Instructions to use karths/binary_classification_train_build with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_build with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_build")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_build") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_build") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 40dff9350c57b852ad6d6e971e8257e4266b6ccb61df11a6857d26c936eb8d23
- Size of remote file:
- 40.1 MB
- SHA256:
- 295d7828a30a1ba76e323db3766d15240b86799484e5d4b2f82cd9f1bd394b2c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.