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---
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: يا زلمة يلي بيصنع معنا معروف بنتشكره شو ما كان يكون وانتم ادعياء الاخوة العرب
هول مش ايرانيين ولا عجم عرب متلنا متلهم
- text: لعمي
- text: هلق رجع لمن قلو الريس تبعو هش قلو مشمو على عيني ؟
- text: مثل الكليشيه وبشكل يومي في حدا بده يعاير التاني بيقوم بيشبهه بالكلب والله
اذا حدا شبهني بالكلب بعتبرها مدح شديد
- text: الله لا يحرمك من الهبل ان شاء الله
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: 0.8497652582159625
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 |
|:---------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| negative | <ul><li>'الف تحية لشيخ العقل ومشايخنا الكرام'</li><li>'بتحبو او بتكرهو انشط وزير و رئيس تيار و ديبلوماسيتو بتتدرّس'</li><li>'نعم معاليك ستظل دمشق المدينة التي تغنى بها الشعراء وهذه الكلمات خير شاهد فرشت فوق ثراك الطاهرالهدبا'</li></ul> |
| positive | <ul><li>'لسانك حصانك وحسنا فعلت قطر لتلغي مركز الأبحاث لا مرحبا بكم انتم ولا تستاهلون اي عمل لكم ناكرين المعروف'</li><li>'ارنب وبضلك ارنب ابكي بترتاح يا صرماية'</li><li>'سليمان فرنجية عبارة عن كلب مسعور لديه حاسة شم قوية جداً شم ريحة كرسي الرئاسة ولكنه لن يجلس عليها ابداً وتصبحو على خير'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8498 |
## 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_ar_hs")
# Run inference
preds = model("لعمي")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 12.2323 | 52 |
| Label | Training Sample Count |
|:---------|:----------------------|
| negative | 1995 |
| positive | 2500 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- max_steps: 10000
- 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_hate_25kv
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0002 | 1 | 0.3185 | - |
| 0.02 | 100 | 0.2901 | - |
| 0.04 | 200 | 0.2441 | - |
| 0.06 | 300 | 0.2209 | - |
| 0.08 | 400 | 0.1715 | - |
| 0.1 | 500 | 0.1304 | - |
| 0.12 | 600 | 0.0891 | - |
| 0.14 | 700 | 0.0604 | - |
| 0.16 | 800 | 0.0436 | - |
| 0.18 | 900 | 0.0408 | - |
| 0.2 | 1000 | 0.0265 | - |
| 0.22 | 1100 | 0.0239 | - |
| 0.24 | 1200 | 0.0235 | - |
| 0.26 | 1300 | 0.0232 | - |
| 0.28 | 1400 | 0.0241 | - |
| 0.3 | 1500 | 0.019 | - |
| 0.32 | 1600 | 0.0168 | - |
| 0.34 | 1700 | 0.0172 | - |
| 0.36 | 1800 | 0.0136 | - |
| 0.38 | 1900 | 0.0099 | - |
| 0.4 | 2000 | 0.0117 | - |
| 0.42 | 2100 | 0.0091 | - |
| 0.44 | 2200 | 0.0067 | - |
| 0.46 | 2300 | 0.0074 | - |
| 0.48 | 2400 | 0.0055 | - |
| 0.5 | 2500 | 0.0053 | - |
| 0.52 | 2600 | 0.0054 | - |
| 0.54 | 2700 | 0.0058 | - |
| 0.56 | 2800 | 0.0059 | - |
| 0.58 | 2900 | 0.0055 | - |
| 0.6 | 3000 | 0.0043 | - |
| 0.62 | 3100 | 0.0045 | - |
| 0.64 | 3200 | 0.0055 | - |
| 0.66 | 3300 | 0.0042 | - |
| 0.68 | 3400 | 0.0024 | - |
| 0.7 | 3500 | 0.0025 | - |
| 0.72 | 3600 | 0.0047 | - |
| 0.74 | 3700 | 0.0036 | - |
| 0.76 | 3800 | 0.0029 | - |
| 0.78 | 3900 | 0.0043 | - |
| 0.8 | 4000 | 0.0036 | - |
| 0.82 | 4100 | 0.0025 | - |
| 0.84 | 4200 | 0.0033 | - |
| 0.86 | 4300 | 0.0018 | - |
| 0.88 | 4400 | 0.0016 | - |
| 0.9 | 4500 | 0.0018 | - |
| 0.92 | 4600 | 0.0023 | - |
| 0.94 | 4700 | 0.0027 | - |
| 0.96 | 4800 | 0.0023 | - |
| 0.98 | 4900 | 0.0012 | - |
| 1.0 | 5000 | 0.0021 | - |
| 1.02 | 5100 | 0.0026 | - |
| 1.04 | 5200 | 0.0019 | - |
| 1.06 | 5300 | 0.002 | - |
| 1.08 | 5400 | 0.0022 | - |
| 1.1 | 5500 | 0.0025 | - |
| 1.12 | 5600 | 0.0033 | - |
| 1.1400 | 5700 | 0.001 | - |
| 1.16 | 5800 | 0.0016 | - |
| 1.18 | 5900 | 0.0015 | - |
| 1.2 | 6000 | 0.0008 | - |
| 1.22 | 6100 | 0.0011 | - |
| 1.24 | 6200 | 0.0012 | - |
| 1.26 | 6300 | 0.0009 | - |
| 1.28 | 6400 | 0.0012 | - |
| 1.3 | 6500 | 0.001 | - |
| 1.32 | 6600 | 0.0014 | - |
| 1.34 | 6700 | 0.0002 | - |
| 1.3600 | 6800 | 0.0005 | - |
| 1.38 | 6900 | 0.0003 | - |
| 1.4 | 7000 | 0.0001 | - |
| 1.42 | 7100 | 0.0007 | - |
| 1.44 | 7200 | 0.0003 | - |
| 1.46 | 7300 | 0.0002 | - |
| 1.48 | 7400 | 0.0005 | - |
| 1.5 | 7500 | 0.0001 | - |
| 1.52 | 7600 | 0.0003 | - |
| 1.54 | 7700 | 0.001 | - |
| 1.56 | 7800 | 0.0003 | - |
| 1.58 | 7900 | 0.0 | - |
| 1.6 | 8000 | 0.0002 | - |
| 1.62 | 8100 | 0.0 | - |
| 1.6400 | 8200 | 0.0002 | - |
| 1.6600 | 8300 | 0.0002 | - |
| 1.6800 | 8400 | 0.0 | - |
| 1.7 | 8500 | 0.0 | - |
| 1.72 | 8600 | 0.0002 | - |
| 1.74 | 8700 | 0.0002 | - |
| 1.76 | 8800 | 0.0002 | - |
| 1.78 | 8900 | 0.0002 | - |
| 1.8 | 9000 | 0.0 | - |
| 1.8200 | 9100 | 0.0004 | - |
| 1.8400 | 9200 | 0.0 | - |
| 1.8600 | 9300 | 0.0002 | - |
| 1.88 | 9400 | 0.0002 | - |
| 1.9 | 9500 | 0.0 | - |
| 1.92 | 9600 | 0.0003 | - |
| 1.94 | 9700 | 0.0 | - |
| 1.96 | 9800 | 0.0 | - |
| 1.98 | 9900 | 0.0 | - |
| 2.0 | 10000 | 0.0 | - |
### Framework Versions
- Python: 3.10.14
- SetFit: 1.2.0.dev0
- Sentence Transformers: 3.2.1
- 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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