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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_800
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: 0.9445266272189349
- name: Recall
type: recall
value: 0.9558383233532934
- name: F1
type: f1
value: 0.9501488095238095
- name: Accuracy
type: accuracy
value: 0.9605263157894737
---
<!-- 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_800
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: 0.2042
- Precision: 0.9445
- Recall: 0.9558
- F1: 0.9501
- Accuracy: 0.9605
## 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 | 1.56 | 250 | 0.9737 | 0.7787 | 0.8166 | 0.7972 | 0.8188 |
| 1.3706 | 3.12 | 500 | 0.5489 | 0.8480 | 0.8645 | 0.8562 | 0.8680 |
| 1.3706 | 4.69 | 750 | 0.3857 | 0.8913 | 0.9087 | 0.8999 | 0.9147 |
| 0.3693 | 6.25 | 1000 | 0.3192 | 0.9117 | 0.9274 | 0.9195 | 0.9317 |
| 0.3693 | 7.81 | 1250 | 0.2816 | 0.9189 | 0.9326 | 0.9257 | 0.9355 |
| 0.1903 | 9.38 | 1500 | 0.2521 | 0.9277 | 0.9409 | 0.9342 | 0.9465 |
| 0.1903 | 10.94 | 1750 | 0.2353 | 0.9357 | 0.9476 | 0.9416 | 0.9550 |
| 0.1231 | 12.5 | 2000 | 0.2361 | 0.9293 | 0.9446 | 0.9369 | 0.9516 |
| 0.1231 | 14.06 | 2250 | 0.2194 | 0.9402 | 0.9528 | 0.9465 | 0.9576 |
| 0.0766 | 15.62 | 2500 | 0.2133 | 0.9416 | 0.9528 | 0.9472 | 0.9580 |
| 0.0766 | 17.19 | 2750 | 0.2117 | 0.9438 | 0.9558 | 0.9498 | 0.9597 |
| 0.0585 | 18.75 | 3000 | 0.2152 | 0.9417 | 0.9551 | 0.9483 | 0.9605 |
| 0.0585 | 20.31 | 3250 | 0.2070 | 0.9431 | 0.9551 | 0.9491 | 0.9588 |
| 0.0454 | 21.88 | 3500 | 0.2093 | 0.9489 | 0.9588 | 0.9538 | 0.9622 |
| 0.0454 | 23.44 | 3750 | 0.2034 | 0.9453 | 0.9566 | 0.9509 | 0.9610 |
| 0.0409 | 25.0 | 4000 | 0.2042 | 0.9445 | 0.9558 | 0.9501 | 0.9605 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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