Instructions to use LinaSaba/distilbert-base-task-multi-label-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LinaSaba/distilbert-base-task-multi-label-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LinaSaba/distilbert-base-task-multi-label-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LinaSaba/distilbert-base-task-multi-label-classification") model = AutoModelForSequenceClassification.from_pretrained("LinaSaba/distilbert-base-task-multi-label-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d6c236b977f9908cf52437aca0305ed8a8f93cc92b3238511e3237eb3d7ff42
- Size of remote file:
- 438 MB
- SHA256:
- e7aa634f9da37d2851945fa7e6a914d3ce206b126749f38df5a266936d20d31b
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