Instructions to use karths/binary_classification_train_TD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_TD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_TD")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_TD") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_TD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 289d4bc232799c66200a0e22d3476499ba0fec41e346285a1a6af17bf5775310
- Size of remote file:
- 657 MB
- SHA256:
- 371f130749c5cb8e77ab314939b61a3e364bb227f9e56de04e85b5d9691e0066
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.