# Load model directly
from transformers import AutoTokenizer, AutoModelForDocumentQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("kanansharmaa/layoutlm_document_qa")
model = AutoModelForDocumentQuestionAnswering.from_pretrained("kanansharmaa/layoutlm_document_qa")Quick Links
layoutlm_document_qa
This model is a fine-tuned version of impira/layoutlm-document-qa on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.17.1.dev0
- Tokenizers 0.15.2
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Model tree for kanansharmaa/layoutlm_document_qa
Base model
impira/layoutlm-document-qa
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="kanansharmaa/layoutlm_document_qa")