Text Classification
Transformers
PyTorch
English
bert
BERTicelli
text classification
abusive language
hate speech
offensive language
Instructions to use patrickquick/BERTicelli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use patrickquick/BERTicelli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="patrickquick/BERTicelli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("patrickquick/BERTicelli") model = AutoModelForSequenceClassification.from_pretrained("patrickquick/BERTicelli") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- en
tags:
- BERTicelli
- text classification
- abusive language
- hate speech
- offensive language
datasets:
- OLID
license: apache-2.0
widget:
- text: If Jamie Oliver fucks with my £3 meal deals at Tesco I'll kill the cunt.
example_title: Example 1
- text: Keep up the good hard work.
example_title: Example 2
- text: >-
That's not hair. Those were polyester fibers because Yoda is (or was) a
puppet.
example_title: Example 3
Mona Allaert • Leonardo Grotti • Patrick Quick
Model description
BERTicelli is an English pre-trained BERT model obtained by fine-tuning the English BERT base cased model with the training data from Offensive Language Identification Dataset (OLID).
This model was developed for the NLP Shared Task in the Digital Text Analysis program at the University of Antwerp (2021–2022).