Instructions to use paritc/multilabelclassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paritc/multilabelclassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="paritc/multilabelclassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("paritc/multilabelclassification") model = AutoModelForSequenceClassification.from_pretrained("paritc/multilabelclassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf974a8a46c70e0c4b1623e01f54a98f2d864605d84a1312867117782d617ec6
- Size of remote file:
- 438 MB
- SHA256:
- 47b3d9d3f1a3e36f1af022f39449c21a1757d4372c62f85f61b55d51ddfb9803
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