--- 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: 'بلد مخيف، صار القتل بحجه الشرف متل قتل بعوضة، واللي بيخوف اكتر من اللي واقف مكتف ايديه ومش مساعد. وين كنآ، ووين وصلنآ، لمتى حنضل عايشين وساكتين! ' - text: "من خلال المتابعة ..يتضح أن أكثر اللاعبين الذين يتم تسويقهم هم لاعبي امريكا\ \ الجنوبية وأقلهم الافارقة. \nمن خلال الواقع ..أكثر اللاعبين تهاونا ولعب على\ \ الواقف في آخر ٦ شهور من عقودهم هم لاعبي امريكا الجنوبية ." - text: ' علم الحزب يا فهمانه ما حطوا لانه عم يحكي وطنيا ومشان ماحدا متلك يعترض. اذا حطوا بتعترضي واذا ما حطوا كمان بتعترضي.' - text: "شيوعي \nعلماني \nمسيحي\nانصار سنه \nصوفي \nيمثلك التجمع \nلا يمثلك التجمع\ \ \nاهلا بكم جميعا فنحن نريد بناء وطن ❤" - text: كنا نهرب بحصة الرياضيات والمحاسبة وبنرجع آخر الحصة بخمس دئايئ ولمن تسئلنا المعلمة بنحكيلها كنا عند المديرة وبتسدئنا وضلينا 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: 0.8606060606060606 name: Accuracy --- This is a setfit hate speech detection model (86 % accuracy/f1) based on the [Ar Hate Speech dataset](https://huggingface.co/datasets/akhooli/ar_hs). Usage: ```python 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](https://www.linkedin.com/posts/akhooli_arabic-hate-speech-detection-is-not-an-easy-activity-7261021456023609344-UJzM) The rest of this content is auto-generated. # 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 ### 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 | | | negative | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.8606 | ## 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("شيوعي علماني مسيحي انصار سنه صوفي يمثلك التجمع لا يمثلك التجمع اهلا بكم جميعا فنحن نريد بناء وطن ❤") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 1 | 18.8448 | 185 | | Label | Training Sample Count | |:---------|:----------------------| | negative | 5200 | | positive | 4943 | ### 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 | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0003 | 1 | 0.3151 | - | | 0.0333 | 100 | 0.2902 | - | | 0.0667 | 200 | 0.248 | - | | 0.1 | 300 | 0.2011 | - | | 0.1333 | 400 | 0.164 | - | | 0.1667 | 500 | 0.136 | - | | 0.2 | 600 | 0.1162 | - | | 0.2333 | 700 | 0.0915 | - | | 0.2667 | 800 | 0.0724 | - | | 0.3 | 900 | 0.0656 | - | | 0.3333 | 1000 | 0.05 | - | | 0.3667 | 1100 | 0.0454 | - | | 0.4 | 1200 | 0.0407 | - | | 0.4333 | 1300 | 0.0318 | - | | 0.4667 | 1400 | 0.0338 | - | | 0.5 | 1500 | 0.0289 | - | | 0.5333 | 1600 | 0.0266 | - | | 0.5667 | 1700 | 0.0238 | - | | 0.6 | 1800 | 0.02 | - | | 0.6333 | 1900 | 0.0167 | - | | 0.6667 | 2000 | 0.0168 | - | | 0.7 | 2100 | 0.0161 | - | | 0.7333 | 2200 | 0.0143 | - | | 0.7667 | 2300 | 0.0128 | - | | 0.8 | 2400 | 0.0128 | - | | 0.8333 | 2500 | 0.0146 | - | | 0.8667 | 2600 | 0.0113 | - | | 0.9 | 2700 | 0.0146 | - | | 0.9333 | 2800 | 0.0109 | - | | 0.9667 | 2900 | 0.0128 | - | | 1.0 | 3000 | 0.0101 | - | | 1.0333 | 3100 | 0.0126 | - | | 1.0667 | 3200 | 0.0092 | - | | 1.1 | 3300 | 0.0108 | - | | 1.1333 | 3400 | 0.0095 | - | | 1.1667 | 3500 | 0.0121 | - | | 1.2 | 3600 | 0.0088 | - | | 1.2333 | 3700 | 0.0086 | - | | 1.2667 | 3800 | 0.0075 | - | | 1.3 | 3900 | 0.009 | - | | 1.3333 | 4000 | 0.008 | - | | 1.3667 | 4100 | 0.0051 | - | | 1.4 | 4200 | 0.007 | - | | 1.4333 | 4300 | 0.0055 | - | | 1.4667 | 4400 | 0.0074 | - | | 1.5 | 4500 | 0.0065 | - | | 1.5333 | 4600 | 0.0086 | - | | 1.5667 | 4700 | 0.0064 | - | | 1.6 | 4800 | 0.0064 | - | | 1.6333 | 4900 | 0.0073 | - | | 1.6667 | 5000 | 0.0052 | - | | 1.7 | 5100 | 0.0056 | - | | 1.7333 | 5200 | 0.0059 | - | | 1.7667 | 5300 | 0.0048 | - | | 1.8 | 5400 | 0.0044 | - | | 1.8333 | 5500 | 0.003 | - | | 1.8667 | 5600 | 0.0045 | - | | 1.9 | 5700 | 0.0043 | - | | 1.9333 | 5800 | 0.0042 | - | | 1.9667 | 5900 | 0.0029 | - | | 2.0 | 6000 | 0.0033 | - | ### 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 ```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} } ```