khaled-kassem/Arabic-Profanity-Checker

Model Card for Model ID

Profanity/Toxic word checker with percentage telling how much a word is profane or normal

Model Details

Model trained on vast amount of data including normal humamn male/female names up to 10,000 names, and Profane/Toxic words up to 12,000 words including fuzzied words and expressions

Model Description

Model target is to check for a given 'Single' word (exactly one token) whether this input is a Profane/Toxic (very inappropriate) word or not with output LABEL_0 for a positive word(non profane) and LABEL_1 as negative word(profane) along side with the percentage of how much this word is profane

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: Khaled Kassem -> Me
• Funded by : Google Colab Research
• Shared by : Public
• Model type: BERT Base Transformer
• License: Public Open Sourced if needed
• Finetuned from model : asafaya/bert-base-arabic

Model Sources [optional]

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Uses

How to use? Simple, input a word or List of words and the model to predict for each word, whether it is a profane/toxic or not outputting LABEL_0 for positive non profane and LABEL_1 for negative profane

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

Model will not work well with contexts or semantics, it is used only for single word check, for sentiment analysis I recommend CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment model

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model. !pip install transformers

from transformers import AutoModelForSequenceClassification, AutoTokenizer from transformers import pipeline

Load the Model

model_path = "khaled-kassem/Arabic-Profanity-Checker" model = AutoModelForSequenceClassification.from_pretrained(model_path) tokenizer = AutoTokenizer.from_pretrained(model_path)

nlp_pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)

example_text = "وغد"

profane_result = nlp_pipeline(example_text)

for word in profane_result: if (word['label'] == 'LABEL_1'): print("Negative") print(word['score']*100) else: print("Positive") print(word['score']*100)

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Training Details

Training Data

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Training Procedure

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Training Hyperparameters

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Evaluation

Testing Data, Factors & Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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