Instructions to use SotirisLegkas/multi_hate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SotirisLegkas/multi_hate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SotirisLegkas/multi_hate")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SotirisLegkas/multi_hate") model = AutoModelForSequenceClassification.from_pretrained("SotirisLegkas/multi_hate") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e227d2102df4d6fa52bc7fcdcdbc267c65b192e8636d9fe20006a7e3a0e40d04
- Size of remote file:
- 499 MB
- SHA256:
- 55f22ebbdaa04d94cdbb3cd5a398ef4d0018b99289d1ba68ceb0e2fb5d971868
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.