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license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LayoutLMv3_97_1
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_97_1
This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8446
- Precision: 0.5939
- Recall: 0.8376
- F1: 0.6950
- Accuracy: 0.8952
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 2.44 | 100 | 0.4463 | 0.4830 | 0.7265 | 0.5802 | 0.8599 |
| No log | 4.88 | 200 | 0.4064 | 0.5924 | 0.7949 | 0.6788 | 0.8884 |
| No log | 7.32 | 300 | 0.4774 | 0.5813 | 0.7949 | 0.6715 | 0.8907 |
| No log | 9.76 | 400 | 0.5800 | 0.6013 | 0.7863 | 0.6815 | 0.8907 |
| 0.2076 | 12.2 | 500 | 0.6426 | 0.6209 | 0.8120 | 0.7037 | 0.8952 |
| 0.2076 | 14.63 | 600 | 0.6872 | 0.5939 | 0.8376 | 0.6950 | 0.8907 |
| 0.2076 | 17.07 | 700 | 0.7801 | 0.5915 | 0.8291 | 0.6904 | 0.8918 |
| 0.2076 | 19.51 | 800 | 0.7865 | 0.5890 | 0.8205 | 0.6857 | 0.8895 |
| 0.2076 | 21.95 | 900 | 0.8533 | 0.5854 | 0.8205 | 0.6833 | 0.8895 |
| 0.0109 | 24.39 | 1000 | 0.7738 | 0.5864 | 0.8120 | 0.6810 | 0.8941 |
| 0.0109 | 26.83 | 1100 | 0.8297 | 0.5854 | 0.8205 | 0.6833 | 0.8872 |
| 0.0109 | 29.27 | 1200 | 0.7690 | 0.6062 | 0.8291 | 0.7004 | 0.8975 |
| 0.0109 | 31.71 | 1300 | 0.8629 | 0.5904 | 0.8376 | 0.6926 | 0.8895 |
| 0.0109 | 34.15 | 1400 | 0.8104 | 0.5976 | 0.8376 | 0.6975 | 0.8941 |
| 0.0027 | 36.59 | 1500 | 0.7864 | 0.5926 | 0.8205 | 0.6882 | 0.8929 |
| 0.0027 | 39.02 | 1600 | 0.8002 | 0.6037 | 0.8462 | 0.7046 | 0.8986 |
| 0.0027 | 41.46 | 1700 | 0.8049 | 0.5964 | 0.8462 | 0.6996 | 0.8964 |
| 0.0027 | 43.9 | 1800 | 0.8355 | 0.5939 | 0.8376 | 0.6950 | 0.8952 |
| 0.0027 | 46.34 | 1900 | 0.8402 | 0.5939 | 0.8376 | 0.6950 | 0.8952 |
| 0.001 | 48.78 | 2000 | 0.8446 | 0.5939 | 0.8376 | 0.6950 | 0.8952 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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