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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e40af7c0b0e99383c3b5b060856c1dd0da5590a8cef4576d4ed3087104829500
- Size of remote file:
- 438 MB
- SHA256:
- 5af75fdfbdb51c954f649ed5dbf73ea61b95d729457316b8b1064e5681fc43ec
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.