setfit_ar_hs

This is a setfit hate speech detection model (86 % accuracy/f1) based on the Ar Hate Speech dataset.

Usage:

pip install setfit
from setfit import SetFitModel
from unicodedata import normalize

# Download model from Hub
model = SetFitModel.from_pretrained("akhooli/setfit_ar_hs")
# Run inference
queries = [
       "سكت دهراً و نطق كفراً",
       "الخلاف ﻻ يفسد للود قضية.",
       "أنت شخص منبوذ. احترم أسيادك.",
       "دع المكارم ﻻ ترحل لبغيتها واقعد فإنك أنت الطاعم الكاسي",
   ]
queries_n = [normalize('NFKC', query) for query in queries]
preds = model.predict(queries_n)
print(preds)
# if you want to see the probabilities for each label
probas = model.predict_proba(queries_n)
print(probas)

• LinkedIn article

The rest of this content is auto-generated.

SetFit with akhooli/sbert_ar_nli_500k_norm

This is a SetFit model that can be used for Text Classification. This SetFit model uses akhooli/sbert_ar_nli_500k_norm as the Sentence Transformer embedding model. A LogisticRegression 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 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
• Classification head: a LogisticRegression 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
• Paper: Efficient Few-Shot Learning Without Prompts
• Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Evaluation

Metrics

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("akhooli/setfit_ar_hs")
# Run inference
preds = model("شيوعي 
علماني 
مسيحي
انصار سنه 
صوفي 
يمثلك التجمع 
لا يمثلك التجمع 
اهلا بكم جميعا فنحن نريد بناء وطن ❤")

<!-- ### 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 Hyperparameters

• batch_size: (32, 32)
• num_epochs: (1, 1)
• max_steps: 6000
• 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_52k_aub_6k
• eval_max_steps: -1
• load_best_model_at_end: False

Training Results

Framework Versions

• Python: 3.10.14
• SetFit: 1.2.0.dev0
• Sentence Transformers: 3.3.0
• Transformers: 4.45.1
• PyTorch: 2.4.0
• Datasets: 3.0.1
• Tokenizers: 0.20.0

Citation

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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