Instructions to use BenjaminOcampo/task-subtle_task__model-deberta__aug_method-eda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BenjaminOcampo/task-subtle_task__model-deberta__aug_method-eda with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BenjaminOcampo/task-subtle_task__model-deberta__aug_method-eda")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BenjaminOcampo/task-subtle_task__model-deberta__aug_method-eda") model = AutoModelForSequenceClassification.from_pretrained("BenjaminOcampo/task-subtle_task__model-deberta__aug_method-eda") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54fd7cf98b02c02f9a16836db7c20488d4f51f3f4f197843b1e67300efc7a9a5
- Size of remote file:
- 738 MB
- SHA256:
- 9a0c53d0773ff36230bd789df0fbf0befe01b8a9a1a80b91f39e0a8c9bbc6ad4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.