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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13141033346907fdcfcf60ca691d335e5eee3762ee2bd4b2c4ee442e498c5ec8
- Size of remote file:
- 4.6 kB
- SHA256:
- d6629bc04a316626288dae65b6a1ea26dfef335e52f895119a9c29bde40ac19d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.