Instructions to use KgModel/IncomeStatement_Cashflow_BalanceStatement__Classifier_LayoutLMv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KgModel/IncomeStatement_Cashflow_BalanceStatement__Classifier_LayoutLMv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KgModel/IncomeStatement_Cashflow_BalanceStatement__Classifier_LayoutLMv3")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("KgModel/IncomeStatement_Cashflow_BalanceStatement__Classifier_LayoutLMv3") model = AutoModelForSequenceClassification.from_pretrained("KgModel/IncomeStatement_Cashflow_BalanceStatement__Classifier_LayoutLMv3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2aee539dd5438877f459c60afef8d794e85655e02934a2d6b34bd3ebb2baaf27
- Size of remote file:
- 504 MB
- SHA256:
- f6714eb8134b58b2155d4f2ce47acf5347a0ea446107f258ede1f12522da2354
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