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")# pip install -U transformers accelerate # 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
Download optimizer.pt from colinglab/UMBERTO_fine-tuned_DocClass_PA: direct link, hf CLI and curl.
- Browser
- Download file 885 MB
-
https://huggingface.co/colinglab/UMBERTO_fine-tuned_DocClass_PA/resolve/main/optimizer.pt
- Command line
-
hf download hf://colinglab/UMBERTO_fine-tuned_DocClass_PA/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/colinglab/UMBERTO_fine-tuned_DocClass_PA/resolve/main/optimizer.pt
885 MB
- Xet hash:
- 5ccf001b72313a1a2c44974f83730ce405d120fe17b3b62869f95f4af858e3b1
- Size of remote file:
- 885 MB
- SHA256:
- c272a194f70cf90477a5bd5350993d5c702ae745f39792b42f63c5d82e7c697b
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