library_name: transformers
tags: []
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]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
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
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
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
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
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)
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]