Instructions to use warrior1127/hate_roberta_large_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use warrior1127/hate_roberta_large_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="warrior1127/hate_roberta_large_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("warrior1127/hate_roberta_large_model") model = AutoModelForSequenceClassification.from_pretrained("warrior1127/hate_roberta_large_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9421d2af49f3a39d99d60b21b3631b2a95b2a4abdff24f8079f087e64de26939
- Size of remote file:
- 1.42 GB
- SHA256:
- 987b97a0175ee00e3f8189333bc22ad2b81f77b7de15c043006f49ff7ddc7f08
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.