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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
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: test
args: cord
metrics:
- name: Precision
type: precision
value: 0.9266666666666666
- name: Recall
type: recall
value: 0.936377245508982
- name: F1
type: f1
value: 0.9314966492926285
- name: Accuracy
type: accuracy
value: 0.9354838709677419
---
<!-- 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: 0.3194
- Precision: 0.9267
- Recall: 0.9364
- F1: 0.9315
- Accuracy: 0.9355
## 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 | 1.0054 | 0.7555 | 0.8024 | 0.7782 | 0.8081 |
| 1.4019 | 8.33 | 500 | 0.5287 | 0.8320 | 0.8638 | 0.8476 | 0.8739 |
| 1.4019 | 12.5 | 750 | 0.3790 | 0.9043 | 0.9192 | 0.9117 | 0.9236 |
| 0.3185 | 16.67 | 1000 | 0.3253 | 0.9178 | 0.9281 | 0.9230 | 0.9355 |
| 0.3185 | 20.83 | 1250 | 0.3231 | 0.9223 | 0.9334 | 0.9278 | 0.9304 |
| 0.1319 | 25.0 | 1500 | 0.3039 | 0.9317 | 0.9394 | 0.9355 | 0.9419 |
| 0.1319 | 29.17 | 1750 | 0.3142 | 0.9287 | 0.9364 | 0.9325 | 0.9334 |
| 0.0725 | 33.33 | 2000 | 0.2982 | 0.9296 | 0.9386 | 0.9341 | 0.9419 |
| 0.0725 | 37.5 | 2250 | 0.3189 | 0.9288 | 0.9371 | 0.9329 | 0.9346 |
| 0.0549 | 41.67 | 2500 | 0.3194 | 0.9267 | 0.9364 | 0.9315 | 0.9355 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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