Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use padmajabfrl/demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use padmajabfrl/demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="padmajabfrl/demo")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("padmajabfrl/demo") model = AutoModelForSequenceClassification.from_pretrained("padmajabfrl/demo") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 43557e3b51442964f44b257505ab5423adbfb75bb9027ae63a9069988301d21a
- Size of remote file:
- 268 MB
- SHA256:
- 5c89fb5ad8e626a111c8344c119ef892b3f16e98afdc63fd905591f755fe6dd7
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