Instructions to use BSC-LT/NextProcurement_pdfutils with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BSC-LT/NextProcurement_pdfutils with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BSC-LT/NextProcurement_pdfutils")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BSC-LT/NextProcurement_pdfutils") model = AutoModelForSequenceClassification.from_pretrained("BSC-LT/NextProcurement_pdfutils", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57dc8cfc70c7c66884ecb5bc18b139488c0b94015ba3658985b932e9a24fe462
- Size of remote file:
- 711 MB
- SHA256:
- f34375f243a24dafa3171e3090a295877acb3dc62aac4a5983fa4becb80e89d8
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