Instructions to use hf-internal-testing/tiny-random-layoutlmv2-for-dqa-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-layoutlmv2-for-dqa-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="hf-internal-testing/tiny-random-layoutlmv2-for-dqa-test")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-layoutlmv2-for-dqa-test") model = AutoModelForDocumentQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-layoutlmv2-for-dqa-test") - Notebooks
- Google Colab
- Kaggle
File size: 430 Bytes
0608988 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"_valid_processor_keys": [
"images",
"do_resize",
"size",
"resample",
"apply_ocr",
"ocr_lang",
"tesseract_config",
"return_tensors",
"data_format",
"input_data_format"
],
"apply_ocr": true,
"do_resize": true,
"image_processor_type": "LayoutLMv2ImageProcessor",
"ocr_lang": null,
"resample": 2,
"size": {
"height": 224,
"width": 224
},
"tesseract_config": ""
}
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