Instructions to use BenjaminOcampo/task-implicit_task__model-bert__aug_method-ri with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BenjaminOcampo/task-implicit_task__model-bert__aug_method-ri with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BenjaminOcampo/task-implicit_task__model-bert__aug_method-ri")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BenjaminOcampo/task-implicit_task__model-bert__aug_method-ri") model = AutoModelForSequenceClassification.from_pretrained("BenjaminOcampo/task-implicit_task__model-bert__aug_method-ri", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a21e3fe39a533240113c4a2455d4da846a12c57a003586bf429890eb0cbb6889
- Size of remote file:
- 3.39 kB
- SHA256:
- 3f960ba2f1b6810498e94345f22d676dc095da70443468a0d97625ab8ddd6372
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.