Instructions to use christinacdl/BERT_Offensive_English_Twitter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use christinacdl/BERT_Offensive_English_Twitter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="christinacdl/BERT_Offensive_English_Twitter")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("christinacdl/BERT_Offensive_English_Twitter") model = AutoModelForSequenceClassification.from_pretrained("christinacdl/BERT_Offensive_English_Twitter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd8205048bfce2631bbb1bbd49ad11ea402a3220e4a00d988b34e3f07be7e03b
- Size of remote file:
- 1.34 GB
- SHA256:
- 9f404187cb4a336af152c99b0a2711a69437a0579755e2e13b1146cd9e2d3839
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.