---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: 'Texas: Cop Walks Into Home She Thought Was Hers, Kills Innocent Homeowner—Not
    Arrested'
- text: Ellison subsequently agreed to dismiss his restraining order against her if
    she no longer contacted him.
- text: Gina Haspel will become the new Director of the CIA, and the first woman so
    chosen.
- text: At some point, the officer fired her weapon striking the victim.
- text: Ronaldo Rauseo-Ricupero, a lawyer for the Indonesians, argued they should
    have 90 days to move to reopen their cases after receiving copies of their administrative
    case files and time to appeal any decision rejecting those motions.
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: 0.8151016456921588
      name: Accuracy
---

# 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) -->
<!-- - **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   | Accuracy |
|:--------|:---------|
| **all** | 0.8151   |

## 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/doubt_repetition_with_noPropaganda_SetFit")
# Run inference
preds = model("At some point, the officer fired her weapon striking the victim.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 20.8138 | 129 |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 5
- 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 |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0004  | 1        | 0.3567        | -               |
| 0.0209  | 50       | 0.3286        | -               |
| 0.0419  | 100      | 0.2663        | -               |
| 0.0628  | 150      | 0.2378        | -               |
| 0.0838  | 200      | 0.1935        | -               |
| 0.1047  | 250      | 0.2549        | -               |
| 0.1257  | 300      | 0.2654        | -               |
| 0.1466  | 350      | 0.1668        | -               |
| 0.1676  | 400      | 0.1811        | -               |
| 0.1885  | 450      | 0.1884        | -               |
| 0.2095  | 500      | 0.157         | -               |
| 0.2304  | 550      | 0.1237        | -               |
| 0.2514  | 600      | 0.1318        | -               |
| 0.2723  | 650      | 0.1334        | -               |
| 0.2933  | 700      | 0.1067        | -               |
| 0.3142  | 750      | 0.1189        | -               |
| 0.3351  | 800      | 0.135         | -               |
| 0.3561  | 850      | 0.0782        | -               |
| 0.3770  | 900      | 0.0214        | -               |
| 0.3980  | 950      | 0.0511        | -               |
| 0.4189  | 1000     | 0.0924        | -               |
| 0.4399  | 1050     | 0.1418        | -               |
| 0.4608  | 1100     | 0.0132        | -               |
| 0.4818  | 1150     | 0.0018        | -               |
| 0.5027  | 1200     | 0.0706        | -               |
| 0.5237  | 1250     | 0.1502        | -               |
| 0.5446  | 1300     | 0.133         | -               |
| 0.5656  | 1350     | 0.0207        | -               |
| 0.5865  | 1400     | 0.0589        | -               |
| 0.6075  | 1450     | 0.0771        | -               |
| 0.6284  | 1500     | 0.0241        | -               |
| 0.6494  | 1550     | 0.0905        | -               |
| 0.6703  | 1600     | 0.0106        | -               |
| 0.6912  | 1650     | 0.0451        | -               |
| 0.7122  | 1700     | 0.0011        | -               |
| 0.7331  | 1750     | 0.0075        | -               |
| 0.7541  | 1800     | 0.0259        | -               |
| 0.7750  | 1850     | 0.0052        | -               |
| 0.7960  | 1900     | 0.0464        | -               |
| 0.8169  | 1950     | 0.0039        | -               |
| 0.8379  | 2000     | 0.0112        | -               |
| 0.8588  | 2050     | 0.0061        | -               |
| 0.8798  | 2100     | 0.0143        | -               |
| 0.9007  | 2150     | 0.0886        | -               |
| 0.9217  | 2200     | 0.2225        | -               |
| 0.9426  | 2250     | 0.0022        | -               |
| 0.9636  | 2300     | 0.0035        | -               |
| 0.9845  | 2350     | 0.002         | -               |
| **1.0** | **2387** | **-**         | **0.2827**      |
| 1.0054  | 2400     | 0.0315        | -               |
| 1.0264  | 2450     | 0.0049        | -               |
| 1.0473  | 2500     | 0.0305        | -               |
| 1.0683  | 2550     | 0.0334        | -               |
| 1.0892  | 2600     | 0.0493        | -               |
| 1.1102  | 2650     | 0.0424        | -               |
| 1.1311  | 2700     | 0.0011        | -               |
| 1.1521  | 2750     | 0.0109        | -               |
| 1.1730  | 2800     | 0.0009        | -               |
| 1.1940  | 2850     | 0.0005        | -               |
| 1.2149  | 2900     | 0.0171        | -               |
| 1.2359  | 2950     | 0.0004        | -               |
| 1.2568  | 3000     | 0.0717        | -               |
| 1.2778  | 3050     | 0.0019        | -               |
| 1.2987  | 3100     | 0.062         | -               |
| 1.3196  | 3150     | 0.0003        | -               |
| 1.3406  | 3200     | 0.0018        | -               |
| 1.3615  | 3250     | 0.0011        | -               |
| 1.3825  | 3300     | 0.0005        | -               |
| 1.4034  | 3350     | 0.0208        | -               |
| 1.4244  | 3400     | 0.0004        | -               |
| 1.4453  | 3450     | 0.001         | -               |
| 1.4663  | 3500     | 0.0003        | -               |
| 1.4872  | 3550     | 0.0015        | -               |
| 1.5082  | 3600     | 0.0004        | -               |
| 1.5291  | 3650     | 0.0473        | -               |
| 1.5501  | 3700     | 0.0092        | -               |
| 1.5710  | 3750     | 0.032         | -               |
| 1.5920  | 3800     | 0.0016        | -               |
| 1.6129  | 3850     | 0.0623        | -               |
| 1.6339  | 3900     | 0.0291        | -               |
| 1.6548  | 3950     | 0.0386        | -               |
| 1.6757  | 4000     | 0.002         | -               |
| 1.6967  | 4050     | 0.0006        | -               |
| 1.7176  | 4100     | 0.0005        | -               |
| 1.7386  | 4150     | 0.0004        | -               |
| 1.7595  | 4200     | 0.0004        | -               |
| 1.7805  | 4250     | 0.0007        | -               |
| 1.8014  | 4300     | 0.033         | -               |
| 1.8224  | 4350     | 0.0001        | -               |
| 1.8433  | 4400     | 0.0489        | -               |
| 1.8643  | 4450     | 0.0754        | -               |
| 1.8852  | 4500     | 0.0086        | -               |
| 1.9062  | 4550     | 0.0092        | -               |
| 1.9271  | 4600     | 0.0591        | -               |
| 1.9481  | 4650     | 0.0013        | -               |
| 1.9690  | 4700     | 0.0043        | -               |
| 1.9899  | 4750     | 0.0338        | -               |
| 2.0     | 4774     | -             | 0.3304          |

* 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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