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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: 0.9458054936896808
- name: Recall
type: recall
value: 0.9535928143712575
- name: F1
type: f1
value: 0.9496831904584422
- name: Accuracy
type: accuracy
value: 0.9588285229202037
---
<!-- 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: 0.2033
- Precision: 0.9458
- Recall: 0.9536
- F1: 0.9497
- Accuracy: 0.9588
## 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 | 1.0015 | 0.7227 | 0.7822 | 0.7513 | 0.7963 |
| 1.3862 | 3.12 | 500 | 0.5334 | 0.8591 | 0.8765 | 0.8677 | 0.8837 |
| 1.3862 | 4.69 | 750 | 0.3689 | 0.8925 | 0.9072 | 0.8998 | 0.9164 |
| 0.3835 | 6.25 | 1000 | 0.2877 | 0.9281 | 0.9371 | 0.9326 | 0.9431 |
| 0.3835 | 7.81 | 1250 | 0.2506 | 0.9312 | 0.9424 | 0.9368 | 0.9452 |
| 0.2048 | 9.38 | 1500 | 0.2373 | 0.9480 | 0.9543 | 0.9511 | 0.9554 |
| 0.2048 | 10.94 | 1750 | 0.2184 | 0.9379 | 0.9491 | 0.9435 | 0.9542 |
| 0.1365 | 12.5 | 2000 | 0.2057 | 0.9393 | 0.9506 | 0.9449 | 0.9567 |
| 0.1365 | 14.06 | 2250 | 0.2024 | 0.9487 | 0.9543 | 0.9515 | 0.9576 |
| 0.1067 | 15.62 | 2500 | 0.2033 | 0.9458 | 0.9536 | 0.9497 | 0.9588 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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