Instructions to use BenjaminOcampo/task-implicit_task__model-hatebert__aug_method-gm_revised 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-gm_revised 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-gm_revised")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BenjaminOcampo/task-implicit_task__model-hatebert__aug_method-gm_revised") model = AutoModelForSequenceClassification.from_pretrained("BenjaminOcampo/task-implicit_task__model-hatebert__aug_method-gm_revised", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 91e55670635d9b69afecb55d10421e171a1b5074fb16562f69ac657b0da418e1
- Size of remote file:
- 438 MB
- SHA256:
- f9c5a2f046119b51960056b10a1f2c6a7c28d6efbf688f49c89c2e3147d1b7a6
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