---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Guy Cecil, the former head of the Democratic Senatorial Campaign Committee
    and now the boss of a leading Democratic super PAC, voiced his frustration with
    the inadequacy of Franken’s apology on Twitter.
- text: Attorney Stephen Le Brocq, who operates a law firm in the North Texas area
    sums up the treatment of Guyger perfectly when he says that “The affidavit isn’t
    written objectively, not at the slightest.
- text: Phone This field is for validation purposes and should be left unchanged.
- text: The Twitter suspension caught me by surprise.
- text: Popular pages like The AntiMedia (2.1 million fans), The Free Thought Project
    (3.1 million fans), Press for Truth (350K fans), Police the Police (1.9 million
    fans), Cop Block (1.7 million fans), and Punk Rock Libertarians (125K fans) are
    just a few of the ones which were unpublished.
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.9987117552334943
      name: Accuracy
---

# SetFit

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 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:** [Unknown](https://huggingface.co/unknown) -->
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 3 classes
<!-- - **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)

### Model Labels
| Label | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2     | <ul><li>'This research group is only interested in violent extremism – according to their website.'</li><li>'No cop, anywhere, “signed up” to be murdered.'</li><li>"(Both those states are also part of today's federal lawsuit filed in the Western District of Washington.)"</li></ul>                                                                                                                                                                                                                                                                                                                |
| 1     | <ul><li>'In the meantime, the New Mexico district attorney who failed to file for a preliminary hearing within 10 days and didn’t show up for court is vowing to pursue prosecution of these jihadis.'</li><li>'According to the Constitution, you, and you alone, are the sole head of the executive branch, and as such you are where the buck stop in making sure the laws are faithfully executed.'</li><li>'And the death of the three-year-old?'</li></ul>                                                                                                                                         |
| 0     | <ul><li>'One of the Indonesian illegal aliens benefiting from her little amnesty took the hint and used the opportunity that Saris created to flee from arrest and deportation, absconding to a sanctuary church to hide from arrest.'</li><li>'So, why did Mueller focus on Manafort?'</li><li>'We had a lot of reporters in that room, many many reporters in that room and they were unable to ask questions because this guy gets up and starts, you know, doing what he’s supposed to be doing for him and for CNN and you know just shouting out questions and making statements, too."'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9987   |

## 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_multiclass_SetFit")
# Run inference
preds = model("The Twitter suspension caught me by surprise.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 20.4272 | 109 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 131                   |
| 1     | 129                   |
| 2     | 2479                  |

### 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 |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0006  | 1        | 0.3869        | -               |
| 0.0292  | 50       | 0.3352        | -               |
| 0.0584  | 100      | 0.2235        | -               |
| 0.0876  | 150      | 0.1518        | -               |
| 0.1168  | 200      | 0.1967        | -               |
| 0.1460  | 250      | 0.1615        | -               |
| 0.1752  | 300      | 0.1123        | -               |
| 0.2044  | 350      | 0.1493        | -               |
| 0.2336  | 400      | 0.0039        | -               |
| 0.2629  | 450      | 0.0269        | -               |
| 0.2921  | 500      | 0.0024        | -               |
| 0.3213  | 550      | 0.0072        | -               |
| 0.3505  | 600      | 0.0649        | -               |
| 0.3797  | 650      | 0.0005        | -               |
| 0.4089  | 700      | 0.0008        | -               |
| 0.4381  | 750      | 0.0041        | -               |
| 0.4673  | 800      | 0.0009        | -               |
| 0.4965  | 850      | 0.0004        | -               |
| 0.5257  | 900      | 0.0013        | -               |
| 0.5549  | 950      | 0.0013        | -               |
| 0.5841  | 1000     | 0.0066        | -               |
| 0.6133  | 1050     | 0.0355        | -               |
| 0.6425  | 1100     | 0.0004        | -               |
| 0.6717  | 1150     | 0.0013        | -               |
| 0.7009  | 1200     | 0.0003        | -               |
| 0.7301  | 1250     | 0.0002        | -               |
| 0.7593  | 1300     | 0.0008        | -               |
| 0.7886  | 1350     | 0.0002        | -               |
| 0.8178  | 1400     | 0.0002        | -               |
| 0.8470  | 1450     | 0.0004        | -               |
| 0.8762  | 1500     | 0.1193        | -               |
| 0.9054  | 1550     | 0.0002        | -               |
| 0.9346  | 1600     | 0.0002        | -               |
| 0.9638  | 1650     | 0.0002        | -               |
| 0.9930  | 1700     | 0.0002        | -               |
| 1.0     | 1712     | -             | 0.0073          |
| 1.0222  | 1750     | 0.0002        | -               |
| 1.0514  | 1800     | 0.0006        | -               |
| 1.0806  | 1850     | 0.0005        | -               |
| 1.1098  | 1900     | 0.0001        | -               |
| 1.1390  | 1950     | 0.0012        | -               |
| 1.1682  | 2000     | 0.0003        | -               |
| 1.1974  | 2050     | 0.0344        | -               |
| 1.2266  | 2100     | 0.0038        | -               |
| 1.2558  | 2150     | 0.0001        | -               |
| 1.2850  | 2200     | 0.0003        | -               |
| 1.3143  | 2250     | 0.0114        | -               |
| 1.3435  | 2300     | 0.0001        | -               |
| 1.3727  | 2350     | 0.0001        | -               |
| 1.4019  | 2400     | 0.0001        | -               |
| 1.4311  | 2450     | 0.0001        | -               |
| 1.4603  | 2500     | 0.0005        | -               |
| 1.4895  | 2550     | 0.0086        | -               |
| 1.5187  | 2600     | 0.0001        | -               |
| 1.5479  | 2650     | 0.0002        | -               |
| 1.5771  | 2700     | 0.0001        | -               |
| 1.6063  | 2750     | 0.0002        | -               |
| 1.6355  | 2800     | 0.0001        | -               |
| 1.6647  | 2850     | 0.0001        | -               |
| 1.6939  | 2900     | 0.0001        | -               |
| 1.7231  | 2950     | 0.0001        | -               |
| 1.7523  | 3000     | 0.0001        | -               |
| 1.7815  | 3050     | 0.0001        | -               |
| 1.8107  | 3100     | 0.0           | -               |
| 1.8400  | 3150     | 0.0001        | -               |
| 1.8692  | 3200     | 0.0001        | -               |
| 1.8984  | 3250     | 0.0001        | -               |
| 1.9276  | 3300     | 0.0           | -               |
| 1.9568  | 3350     | 0.0001        | -               |
| 1.9860  | 3400     | 0.0002        | -               |
| **2.0** | **3424** | **-**         | **0.0053**      |

* 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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