Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use caush/Clickbait2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use caush/Clickbait2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="caush/Clickbait2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("caush/Clickbait2") model = AutoModelForSequenceClassification.from_pretrained("caush/Clickbait2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c8f7c8fd9b7c56a43be9844713bdd468b7a17ed5170311ce768adfc6e2d41ea
- Size of remote file:
- 17.1 MB
- SHA256:
- 0b44a9d7b51c3c62626640cda0e2c2f70fdacdc25bbbd68038369d14ebdf4c39
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.