---
base_model: akhooli/sbert_ar_nli_500k_norm
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 'فيلم تشويق بدون الكثير من الإثارة. '
- text: 'النقطة النهائية للفيلم - وهي أن كل شخص يجب أن يكون على طبيعته - هي نقطة مبتذلة،
    لكن كاتب السيناريو والمخرج ميشيل جوندري أعاد تأكيدها إلى حد السخافة. '
- text: 'واحد من أعظم الأفلام التي رأيتها على الإطلاق. '
- text: 'إنها لا تلعب دورًا دراميًا أبدًا حتى عندما تحدث أشياء دراماتيكية للناس. '
- text: 'يستغل -lrb- headbanger -rrb- الصور النمطية بطريقة ممتعة، مع إضافة القليل
    من القلب والموضوع المقلق. '
inference: true
model-index:
- name: SetFit with akhooli/sbert_ar_nli_500k_norm
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.841995841995842
      name: Accuracy
---

# SetFit with akhooli/sbert_ar_nli_500k_norm

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [akhooli/sbert_ar_nli_500k_norm](https://huggingface.co/akhooli/sbert_ar_nli_500k_norm) as the Sentence Transformer embedding model. 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 body:** [akhooli/sbert_ar_nli_500k_norm](https://huggingface.co/akhooli/sbert_ar_nli_500k_norm)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 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                                                                                                                                                                                          |
|:---------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| positive | <ul><li>'جلبت إلى الحياة على الشاشة الكبيرة. '</li><li>'يجب أن ينظر إليه على أنه يعتقد. '</li><li>'تم إجراء القتال بالسيف بشكل جيد، كما أن auteuil هو متعة أبله. '</li></ul>                      |
| negative | <ul><li>'الشعور العام لا يختلف عن مشاهدة حلقة مجيدة من "الجنة السابعة". \'\' '</li><li>'... الأمر برمته لم ينجح إلا في جعلني أشعر بالترنح. '</li><li>'يتطلب الكثير من معظم المشاهدين. '</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8420   |

## 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("akhooli/setfit")
# Run inference
preds = model("فيلم تشويق بدون الكثير من الإثارة. ")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 15.787 | 45  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| negative | 1000                  |
| positive | 1000                  |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (10, 10)
- max_steps: 1000
- sampling_strategy: undersampling
- 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
- l2_weight: 0.01
- seed: 42
- run_name: setfit_sst2
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-----:|:----:|:-------------:|:---------------:|
| 0.02  | 1    | 0.1616        | -               |
| 1.0   | 50   | 0.1473        | -               |
| 2.0   | 100  | 0.0924        | -               |
| 0.002 | 1    | 0.1187        | -               |
| 0.1   | 50   | 0.1062        | -               |
| 0.2   | 100  | 0.079         | -               |
| 0.3   | 150  | 0.0586        | -               |
| 0.4   | 200  | 0.0404        | -               |
| 0.5   | 250  | 0.0285        | -               |
| 0.6   | 300  | 0.02          | -               |
| 0.7   | 350  | 0.0151        | -               |
| 0.8   | 400  | 0.0163        | -               |
| 0.9   | 450  | 0.0125        | -               |
| 1.0   | 500  | 0.0094        | -               |
| 1.1   | 550  | 0.0109        | -               |
| 1.2   | 600  | 0.0086        | -               |
| 1.3   | 650  | 0.0074        | -               |
| 1.4   | 700  | 0.0081        | -               |
| 1.5   | 750  | 0.0071        | -               |
| 1.6   | 800  | 0.0076        | -               |
| 1.7   | 850  | 0.0078        | -               |
| 1.8   | 900  | 0.0103        | -               |
| 1.9   | 950  | 0.0074        | -               |
| 2.0   | 1000 | 0.011         | -               |

### Framework Versions
- Python: 3.10.14
- SetFit: 1.2.0.dev0
- Sentence Transformers: 3.1.1
- Transformers: 4.45.1
- PyTorch: 2.4.0
- Datasets: 3.0.1
- Tokenizers: 0.20.0

## 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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