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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_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-funsd_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an FUNSD dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5728
- Precision: 0.8172
- Recall: 0.8664
- F1: 0.8411
- Accuracy: 0.8318
## 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: 250
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.83 | 25 | 1.3530 | 0.2996 | 0.3040 | 0.3018 | 0.5402 |
| No log | 1.67 | 50 | 0.9373 | 0.6537 | 0.7193 | 0.6850 | 0.7412 |
| No log | 2.5 | 75 | 0.7492 | 0.7574 | 0.8018 | 0.7790 | 0.7748 |
| No log | 3.33 | 100 | 0.6587 | 0.7721 | 0.8097 | 0.7905 | 0.7900 |
| No log | 4.17 | 125 | 0.6224 | 0.7808 | 0.8336 | 0.8063 | 0.8005 |
| No log | 5.0 | 150 | 0.5720 | 0.7870 | 0.8445 | 0.8148 | 0.8171 |
| No log | 5.83 | 175 | 0.5343 | 0.8164 | 0.8549 | 0.8352 | 0.8250 |
| No log | 6.67 | 200 | 0.5856 | 0.8139 | 0.8604 | 0.8365 | 0.8268 |
| No log | 7.5 | 225 | 0.5787 | 0.8166 | 0.8624 | 0.8388 | 0.8266 |
| No log | 8.33 | 250 | 0.5728 | 0.8172 | 0.8664 | 0.8411 | 0.8318 |
### Framework versions
- Transformers 4.39.2
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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