Instructions to use TomasFAV/BERTInvoiceCzechV0123 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TomasFAV/BERTInvoiceCzechV0123 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="TomasFAV/BERTInvoiceCzechV0123")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("TomasFAV/BERTInvoiceCzechV0123") model = AutoModelForTokenClassification.from_pretrained("TomasFAV/BERTInvoiceCzechV0123", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- caece1b3ff2d958f8e272b8593afe733fa6a85267c930641ff6fba165a58ad7e
- Size of remote file:
- 5.2 kB
- SHA256:
- a56b5269cc59512cfd5c7db740d16c8bf5488ef1ed141eb3ac9bc9d54f58b696
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