---
library_name: transformers
tags: []
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
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

<!-- Provide a longer summary of what this model is. -->
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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- **Repository:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]

## Uses

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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

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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

[More Information Needed]

## Bias, Risks, and Limitations

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### Recommendations

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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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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## Model Card Authors [optional]

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