Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use jkhan447/HateXplain-second-annotator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jkhan447/HateXplain-second-annotator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jkhan447/HateXplain-second-annotator")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jkhan447/HateXplain-second-annotator") model = AutoModelForSequenceClassification.from_pretrained("jkhan447/HateXplain-second-annotator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 05f29e7f840292a1233adae5d34320d56c4a74c8ff31aed0fa197ed9bcd034a9
- Size of remote file:
- 3.45 kB
- SHA256:
- e10215433b7ef3d28c2bf210ef278d4ad38d9480a1022688f9d1f4d507b67988
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