Instructions to use karths/binary_classification_train_requirement with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_requirement with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_requirement")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_requirement") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_requirement") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d7bf9f3cb50e86ee50b25894fe3b86c73bb37d9f6cb83f8e709a4b0dbeaf6ab8
- Size of remote file:
- 657 MB
- SHA256:
- f232a70322a5f80d2df3fb410c2d207ecc26bbc8182be23ddfd71321bc81425e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.