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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- layoutlm_v3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: layoutlm_v3
type: layoutlm_v3
config: cord
split: test
args: cord
metrics:
- name: Precision
type: precision
value: 0.9297856614929786
- name: Recall
type: recall
value: 0.9416167664670658
- name: F1
type: f1
value: 0.9356638155448121
- name: Accuracy
type: accuracy
value: 0.9393039049235993
---
<!-- 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 layoutlm_v3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2976
- Precision: 0.9298
- Recall: 0.9416
- F1: 0.9357
- Accuracy: 0.9393
## 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 | 4.17 | 250 | 1.0222 | 0.7468 | 0.7949 | 0.7701 | 0.8014 |
| 1.3962 | 8.33 | 500 | 0.5292 | 0.8414 | 0.8735 | 0.8571 | 0.8778 |
| 1.3962 | 12.5 | 750 | 0.3844 | 0.9049 | 0.9192 | 0.9120 | 0.9249 |
| 0.335 | 16.67 | 1000 | 0.3302 | 0.9243 | 0.9326 | 0.9285 | 0.9342 |
| 0.335 | 20.83 | 1250 | 0.3062 | 0.9204 | 0.9349 | 0.9276 | 0.9406 |
| 0.1419 | 25.0 | 1500 | 0.2931 | 0.9268 | 0.9386 | 0.9327 | 0.9414 |
| 0.1419 | 29.17 | 1750 | 0.2925 | 0.9248 | 0.9386 | 0.9316 | 0.9359 |
| 0.0801 | 33.33 | 2000 | 0.2963 | 0.9276 | 0.9394 | 0.9334 | 0.9359 |
| 0.0801 | 37.5 | 2250 | 0.2916 | 0.9283 | 0.9401 | 0.9342 | 0.9363 |
| 0.0584 | 41.67 | 2500 | 0.2976 | 0.9298 | 0.9416 | 0.9357 | 0.9393 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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