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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: 0.9524870081662955
- name: Recall
type: recall
value: 0.9603293413173652
- name: F1
type: f1
value: 0.9563920983973164
- name: Accuracy
type: accuracy
value: 0.9647707979626485
---
<!-- 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: 0.1948
- Precision: 0.9525
- Recall: 0.9603
- F1: 0.9564
- Accuracy: 0.9648
## 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: 1.1e-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 | 1.56 | 250 | 0.9568 | 0.7298 | 0.7844 | 0.7561 | 0.7992 |
| 1.3271 | 3.12 | 500 | 0.5239 | 0.8398 | 0.8713 | 0.8553 | 0.8858 |
| 1.3271 | 4.69 | 750 | 0.3586 | 0.8945 | 0.9207 | 0.9074 | 0.9300 |
| 0.3495 | 6.25 | 1000 | 0.2716 | 0.9298 | 0.9416 | 0.9357 | 0.9410 |
| 0.3495 | 7.81 | 1250 | 0.2331 | 0.9198 | 0.9356 | 0.9276 | 0.9474 |
| 0.1725 | 9.38 | 1500 | 0.2134 | 0.9379 | 0.9499 | 0.9438 | 0.9529 |
| 0.1725 | 10.94 | 1750 | 0.2079 | 0.9401 | 0.9513 | 0.9457 | 0.9605 |
| 0.1116 | 12.5 | 2000 | 0.1992 | 0.9554 | 0.9618 | 0.9586 | 0.9656 |
| 0.1116 | 14.06 | 2250 | 0.1941 | 0.9517 | 0.9588 | 0.9553 | 0.9631 |
| 0.0762 | 15.62 | 2500 | 0.1966 | 0.9503 | 0.9588 | 0.9545 | 0.9639 |
| 0.0762 | 17.19 | 2750 | 0.1951 | 0.9510 | 0.9588 | 0.9549 | 0.9626 |
| 0.0636 | 18.75 | 3000 | 0.1948 | 0.9525 | 0.9603 | 0.9564 | 0.9648 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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