -               |
| 1.9305  | 7950     | 0.014         | -               |
| 1.9427  | 8000     | 0.0083        | -               |
| 1.9548  | 8050     | 0.0084        | -               |
| 1.9670  | 8100     | 0.0009        | -               |
| 1.9791  | 8150     | 0.002         | -               |
| 1.9913  | 8200     | 0.0002        | -               |
| 2.0     | 8236     | -             | 0.2768          |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.0

## 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": ["accuracy"], "widget": [{"text": "Fox News, The Washington Post, NBC News, The Associated Press and the Los Angeles Times are among the entities that have said they will file amicus briefs on behalf of CNN."}, {"text": "Tommy Robinson is in prison today because he violated a court order demanding that he not film videos outside the trials of Muslim rape gangs.\n"}, {"text": "As I wrote during the presidential campaign, Trump has no idea of Washington and no idea who to appoint who would support him rather than work against him."}, {"text": "IN MAY 2013, the Washington Post\u2019s Greg Miller reported that the head of the CIA\u2019s clandestine service was being shifted out of that position as a result of \u201ca management shake-up\u201d by then-Director John Brennan."}, {"text": "Columbus police are investigating the shootings."}], "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": "accuracy", "value": 0.602089552238806, "name": "Accuracy"}]}]}]}
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