Instructions to use faisalraza/layoutlm-invoices with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use faisalraza/layoutlm-invoices with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="faisalraza/layoutlm-invoices")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForDocumentQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("faisalraza/layoutlm-invoices") model = AutoModelForDocumentQuestionAnswering.from_pretrained("faisalraza/layoutlm-invoices", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from faisalraza/layoutlm-invoices: direct link, hf CLI and curl.
- Browser
- Download file 511 MB
-
https://huggingface.co/faisalraza/layoutlm-invoices/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://faisalraza/layoutlm-invoices/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/faisalraza/layoutlm-invoices/resolve/main/pytorch_model.bin
511 MB
- Xet hash:
- e0f20dd8e19946c54f28bccc4fe8ebf9487f1cfd188d8769ae345e4aab202946
- Size of remote file:
- 511 MB
- SHA256:
- bacb21323970333f86ca1768bc8bd925471e8495521eae181cdff984c11b94f3
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