Instructions to use pardeepSF/layoutlm-vqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pardeepSF/layoutlm-vqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="pardeepSF/layoutlm-vqa")# Load model directly from transformers import AutoTokenizer, AutoModelForDocumentQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("pardeepSF/layoutlm-vqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("pardeepSF/layoutlm-vqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 532 Bytes
62ab108 cec27ac 62ab108 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": true,
"mask_token": "[MASK]",
"max_len": 512,
"name_or_path": "microsoft/layoutlm-large-uncased",
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"special_tokens_map_file": "data/models/models--microsoft--layoutlm-large-uncased/snapshots/1e7d50dced3cdfea3a3d63c610e2aab36933dbef/special_tokens_map.json",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
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