Instructions to use BenjaminOcampo/task-implicit_task__model-hatebert__aug_method-eda 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-eda 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-eda")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BenjaminOcampo/task-implicit_task__model-hatebert__aug_method-eda") model = AutoModelForSequenceClassification.from_pretrained("BenjaminOcampo/task-implicit_task__model-hatebert__aug_method-eda", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66feed4079634fdaa87837d4ffc27ce2eb80d2c7ec1af2694c3e478cee559673
- Size of remote file:
- 438 MB
- SHA256:
- 896755d036ebb9f14d0c4bce421a2411b82105fa8404ec488a1b88fc6f5cb7a3
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