Instructions to use BenjaminOcampo/task-subtle_task__model-bert__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-bert__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-bert__aug_method-eda")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BenjaminOcampo/task-subtle_task__model-bert__aug_method-eda") model = AutoModelForSequenceClassification.from_pretrained("BenjaminOcampo/task-subtle_task__model-bert__aug_method-eda") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 914b23c9206efb8a7337f3949907a9b41d44c9cd5e60293242d145c190d94407
- Size of remote file:
- 438 MB
- SHA256:
- 196f06bb8efb4ad530cb63d578a8cf4859a2906fc74b8064a980f8a8b10b1b1a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.