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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-funsd2
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-finetuned-funsd2
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8330
- Precision: 0.9046
- Recall: 0.9105
- F1: 0.9076
- Accuracy: 0.8536
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.32 | 50 | 0.6163 | 0.8088 | 0.8965 | 0.8504 | 0.8088 |
| No log | 2.63 | 100 | 0.5416 | 0.8037 | 0.868 | 0.8346 | 0.8134 |
| No log | 3.95 | 150 | 0.5572 | 0.8446 | 0.8885 | 0.8660 | 0.8385 |
| No log | 5.26 | 200 | 0.7317 | 0.8458 | 0.8555 | 0.8506 | 0.8124 |
| No log | 6.58 | 250 | 0.7220 | 0.8877 | 0.8935 | 0.8906 | 0.8385 |
| No log | 7.89 | 300 | 0.8070 | 0.8778 | 0.9055 | 0.8915 | 0.8436 |
| No log | 9.21 | 350 | 0.7895 | 0.8969 | 0.913 | 0.9049 | 0.8477 |
| No log | 10.53 | 400 | 0.8168 | 0.8935 | 0.889 | 0.8912 | 0.8412 |
| No log | 11.84 | 450 | 0.8233 | 0.8955 | 0.917 | 0.9061 | 0.8521 |
| 0.2564 | 13.16 | 500 | 0.8330 | 0.9046 | 0.9105 | 0.9076 | 0.8536 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 2.13.2
- Tokenizers 0.10.1
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