Instructions to use vikp/pdf_postprocessor_t5_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vikp/pdf_postprocessor_t5_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vikp/pdf_postprocessor_t5_base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("vikp/pdf_postprocessor_t5_base") model = AutoModelForTokenClassification.from_pretrained("vikp/pdf_postprocessor_t5_base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e40d3b11eb2bc6e5b2c9375d5f43c71ff692a4b37a6586bd9603c7941666c8c7
- Size of remote file:
- 1.66 GB
- SHA256:
- fc223e287f3037dd6ac4dec4580d703b4294a87923fbe74859a18bd86431c191
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