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:
- 578ab0759692d35eb30b330a3398ae5a93cb3f3a58bf8c8fbb907b4ed7ab061d
- Size of remote file:
- 438 MB
- SHA256:
- 89c3e939b8985d0f1607a3ae98f14370b80ec600c93164e40f8930522f882b6a
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