Instructions to use palakagl/distilbert_MultiClass_TextClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use palakagl/distilbert_MultiClass_TextClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="palakagl/distilbert_MultiClass_TextClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("palakagl/distilbert_MultiClass_TextClassification") model = AutoModelForSequenceClassification.from_pretrained("palakagl/distilbert_MultiClass_TextClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab67484b05d560e2e9a717902acc8254483f5c36987f0e35caaf7b73ffef4f55
- Size of remote file:
- 263 MB
- SHA256:
- 825278c99a7765582c075962015560661527229207cac274d2db31435440be79
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