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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: 0.9393042190969653
- name: Recall
type: recall
value: 0.9498502994011976
- name: F1
type: f1
value: 0.9445478228507629
- name: Accuracy
type: accuracy
value: 0.9494906621392191
---
<!-- 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: 0.2454
- Precision: 0.9393
- Recall: 0.9499
- F1: 0.9445
- Accuracy: 0.9495
## 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 | 2.5 | 250 | 1.0544 | 0.7297 | 0.7822 | 0.7551 | 0.7852 |
| 1.4348 | 5.0 | 500 | 0.5651 | 0.8477 | 0.8705 | 0.8589 | 0.8693 |
| 1.4348 | 7.5 | 750 | 0.4012 | 0.8833 | 0.9012 | 0.8922 | 0.9083 |
| 0.4052 | 10.0 | 1000 | 0.3168 | 0.9208 | 0.9311 | 0.9259 | 0.9338 |
| 0.4052 | 12.5 | 1250 | 0.2823 | 0.9304 | 0.9401 | 0.9352 | 0.9410 |
| 0.2039 | 15.0 | 1500 | 0.2626 | 0.9242 | 0.9394 | 0.9317 | 0.9397 |
| 0.2039 | 17.5 | 1750 | 0.2504 | 0.9305 | 0.9424 | 0.9364 | 0.9448 |
| 0.1333 | 20.0 | 2000 | 0.2425 | 0.9324 | 0.9491 | 0.9407 | 0.9503 |
| 0.1333 | 22.5 | 2250 | 0.2442 | 0.9371 | 0.9484 | 0.9427 | 0.9486 |
| 0.1042 | 25.0 | 2500 | 0.2454 | 0.9393 | 0.9499 | 0.9445 | 0.9495 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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