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layoutlmv3-finetuned-cord_5
4134a42
---
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: 0.596078431372549
- name: Recall
type: recall
value: 0.6826347305389222
- name: F1
type: f1
value: 0.636427076064201
- name: Accuracy
type: accuracy
value: 0.684634974533107
---
<!-- 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: 1.9357
- Precision: 0.5961
- Recall: 0.6826
- F1: 0.6364
- Accuracy: 0.6846
## 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 | 250.0 | 250 | 1.5298 | 0.5778 | 0.6781 | 0.6240 | 0.6825 |
| 0.6654 | 500.0 | 500 | 1.6175 | 0.5942 | 0.6849 | 0.6363 | 0.6880 |
| 0.6654 | 750.0 | 750 | 1.7087 | 0.5947 | 0.6841 | 0.6363 | 0.6876 |
| 0.0208 | 1000.0 | 1000 | 1.7729 | 0.5948 | 0.6834 | 0.6360 | 0.6859 |
| 0.0208 | 1250.0 | 1250 | 1.8273 | 0.5949 | 0.6826 | 0.6358 | 0.6851 |
| 0.0099 | 1500.0 | 1500 | 1.8693 | 0.5957 | 0.6826 | 0.6362 | 0.6846 |
| 0.0099 | 1750.0 | 1750 | 1.8969 | 0.5950 | 0.6819 | 0.6355 | 0.6842 |
| 0.0066 | 2000.0 | 2000 | 1.9196 | 0.5972 | 0.6826 | 0.6371 | 0.6842 |
| 0.0066 | 2250.0 | 2250 | 1.9312 | 0.5946 | 0.6819 | 0.6353 | 0.6838 |
| 0.0054 | 2500.0 | 2500 | 1.9357 | 0.5961 | 0.6826 | 0.6364 | 0.6846 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1