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---
tags:
- generated_from_trainer
datasets:
- cord
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cord
type: cord
args: cord
metrics:
- name: Precision
type: precision
value: 0.9190581309786607
- name: Recall
type: recall
value: 0.9348802395209581
- name: F1
type: f1
value: 0.9269016697588126
- name: Accuracy
type: accuracy
value: 0.9384550084889643
---
<!-- 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
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3056
- Precision: 0.9191
- Recall: 0.9349
- F1: 0.9269
- Accuracy: 0.9385
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 2.0 | 100 | 1.6054 | 0.52 | 0.6130 | 0.5627 | 0.6367 |
| No log | 4.0 | 200 | 0.9172 | 0.7923 | 0.8278 | 0.8097 | 0.8315 |
| No log | 6.0 | 300 | 0.6382 | 0.8367 | 0.8630 | 0.8497 | 0.8667 |
| No log | 8.0 | 400 | 0.4974 | 0.8648 | 0.8907 | 0.8776 | 0.8960 |
| 1.1589 | 10.0 | 500 | 0.4124 | 0.8769 | 0.9064 | 0.8914 | 0.9164 |
| 1.1589 | 12.0 | 600 | 0.3767 | 0.8961 | 0.9169 | 0.9064 | 0.9236 |
| 1.1589 | 14.0 | 700 | 0.3388 | 0.9120 | 0.9304 | 0.9211 | 0.9338 |
| 1.1589 | 16.0 | 800 | 0.3138 | 0.9198 | 0.9356 | 0.9276 | 0.9393 |
| 1.1589 | 18.0 | 900 | 0.3073 | 0.9176 | 0.9334 | 0.9254 | 0.9376 |
| 0.2992 | 20.0 | 1000 | 0.3056 | 0.9191 | 0.9349 | 0.9269 | 0.9385 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
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