LayoutLM-v3 model fine-tuned on invoice dataset
This model is a fine-tuned version of microsoft/layoutlmv3-base on the Rhenus dataset.
We use Microsoft’s LayoutLMv3 trained on Eurocorporation Dataset to predict the labels. To use it, simply upload an image. Results will show up in a few seconds.
It achieves the following results on the evaluation set:
- Loss: 0.0012
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
All the training codes are available from the below GitHub link.
The model can be evaluated at the HuggingFace Spaces link:
https://huggingface.co/DataIntelligenceTeam/Rhenus
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 5
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
Training results
Step | Training Loss | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|
100 | No log | 0.57765 | 0.568773 | 0.600000 | 0.583969 | 0.895848 |
200 | No log | 0.181364 | 0.933594 | 0.937255 | 0.935421 | 0.988037 |
300 | No log | 0.091626 | 0.945312 | 0.949020 | 0.947162 | 0.991555 |
400 | No log | 0.060504 | 0.964981 | 0.972549 | 0.968750 | 0.995074 |
500 | 0.360900 | 0.046041 | 0.988327 | 0.996078 | 0.992188 | 0.999296 |
600 | 0.360900 | 0.036889 | 0.988327 | 0.996078 | 0.992188 | 0.999296 |
700 | 0.360900 | 0.032077 | 0.988327 | 0.996078 | 0.992188 | 0.999296 |
800 | 0.360900 | 0.028109 | 0.988327 | 0.996078 | 0.992188 | 0.999296 |
900 | 0.360900 | 0.027945 | 0.988327 | 0.996078 | 0.992188 | 0.999296 |
1000 | 0.037800 | 0.027469 | 0.988327 | 0.996078 | 0.992188 | 0.999296 |
Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1