Instructions to use UDHOV/mbert-nepali-hate-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UDHOV/mbert-nepali-hate-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UDHOV/mbert-nepali-hate-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UDHOV/mbert-nepali-hate-classification") model = AutoModelForSequenceClassification.from_pretrained("UDHOV/mbert-nepali-hate-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 2217cfb289593d789bb128c5e77d04bcccf2ae2996bfbe16bf21cec52283ec35
- Size of remote file:
- 175 kB
- SHA256:
- c52592ff7e51d4295915080d0de51117141d90c3cd7cd0aa3fe13f9536ea98d5
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