20231109_layoutlm2_5k_20ep_Doc_NA

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results

This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: nan

Model description

This DocVQA model, built on the Layout LM v2 framework, represents an initial step in a series of experimental models aimed at document visual question answering. It's the "medium" version in a planned series, trained on a mid-sized dataset of 5k samples (split between training and test) over 20 epochs. The training setup was modest, employing mixed precision (fp16), with manageable batch sizes and a focused approach to learning rate adjustment (warmup steps and weight decay). Notably, this model was trained without external reporting tools, emphasizing internal evaluation. As the first iteration in a progressive series that will later include medium (5k samples) and large (50k samples) models, this version serves as a foundational experiment, setting the stage for more extensive and complex models in the future.

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: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 20

Training results

Framework versions

• Transformers 4.34.1
• Pytorch 2.0.1+cu118
• Datasets 2.10.1
• Tokenizers 0.14.1