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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv2-base-uncased
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
|