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:
- 66a10dd2da9c635535ca9a0c349124379feaacee85f89556c8c4b0807c7f2d3a
- Size of remote file:
- 3.38 kB
- SHA256:
- 1f34b55c624f165275474f48da086113cee4ae720a3c4eebb2c95e07204f5619
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