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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-finetuned-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-finetuned-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: 1.1951
- Precision: 0.9104
- Recall: 0.9086
- F1: 0.9095
- Accuracy: 0.8530
## 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: 5
- eval_batch_size: 5
- 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 | 3.3333 | 100 | 0.8172 | 0.8957 | 0.9046 | 0.9001 | 0.8418 |
| No log | 6.6667 | 200 | 0.8379 | 0.8870 | 0.9160 | 0.9013 | 0.8381 |
| No log | 10.0 | 300 | 0.9611 | 0.8887 | 0.9041 | 0.8963 | 0.8328 |
| No log | 13.3333 | 400 | 0.9324 | 0.8888 | 0.9091 | 0.8988 | 0.8438 |
| 0.0651 | 16.6667 | 500 | 0.9475 | 0.8928 | 0.9185 | 0.9055 | 0.8511 |
| 0.0651 | 20.0 | 600 | 1.1234 | 0.8834 | 0.9031 | 0.8931 | 0.8343 |
| 0.0651 | 23.3333 | 700 | 1.1130 | 0.8921 | 0.8957 | 0.8939 | 0.8254 |
| 0.0651 | 26.6667 | 800 | 1.0760 | 0.8931 | 0.9175 | 0.9052 | 0.8416 |
| 0.0651 | 30.0 | 900 | 1.1777 | 0.8894 | 0.9031 | 0.8962 | 0.8336 |
| 0.0115 | 33.3333 | 1000 | 1.2102 | 0.9025 | 0.9101 | 0.9063 | 0.8387 |
| 0.0115 | 36.6667 | 1100 | 1.1602 | 0.9012 | 0.9111 | 0.9061 | 0.8467 |
| 0.0115 | 40.0 | 1200 | 1.1819 | 0.9011 | 0.9101 | 0.9056 | 0.8433 |
| 0.0115 | 43.3333 | 1300 | 1.2095 | 0.9051 | 0.9051 | 0.9051 | 0.8452 |
| 0.0115 | 46.6667 | 1400 | 1.1687 | 0.9064 | 0.9185 | 0.9124 | 0.8570 |
| 0.0031 | 50.0 | 1500 | 1.1951 | 0.9104 | 0.9086 | 0.9095 | 0.8530 |
| 0.0031 | 53.3333 | 1600 | 1.1967 | 0.9041 | 0.9131 | 0.9086 | 0.8530 |
| 0.0031 | 56.6667 | 1700 | 1.1989 | 0.9015 | 0.9091 | 0.9053 | 0.8531 |
| 0.0031 | 60.0 | 1800 | 1.1973 | 0.9000 | 0.9126 | 0.9063 | 0.8549 |
| 0.0031 | 63.3333 | 1900 | 1.2135 | 0.9015 | 0.9096 | 0.9055 | 0.8490 |
| 0.0012 | 66.6667 | 2000 | 1.2210 | 0.9015 | 0.9091 | 0.9053 | 0.8469 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1
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