Instructions to use devangb4/scikit-issues-distillbert-multilabel-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devangb4/scikit-issues-distillbert-multilabel-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="devangb4/scikit-issues-distillbert-multilabel-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("devangb4/scikit-issues-distillbert-multilabel-classification") model = AutoModelForSequenceClassification.from_pretrained("devangb4/scikit-issues-distillbert-multilabel-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5262df0006e58a6aed1e7031ec0edd54722c1fbf383a79e6c151bc3a4ccb371e
- Size of remote file:
- 268 MB
- SHA256:
- 8154cc3b722a6b5b7baa0378aa5a50f801cb6ba14b522fea93ede5237309b86f
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