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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-funsd
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. -->
# layoutlmv3-funsd
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8428
- Precision: 0.8993
- Recall: 0.9046
- F1: 0.9019
- Accuracy: 0.8354
## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 2.63 | 100 | 0.6294 | 0.7864 | 0.8286 | 0.8070 | 0.7966 |
| No log | 5.26 | 200 | 0.5034 | 0.8389 | 0.8793 | 0.8586 | 0.8343 |
| No log | 7.89 | 300 | 0.5673 | 0.8597 | 0.9011 | 0.8799 | 0.8416 |
| No log | 10.53 | 400 | 0.5730 | 0.8783 | 0.9106 | 0.8941 | 0.8395 |
| 0.4463 | 13.16 | 500 | 0.6630 | 0.8923 | 0.9016 | 0.8970 | 0.8412 |
| 0.4463 | 15.79 | 600 | 0.7048 | 0.8850 | 0.8947 | 0.8898 | 0.8329 |
| 0.4463 | 18.42 | 700 | 0.7772 | 0.8925 | 0.9071 | 0.8997 | 0.8317 |
| 0.4463 | 21.05 | 800 | 0.8408 | 0.8959 | 0.9016 | 0.8987 | 0.8313 |
| 0.4463 | 23.68 | 900 | 0.8580 | 0.8918 | 0.9051 | 0.8984 | 0.8313 |
| 0.0611 | 26.32 | 1000 | 0.8428 | 0.8993 | 0.9046 | 0.9019 | 0.8354 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.16.1
- Tokenizers 0.15.0
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