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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: 0.9457652303120356
- name: Recall
type: recall
value: 0.9528443113772455
- name: F1
type: f1
value: 0.9492915734526474
- name: Accuracy
type: accuracy
value: 0.9490662139219015
---
<!-- 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: 0.2296
- Precision: 0.9458
- Recall: 0.9528
- F1: 0.9493
- Accuracy: 0.9491
## 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 | 1.1659 | 0.6767 | 0.7552 | 0.7138 | 0.7738 |
| 1.4723 | 3.12 | 500 | 0.6092 | 0.8320 | 0.8600 | 0.8458 | 0.8667 |
| 1.4723 | 4.69 | 750 | 0.4107 | 0.8730 | 0.9004 | 0.8865 | 0.9045 |
| 0.4246 | 6.25 | 1000 | 0.3370 | 0.9143 | 0.9259 | 0.9200 | 0.9270 |
| 0.4246 | 7.81 | 1250 | 0.2909 | 0.9267 | 0.9371 | 0.9319 | 0.9372 |
| 0.2225 | 9.38 | 1500 | 0.2571 | 0.9355 | 0.9439 | 0.9396 | 0.9414 |
| 0.2225 | 10.94 | 1750 | 0.2547 | 0.9383 | 0.9454 | 0.9418 | 0.9431 |
| 0.1514 | 12.5 | 2000 | 0.2412 | 0.9306 | 0.9431 | 0.9368 | 0.9435 |
| 0.1514 | 14.06 | 2250 | 0.2329 | 0.9443 | 0.9513 | 0.9478 | 0.9478 |
| 0.1168 | 15.62 | 2500 | 0.2296 | 0.9458 | 0.9528 | 0.9493 | 0.9491 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.10.2+cpu
- Datasets 2.8.0
- Tokenizers 0.13.2
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