Instructions to use Mesay/Homo-mex-multi-class-hate-speech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mesay/Homo-mex-multi-class-hate-speech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mesay/Homo-mex-multi-class-hate-speech")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mesay/Homo-mex-multi-class-hate-speech") model = AutoModelForSequenceClassification.from_pretrained("Mesay/Homo-mex-multi-class-hate-speech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30a77de0714c723c83344598b56a54eafa5222593683e13a2e4063dc98f7a38e
- Size of remote file:
- 499 MB
- SHA256:
- d373df2aa5840df81c89176f0a40ec05aaacd8ad80f149f8b0b6be03947464dc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.