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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: 0.953125
- name: Recall
type: recall
value: 0.9588323353293413
- name: F1
type: f1
value: 0.9559701492537314
- name: Accuracy
type: accuracy
value: 0.965195246179966
---
<!-- 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: 0.1913
- Precision: 0.9531
- Recall: 0.9588
- F1: 0.9560
- Accuracy: 0.9652
## 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 | 1.0033 | 0.7434 | 0.7957 | 0.7686 | 0.8060 |
| 1.3714 | 3.12 | 500 | 0.5413 | 0.8534 | 0.8757 | 0.8644 | 0.8769 |
| 1.3714 | 4.69 | 750 | 0.3792 | 0.9013 | 0.9162 | 0.9087 | 0.9219 |
| 0.3763 | 6.25 | 1000 | 0.2743 | 0.9333 | 0.9431 | 0.9382 | 0.9457 |
| 0.3763 | 7.81 | 1250 | 0.2404 | 0.9313 | 0.9439 | 0.9375 | 0.9495 |
| 0.2026 | 9.38 | 1500 | 0.2479 | 0.9325 | 0.9409 | 0.9367 | 0.9431 |
| 0.2026 | 10.94 | 1750 | 0.2001 | 0.9338 | 0.9499 | 0.9417 | 0.9559 |
| 0.1349 | 12.5 | 2000 | 0.2102 | 0.9407 | 0.9499 | 0.9453 | 0.9571 |
| 0.1349 | 14.06 | 2250 | 0.1961 | 0.9560 | 0.9603 | 0.9582 | 0.9648 |
| 0.104 | 15.62 | 2500 | 0.1913 | 0.9531 | 0.9588 | 0.9560 | 0.9652 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
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