readme: ---
  license: apache-2.0
  language: en
  tags:
  - hate
  - speech
  
  widget:
  - text: "RT @ShenikaRoberts: The shit you hear about me might be true or it might be faker than the bitch who told it to ya &#5736"
  
  ---
  
  # Dataset Collection:
  * The hatespeech dataset is collected from different open sources like Kaggle ,social media like Twitter.
  * The dataset has the two classes hatespeech and non hatespeech.
  * The class distribution is equal
  * Different strategies have been followed during the data gathering phase.
  * The dataset is collected from relevant sources.
  
  # distilbert-base-uncased model is fine-tuned for Hate Speech Detection
  * The model is fine-tuned on the dataset.
  * This model can be used to create the labels for academic purposes or for industrial purposes.
  * This model can be used for the inference purpose as well.
  
  # Data Fields:
   
  **label**: 0 - it is a hate speech, 1 - not a hate speech
  
  # Application:
  * This model is useful for the detection of hatespeech in the tweets.
  * There are numerous situations where we have tweet data but no labels, so this approach can be used to create labels.
  * You can fine-tune this model for your particular use cases.
  
  # Model Implementation
  
  # !pip install transformers[sentencepiece]
  
  from transformers import pipeline
  
  model_name="Sakil/distilbert_lazylearner_hatespeech_detection"
  
  classifier = pipeline("text-classification",model=model_name)
  
  classifier("!!! RT @mayasolovely: As a woman you shouldn't complain about cleaning up your house. &amp; as a man you should always take the trash out...")
  
  # Github: [Sakil Ansari](https://github.com/Sakil786/hate_speech_detection_pretrained_model)