Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use padmajabfrl/Religion-Classification-Custom-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use padmajabfrl/Religion-Classification-Custom-Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="padmajabfrl/Religion-Classification-Custom-Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("padmajabfrl/Religion-Classification-Custom-Model") model = AutoModelForSequenceClassification.from_pretrained("padmajabfrl/Religion-Classification-Custom-Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb044bd68e2eea92782e8eb093d9dd5433e7a28648ab1c8f1a808b222f0d663c
- Size of remote file:
- 268 MB
- SHA256:
- 3c5286e45403e317d38fcedb9215c20373db31048f02743162cf57399c33e3ee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.