Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use theojolliffe/classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use theojolliffe/classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="theojolliffe/classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("theojolliffe/classifier") model = AutoModelForSequenceClassification.from_pretrained("theojolliffe/classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8ff41839d7ca0ed796038186ce03854c9c26fe5a3f490d302fdc7082f7fd6d2
- Size of remote file:
- 446 MB
- SHA256:
- fa94e69c50b70a8e5494e6a45676feeec9c7977061b6569b8cf726ffbf0c27a3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.