Instructions to use colinglab/UMBERTO_fine-tuned_DocClass_PA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use colinglab/UMBERTO_fine-tuned_DocClass_PA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="colinglab/UMBERTO_fine-tuned_DocClass_PA")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("colinglab/UMBERTO_fine-tuned_DocClass_PA") model = AutoModelForSequenceClassification.from_pretrained("colinglab/UMBERTO_fine-tuned_DocClass_PA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6dbafcac38233c5787c2ed136ff47491de9fd2416da64157204d487a58f98911
- Size of remote file:
- 623 Bytes
- SHA256:
- ba822b4b83e2e2dc8d2975326826bcb83ca1fb1f42a124e363f75d0087bfd56b
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