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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cord-layoutlmv3
type: cord-layoutlmv3
config: cord
split: train
args: cord
metrics:
- name: Precision
type: precision
value: 0.9415247964470762
- name: Recall
type: recall
value: 0.9520958083832335
- name: F1
type: f1
value: 0.9467807964272422
- name: Accuracy
type: accuracy
value: 0.9575551782682513
---
<!-- 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 the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2246
- Precision: 0.9415
- Recall: 0.9521
- F1: 0.9468
- Accuracy: 0.9576
## 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 | 1.56 | 250 | 1.0265 | 0.7630 | 0.8099 | 0.7858 | 0.8086 |
| 1.4021 | 3.12 | 500 | 0.5804 | 0.8290 | 0.8638 | 0.8460 | 0.8718 |
| 1.4021 | 4.69 | 750 | 0.3937 | 0.8882 | 0.9034 | 0.8957 | 0.9126 |
| 0.4062 | 6.25 | 1000 | 0.3171 | 0.9137 | 0.9274 | 0.9205 | 0.9351 |
| 0.4062 | 7.81 | 1250 | 0.2798 | 0.9332 | 0.9409 | 0.9370 | 0.9444 |
| 0.2212 | 9.38 | 1500 | 0.2558 | 0.9277 | 0.9416 | 0.9346 | 0.9461 |
| 0.2212 | 10.94 | 1750 | 0.2479 | 0.9335 | 0.9454 | 0.9394 | 0.9516 |
| 0.1525 | 12.5 | 2000 | 0.2356 | 0.9444 | 0.9536 | 0.9490 | 0.9588 |
| 0.1525 | 14.06 | 2250 | 0.2286 | 0.9365 | 0.9491 | 0.9428 | 0.9563 |
| 0.1134 | 15.62 | 2500 | 0.2246 | 0.9415 | 0.9521 | 0.9468 | 0.9576 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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