Instructions to use BenjaminOcampo/task-subtle_task__model-bert__aug_method-gm 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-gm 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-gm")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BenjaminOcampo/task-subtle_task__model-bert__aug_method-gm") model = AutoModelForSequenceClassification.from_pretrained("BenjaminOcampo/task-subtle_task__model-bert__aug_method-gm") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94e0a7a4e17000853c5fdd5c6aab794fcd375a506f6c6bd59b344500f45533d6
- Size of remote file:
- 438 MB
- SHA256:
- 6865d20748a20776ea65bb48a23046418fa0d33171a9372b13a354f0a8dc0fa3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.