Instructions to use BenjaminOcampo/task-implicit_task__model-hatebert__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-hatebert__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-hatebert__aug_method-bt")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BenjaminOcampo/task-implicit_task__model-hatebert__aug_method-bt") model = AutoModelForSequenceClassification.from_pretrained("BenjaminOcampo/task-implicit_task__model-hatebert__aug_method-bt") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 687a435fd962a53712f6af027fbde1f64a51465ff65b314d2976cad587cccd07
- Size of remote file:
- 438 MB
- SHA256:
- 3504f78d9b1ba7f56db0c4fd982c01c57efeb8452bffda98e2e6a4c88017810d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.