Instructions to use BenjaminOcampo/task-implicit_task__model-deberta__aug_method-bt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BenjaminOcampo/task-implicit_task__model-deberta__aug_method-bt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BenjaminOcampo/task-implicit_task__model-deberta__aug_method-bt")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BenjaminOcampo/task-implicit_task__model-deberta__aug_method-bt") model = AutoModelForSequenceClassification.from_pretrained("BenjaminOcampo/task-implicit_task__model-deberta__aug_method-bt") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e812b2aa94fe6b0699e6e0a195cda4ca66899bc151bdb016a1c9d6f6894c5391
- Size of remote file:
- 738 MB
- SHA256:
- 47d948b9d591af30eece5980fbf5b954fbaab244348384d701991b02b89ab711
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.