
# SetFit

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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 [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 2 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0.0   | <ul><li>'Pamela Geller and Robert Spencer co-founded anti-Muslim group Stop Islamization of America.\n'</li><li>'He added: "We condemn all those whose behaviours and views run counter to our shared values and will not stand for extremism in any form."\n'</li><li>'Ms Geller, of the Atlas Shrugs blog, and Mr Spencer, of Jihad Watch, are also co-founders of the American Freedom Defense Initiative, best known for a pro-Israel "Defeat Jihad" poster campaign on the New York subway.\n'</li></ul>                    |
| 1.0   | <ul><li>'On both of their blogs the pair called their bans from entering the UK "a striking blow against freedom" and said the "the nation that gave the world the Magna Carta is dead".\n'</li><li>'A researcher with the organisation, Matthew Collins, said it was "delighted" with the decision.\n'</li><li>'Lead attorney Matt Gonzalez has argued that the weapon was a SIG Sauer with a "hair trigger in single-action mode" — a model well-known for accidental discharges even among experienced shooters.\n'</li></ul> |

## Evaluation

### Metrics
| Label   | F1     |
|:--------|:-------|
| **all** | 0.3372 |

## 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/Roberta-large-G3-setfit-model")
# Run inference
preds = model("There are 2 trillion Google searches per day.")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 26.8625 | 105 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 200                   |
| 1     | 200                   |

### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (3, 3)
- 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 |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.002   | 1        | 0.3467        | -               |
| 0.1     | 50       | 0.2333        | -               |
| 0.2     | 100      | 0.237         | -               |
| 0.3     | 150      | 0.2466        | -               |
| 0.4     | 200      | 0.208         | -               |
| 0.5     | 250      | 0.2121        | -               |
| 0.6     | 300      | 0.0076        | -               |
| 0.7     | 350      | 0.0011        | -               |
| 0.8     | 400      | 0.0007        | -               |
| 0.9     | 450      | 0.0002        | -               |
| 1.0     | 500      | 0.0015        | 0.3342          |
| 1.1     | 550      | 0.0001        | -               |
| 1.2     | 600      | 0.0002        | -               |
| 1.3     | 650      | 0.0003        | -               |
| 1.4     | 700      | 0.0003        | -               |
| 1.5     | 750      | 0.0002        | -               |
| 1.6     | 800      | 0.0002        | -               |
| 1.7     | 850      | 0.0001        | -               |
| 1.8     | 900      | 0.0001        | -               |
| 1.9     | 950      | 0.0001        | -               |
| **2.0** | **1000** | **0.0001**    | **0.3303**      |
| 2.1     | 1050     | 0.0           | -               |
| 2.2     | 1100     | 0.0           | -               |
| 2.3     | 1150     | 0.0001        | -               |
| 2.4     | 1200     | 0.0           | -               |
| 2.5     | 1250     | 0.0           | -               |
| 2.6     | 1300     | 0.0           | -               |
| 2.7     | 1350     | 0.0001        | -               |
| 2.8     | 1400     | 0.0001        | -               |
| 2.9     | 1450     | 0.0           | -               |
| 3.0     | 1500     | 0.0           | 0.3327          |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.2
- 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
{"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["f1"], "widget": [{"text": "This also goes for bigger issues like foreign policy as well; multiple full-scale invasions of Syria have been prevented because of information that the alternative media made viral."}, {"text": "Yesterday\u2019s State of the Union address issued by Donald Trump represented a refreshing break from the eight years of pusillanimous foreign policies pursued by past administration.\n"}, {"text": "There are 2 trillion Google searches per day."}, {"text": "Westerville Officers Eric Joering, 39, and Anthony Morelli, 54, were killed shortly after noon Saturday in this normally quiet suburb while responding to a 911 hang-up call."}, {"text": "Trump was right, Acosta is a \"rude, terrible person.\"\n"}], "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": "f1", "value": 0.3371824480369515, "name": "F1"}]}]}]}
setfit safetensors roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 model-index region:us