transformers pytorch layoutlmv2 document-question-answering generated_from_trainer base_model:microsoft/layoutlmv2-base-uncased base_model:finetune:microsoft/layoutlmv2-base-uncased license:cc-by-nc-sa-4.0 endpoints_compatible region:us
---
base_model: microsoft/layoutlmv2-base-uncased
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: results
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# results

This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/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

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 0.99  | 62   | 5.4841          |
| No log        | 2.0   | 125  | 4.6253          |
| No log        | 2.99  | 187  | 4.3093          |
| No log        | 4.0   | 250  | 4.0361          |
| No log        | 4.99  | 312  | 3.6892          |
| No log        | 6.0   | 375  | 3.3862          |
| No log        | 6.99  | 437  | 3.0017          |
| 4.3469        | 8.0   | 500  | nan             |
| 4.3469        | 8.99  | 562  | nan             |
| 4.3469        | 10.0  | 625  | nan             |
| 4.3469        | 10.99 | 687  | nan             |
| 4.3469        | 12.0  | 750  | nan             |
| 4.3469        | 12.99 | 812  | nan             |
| 4.3469        | 14.0  | 875  | nan             |
| 4.3469        | 14.99 | 937  | nan             |
| 21709.916     | 16.0  | 1000 | nan             |
| 21709.916     | 16.99 | 1062 | nan             |
| 21709.916     | 18.0  | 1125 | nan             |
| 21709.916     | 18.99 | 1187 | nan             |
| 21709.916     | 19.84 | 1240 | nan             |


### Framework versions

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