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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_200
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: 0.9033923303834809
- name: Recall
type: recall
value: 0.9169161676646707
- name: F1
type: f1
value: 0.9101040118870729
- name: Accuracy
type: accuracy
value: 0.9121392190152802
---
<!-- 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_200
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: 0.4529
- Precision: 0.9034
- Recall: 0.9169
- F1: 0.9101
- Accuracy: 0.9121
## 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: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 6.25 | 250 | 1.0785 | 0.6815 | 0.7575 | 0.7175 | 0.7780 |
| 1.3902 | 12.5 | 500 | 0.5871 | 0.8542 | 0.8683 | 0.8612 | 0.8604 |
| 1.3902 | 18.75 | 750 | 0.4572 | 0.8728 | 0.8937 | 0.8831 | 0.8905 |
| 0.298 | 25.0 | 1000 | 0.3947 | 0.8936 | 0.9117 | 0.9026 | 0.9092 |
| 0.298 | 31.25 | 1250 | 0.3925 | 0.8982 | 0.9177 | 0.9078 | 0.9117 |
| 0.1023 | 37.5 | 1500 | 0.4290 | 0.8908 | 0.9102 | 0.9004 | 0.9041 |
| 0.1023 | 43.75 | 1750 | 0.4220 | 0.8980 | 0.9162 | 0.9070 | 0.9117 |
| 0.0475 | 50.0 | 2000 | 0.4755 | 0.8944 | 0.9064 | 0.9004 | 0.8990 |
| 0.0475 | 56.25 | 2250 | 0.4635 | 0.8992 | 0.9147 | 0.9069 | 0.9070 |
| 0.0296 | 62.5 | 2500 | 0.4475 | 0.9019 | 0.9154 | 0.9086 | 0.9117 |
| 0.0296 | 68.75 | 2750 | 0.4484 | 0.9004 | 0.9139 | 0.9071 | 0.9079 |
| 0.0242 | 75.0 | 3000 | 0.4529 | 0.9034 | 0.9169 | 0.9101 | 0.9121 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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