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:
- c42c9f6fddcfd0f3dc17aa4eefd01c47bf6a9fc655ef8040e7ef2b082b0d11e8
- Size of remote file:
- 5.11 kB
- SHA256:
- 1e53aee65e58eb7e30c9ba75f0000df2250a3c475d582ffa2323119abe0dcd5f
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