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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-cord_100
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-cord_100
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: 0.2879
- Precision: 0.9258
- Recall: 0.9384
- F1: 0.9321
- Accuracy: 0.9474
## 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: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 4.17 | 250 | 0.6982 | 0.7974 | 0.8017 | 0.7995 | 0.8265 |
| 0.9999 | 8.33 | 500 | 0.4214 | 0.8603 | 0.8754 | 0.8678 | 0.8936 |
| 0.9999 | 12.5 | 750 | 0.2820 | 0.9081 | 0.9164 | 0.9123 | 0.9364 |
| 0.1912 | 16.67 | 1000 | 0.2710 | 0.9147 | 0.9293 | 0.9220 | 0.9389 |
| 0.1912 | 20.83 | 1250 | 0.2748 | 0.9125 | 0.9271 | 0.9197 | 0.9406 |
| 0.061 | 25.0 | 1500 | 0.2612 | 0.9220 | 0.9339 | 0.9279 | 0.9474 |
| 0.061 | 29.17 | 1750 | 0.2731 | 0.9300 | 0.9384 | 0.9342 | 0.9478 |
| 0.0275 | 33.33 | 2000 | 0.2824 | 0.9279 | 0.9384 | 0.9331 | 0.9487 |
| 0.0275 | 37.5 | 2250 | 0.2886 | 0.9242 | 0.9362 | 0.9302 | 0.9457 |
| 0.0168 | 41.67 | 2500 | 0.2879 | 0.9258 | 0.9384 | 0.9321 | 0.9474 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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