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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: test
args: cord
metrics:
- name: Precision
type: precision
value: 0.9465478841870824
- name: Recall
type: recall
value: 0.9543413173652695
- name: F1
type: f1
value: 0.9504286246738725
- name: Accuracy
type: accuracy
value: 0.9584040747028862
---
<!-- 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: 0.2090
- Precision: 0.9465
- Recall: 0.9543
- F1: 0.9504
- Accuracy: 0.9584
## 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 | 1.0347 | 0.6965 | 0.7695 | 0.7312 | 0.7861 |
| 1.4031 | 3.12 | 500 | 0.5641 | 0.8491 | 0.8720 | 0.8604 | 0.8744 |
| 1.4031 | 4.69 | 750 | 0.3899 | 0.8810 | 0.9087 | 0.8946 | 0.9138 |
| 0.4005 | 6.25 | 1000 | 0.3025 | 0.9202 | 0.9319 | 0.9260 | 0.9355 |
| 0.4005 | 7.81 | 1250 | 0.2641 | 0.9211 | 0.9349 | 0.9279 | 0.9402 |
| 0.2161 | 9.38 | 1500 | 0.2406 | 0.9277 | 0.9416 | 0.9346 | 0.9474 |
| 0.2161 | 10.94 | 1750 | 0.2250 | 0.9343 | 0.9469 | 0.9405 | 0.9516 |
| 0.1474 | 12.5 | 2000 | 0.2238 | 0.9415 | 0.9513 | 0.9464 | 0.9542 |
| 0.1474 | 14.06 | 2250 | 0.2128 | 0.9451 | 0.9536 | 0.9493 | 0.9571 |
| 0.1128 | 15.62 | 2500 | 0.2090 | 0.9465 | 0.9543 | 0.9504 | 0.9584 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3
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