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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: 0.917960088691796
- name: Recall
type: recall
value: 0.9296407185628742
- name: F1
type: f1
value: 0.9237634808478989
- name: Accuracy
type: accuracy
value: 0.9303904923599321
---
<!-- 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: 0.2854
- Precision: 0.9180
- Recall: 0.9296
- F1: 0.9238
- Accuracy: 0.9304
## 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: 2
- eval_batch_size: 2
- 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 | 0.62 | 250 | 1.2967 | 0.6175 | 0.7021 | 0.6571 | 0.7296 |
| 1.6872 | 1.25 | 500 | 0.7576 | 0.8140 | 0.8383 | 0.8260 | 0.8383 |
| 1.6872 | 1.88 | 750 | 0.5695 | 0.8301 | 0.8518 | 0.8408 | 0.8544 |
| 0.6109 | 2.5 | 1000 | 0.4778 | 0.8564 | 0.875 | 0.8656 | 0.8812 |
| 0.6109 | 3.12 | 1250 | 0.3825 | 0.8694 | 0.8922 | 0.8807 | 0.8986 |
| 0.3905 | 3.75 | 1500 | 0.3546 | 0.8831 | 0.9049 | 0.8939 | 0.9143 |
| 0.3905 | 4.38 | 1750 | 0.3153 | 0.8998 | 0.9207 | 0.9101 | 0.9223 |
| 0.275 | 5.0 | 2000 | 0.3065 | 0.8926 | 0.9147 | 0.9035 | 0.9202 |
| 0.275 | 5.62 | 2250 | 0.2872 | 0.9131 | 0.9281 | 0.9206 | 0.9291 |
| 0.2275 | 6.25 | 2500 | 0.2854 | 0.9180 | 0.9296 | 0.9238 | 0.9304 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cpu
- Datasets 2.8.0
- Tokenizers 0.13.2
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