akhooli/setfit_ar_100k_reviews

SetFit with akhooli/sbertarnli500knorm

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. It was trained on akhooli/arreviews100k_3 dataset (4500 samples, as few shot) with 68.7% accuracy. There are 3 labels in the dataset: 0: negative, 1:positive, 2:mixed/neutral. Normalize the text before classifying as the model uses normalized text. Here's how to use the model:

pip install setfit
from setfit import SetFitModel
from unicodedata import normalize

# Download model from Hub
model = SetFitModel.from_pretrained("akhooli/setfit_ar_100k_reviews")
# Run inference
queries = [
        "يغلي الماء عند 100 درجة مئوية",
        "فعلا لقد أحببت ذلك الفيلم",
        "🤮 اﻷناناس مع البيتزا؟ إنه غير محبذ",
    "رأيت أناسا بائسين في الطريق",
    "لم يعجبني المطعم رغم أن السعر مقبول",  
    "من باب جبر الخاطر هذه 3 نجوم لتقييم الخدمة",
    "من باب جبر الخواطر، هذه نجمة واحدة لخدمة ﻻ تستحق"
    ]
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)

The rest of this model card is auto generated.

The model has been trained using an efficient few-shot learning technique that involves:

1. 1.Fine-tuning a Sentence Transformer with contrastive learning.
2. 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: 3 classes <!-- - Training Dataset: 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_100k_reviews")
# Run inference
preds = model("المرأة الخارقة")

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Training Details

Training Set Metrics

Training Hyperparameters

• —batch_size: (16, 16)
• —num_epochs: (1, 1)
• —max_steps: 5000
• —sampling_strategy: undersampling
• —bodylearningrate: (2e-05, 1e-05)
• —headlearningrate: 0.01
• —loss: CosineSimilarityLoss
• —distancemetric: cosinedistance
• —margin: 0.25
• —endtoend: False
• —use_amp: False
• —warmup_proportion: 0.1
• —l2_weight: 0.01
• —seed: 42
• —runname: setfitreviews_7.5k
• —evalmaxsteps: -1
• —loadbestmodelatend: False

Training Results

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

@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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