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:
- c59e5a3a16d0b74c661a6b6c8f296de9088b20d7c25ab17a92a7defea96ce6f3
- Size of remote file:
- 438 MB
- SHA256:
- 0beefe565e7439ef42235fc5efe71eadd1b7753d6bed36e238c13fc4c9d7d350
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