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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: 0.9472118959107807
- name: Recall
type: recall
value: 0.9535928143712575
- name: F1
type: f1
value: 0.9503916449086163
- name: Accuracy
type: accuracy
value: 0.9562818336162988
---
<!-- 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: 0.2152
- Precision: 0.9472
- Recall: 0.9536
- F1: 0.9504
- Accuracy: 0.9563
## 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 | 0.9909 | 0.7582 | 0.8099 | 0.7832 | 0.8128 |
| 1.3653 | 3.12 | 500 | 0.5650 | 0.8392 | 0.8675 | 0.8531 | 0.8756 |
| 1.3653 | 4.69 | 750 | 0.3851 | 0.8865 | 0.9177 | 0.9018 | 0.9181 |
| 0.3744 | 6.25 | 1000 | 0.3104 | 0.9280 | 0.9364 | 0.9322 | 0.9380 |
| 0.3744 | 7.81 | 1250 | 0.2778 | 0.9347 | 0.9424 | 0.9385 | 0.9440 |
| 0.1955 | 9.38 | 1500 | 0.2316 | 0.9327 | 0.9446 | 0.9386 | 0.9440 |
| 0.1955 | 10.94 | 1750 | 0.2461 | 0.9414 | 0.9491 | 0.9452 | 0.9533 |
| 0.1349 | 12.5 | 2000 | 0.2316 | 0.9379 | 0.9491 | 0.9435 | 0.9478 |
| 0.1349 | 14.06 | 2250 | 0.2227 | 0.9487 | 0.9551 | 0.9519 | 0.9533 |
| 0.1024 | 15.62 | 2500 | 0.2152 | 0.9472 | 0.9536 | 0.9504 | 0.9563 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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