Instructions to use djsull/binary_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djsull/binary_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="djsull/binary_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("djsull/binary_classification") model = AutoModelForSequenceClassification.from_pretrained("djsull/binary_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fcc3350f291894a8299d9d4c6ae3a1e6b5acd947a955b0cda2c299a8520b7632
- Size of remote file:
- 272 MB
- SHA256:
- 5eb8f00dc2e323e65cc0a4bc3ae84ba8fab918726e1841ac7bcc3ecdc69417f5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.