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. & as a man you should always take the trash out...")

Github: Sakil Ansari