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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: 0.9256806475349522
- name: Recall
type: recall
value: 0.9416167664670658
- name: F1
type: f1
value: 0.9335807050092764
- name: Accuracy
type: accuracy
value: 0.9460950764006791
---
<!-- 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: 0.2933
- Precision: 0.9257
- Recall: 0.9416
- F1: 0.9336
- Accuracy: 0.9461
## 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 | 4.17 | 250 | 1.0415 | 0.7691 | 0.8129 | 0.7904 | 0.8132 |
| 1.3968 | 8.33 | 500 | 0.5604 | 0.8509 | 0.8757 | 0.8632 | 0.8722 |
| 1.3968 | 12.5 | 750 | 0.4191 | 0.8833 | 0.9064 | 0.8947 | 0.9092 |
| 0.3531 | 16.67 | 1000 | 0.3352 | 0.9139 | 0.9296 | 0.9217 | 0.9308 |
| 0.3531 | 20.83 | 1250 | 0.3185 | 0.9189 | 0.9326 | 0.9257 | 0.9351 |
| 0.161 | 25.0 | 1500 | 0.3069 | 0.9177 | 0.9349 | 0.9262 | 0.9389 |
| 0.161 | 29.17 | 1750 | 0.2989 | 0.9270 | 0.9409 | 0.9339 | 0.9448 |
| 0.0956 | 33.33 | 2000 | 0.2897 | 0.9242 | 0.9394 | 0.9317 | 0.9440 |
| 0.0956 | 37.5 | 2250 | 0.2893 | 0.9242 | 0.9401 | 0.9321 | 0.9452 |
| 0.0704 | 41.67 | 2500 | 0.2933 | 0.9257 | 0.9416 | 0.9336 | 0.9461 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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