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---
tags:
- generated_from_trainer
datasets:
- mp-02/funsd
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-funsd
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: mp-02/funsd
type: mp-02/funsd
metrics:
- name: Precision
type: precision
value: 0.8746976294146106
- name: Recall
type: recall
value: 0.904
- name: F1
type: f1
value: 0.8891074502089993
- name: Accuracy
type: accuracy
value: 0.8368167202572347
---
<!-- 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 [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6541
- Precision: 0.8747
- Recall: 0.904
- F1: 0.8891
- Accuracy: 0.8368
## 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: 6
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 25 | 1.2831 | 0.4033 | 0.4795 | 0.4381 | 0.6092 |
| No log | 2.0 | 50 | 0.8178 | 0.7266 | 0.7935 | 0.7586 | 0.7748 |
| No log | 3.0 | 75 | 0.6843 | 0.7951 | 0.8345 | 0.8143 | 0.7990 |
| No log | 4.0 | 100 | 0.6317 | 0.8024 | 0.861 | 0.8307 | 0.8161 |
| No log | 5.0 | 125 | 0.5964 | 0.8260 | 0.897 | 0.8600 | 0.8234 |
| No log | 6.0 | 150 | 0.6050 | 0.8204 | 0.87 | 0.8445 | 0.8207 |
| No log | 7.0 | 175 | 0.6281 | 0.8203 | 0.8765 | 0.8475 | 0.8168 |
| No log | 8.0 | 200 | 0.6228 | 0.8449 | 0.8985 | 0.8709 | 0.8235 |
| No log | 9.0 | 225 | 0.6213 | 0.8345 | 0.88 | 0.8567 | 0.8266 |
| No log | 10.0 | 250 | 0.6173 | 0.8450 | 0.897 | 0.8702 | 0.8357 |
| No log | 11.0 | 275 | 0.6476 | 0.8388 | 0.8895 | 0.8634 | 0.8299 |
| No log | 12.0 | 300 | 0.6359 | 0.8584 | 0.8945 | 0.8761 | 0.8382 |
| No log | 13.0 | 325 | 0.6469 | 0.8759 | 0.907 | 0.8912 | 0.8395 |
| No log | 14.0 | 350 | 0.6510 | 0.8729 | 0.9035 | 0.8880 | 0.8373 |
| No log | 15.0 | 375 | 0.6555 | 0.8656 | 0.902 | 0.8834 | 0.8354 |
| No log | 16.0 | 400 | 0.6541 | 0.8747 | 0.904 | 0.8891 | 0.8368 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 2.13.2
- Tokenizers 0.10.1
|