Instructions to use FuriouslyAsleep/markingMultiClass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FuriouslyAsleep/markingMultiClass with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FuriouslyAsleep/markingMultiClass")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FuriouslyAsleep/markingMultiClass") model = AutoModelForSequenceClassification.from_pretrained("FuriouslyAsleep/markingMultiClass", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b539132e0d6d55ecac5becf92f58047218d797113695f45b284180e9020007f
- Size of remote file:
- 268 MB
- SHA256:
- d162dd2736897bd4f5275d3de4c19d227065cd128e35c13a157a77022a77c6a1
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