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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_300
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: 0.9325426241660489
- name: Recall
type: recall
value: 0.9416167664670658
- name: F1
type: f1
value: 0.9370577281191806
- name: Accuracy
type: accuracy
value: 0.9363327674023769
---
<!-- 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_300
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: 0.3434
- Precision: 0.9325
- Recall: 0.9416
- F1: 0.9371
- Accuracy: 0.9363
## 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: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 4.17 | 250 | 1.0379 | 0.7204 | 0.7829 | 0.7504 | 0.7941 |
| 1.4162 | 8.33 | 500 | 0.5642 | 0.8462 | 0.8772 | 0.8614 | 0.8820 |
| 1.4162 | 12.5 | 750 | 0.3836 | 0.9055 | 0.9184 | 0.9119 | 0.9206 |
| 0.3211 | 16.67 | 1000 | 0.3209 | 0.9139 | 0.9296 | 0.9217 | 0.9334 |
| 0.3211 | 20.83 | 1250 | 0.2962 | 0.9275 | 0.9386 | 0.9330 | 0.9435 |
| 0.1191 | 25.0 | 1500 | 0.2979 | 0.9254 | 0.9379 | 0.9316 | 0.9402 |
| 0.1191 | 29.17 | 1750 | 0.3079 | 0.9282 | 0.9386 | 0.9334 | 0.9355 |
| 0.059 | 33.33 | 2000 | 0.3039 | 0.9232 | 0.9364 | 0.9298 | 0.9325 |
| 0.059 | 37.5 | 2250 | 0.3254 | 0.9248 | 0.9386 | 0.9316 | 0.9355 |
| 0.0342 | 41.67 | 2500 | 0.3404 | 0.9246 | 0.9364 | 0.9305 | 0.9334 |
| 0.0342 | 45.83 | 2750 | 0.3386 | 0.9354 | 0.9431 | 0.9392 | 0.9355 |
| 0.0226 | 50.0 | 3000 | 0.3274 | 0.9354 | 0.9431 | 0.9392 | 0.9359 |
| 0.0226 | 54.17 | 3250 | 0.3282 | 0.9341 | 0.9446 | 0.9393 | 0.9393 |
| 0.017 | 58.33 | 3500 | 0.3475 | 0.9319 | 0.9424 | 0.9371 | 0.9363 |
| 0.017 | 62.5 | 3750 | 0.3367 | 0.9340 | 0.9431 | 0.9385 | 0.9372 |
| 0.0145 | 66.67 | 4000 | 0.3434 | 0.9325 | 0.9416 | 0.9371 | 0.9363 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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