Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use jkhan447/HateXplain-third-annotator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jkhan447/HateXplain-third-annotator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jkhan447/HateXplain-third-annotator")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jkhan447/HateXplain-third-annotator") model = AutoModelForSequenceClassification.from_pretrained("jkhan447/HateXplain-third-annotator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01cd7d9dc7a93a733bf2cb45cc5d892289aa4a6e2769e216251a0ffd2914e0d9
- Size of remote file:
- 3.45 kB
- SHA256:
- dfe68188cb6551ad80c36df2ad01628096953fa85fae7a5a1bc946ffbce533a2
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