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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: 0.8760330578512396
- name: Recall
type: recall
value: 0.8983050847457628
- name: F1
type: f1
value: 0.8870292887029289
- name: Accuracy
type: accuracy
value: 0.9146919431279621
---
<!-- 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: 0.4548
- Precision: 0.8760
- Recall: 0.8983
- F1: 0.8870
- Accuracy: 0.9147
## 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 | 31.25 | 250 | 0.7175 | 0.7874 | 0.8475 | 0.8163 | 0.8768 |
| 1.0535 | 62.5 | 500 | 0.4279 | 0.8843 | 0.9068 | 0.8954 | 0.9147 |
| 1.0535 | 93.75 | 750 | 0.4042 | 0.8760 | 0.8983 | 0.8870 | 0.9147 |
| 0.0727 | 125.0 | 1000 | 0.4065 | 0.8760 | 0.8983 | 0.8870 | 0.9194 |
| 0.0727 | 156.25 | 1250 | 0.4290 | 0.8843 | 0.9068 | 0.8954 | 0.9147 |
| 0.0245 | 187.5 | 1500 | 0.4511 | 0.8760 | 0.8983 | 0.8870 | 0.9100 |
| 0.0245 | 218.75 | 1750 | 0.4594 | 0.8760 | 0.8983 | 0.8870 | 0.9147 |
| 0.0155 | 250.0 | 2000 | 0.4566 | 0.8760 | 0.8983 | 0.8870 | 0.9147 |
| 0.0155 | 281.25 | 2250 | 0.4489 | 0.8760 | 0.8983 | 0.8870 | 0.9147 |
| 0.0125 | 312.5 | 2500 | 0.4548 | 0.8760 | 0.8983 | 0.8870 | 0.9147 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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