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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: 0.9485842026825634
- name: Recall
type: recall
value: 0.9528443113772455
- name: F1
type: f1
value: 0.9507094846900671
- name: Accuracy
type: accuracy
value: 0.9592529711375212
---
<!-- 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: 0.1978
- Precision: 0.9486
- Recall: 0.9528
- F1: 0.9507
- Accuracy: 0.9593
## 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 | 1.56 | 250 | 0.9543 | 0.7832 | 0.8166 | 0.7996 | 0.8226 |
| 1.3644 | 3.12 | 500 | 0.5338 | 0.8369 | 0.8683 | 0.8523 | 0.8824 |
| 1.3644 | 4.69 | 750 | 0.3658 | 0.8840 | 0.9072 | 0.8955 | 0.9232 |
| 0.3802 | 6.25 | 1000 | 0.3019 | 0.9156 | 0.9251 | 0.9203 | 0.9334 |
| 0.3802 | 7.81 | 1250 | 0.2833 | 0.9094 | 0.9237 | 0.9165 | 0.9346 |
| 0.2061 | 9.38 | 1500 | 0.2241 | 0.9377 | 0.9469 | 0.9423 | 0.9525 |
| 0.2061 | 10.94 | 1750 | 0.2282 | 0.9304 | 0.9409 | 0.9356 | 0.9474 |
| 0.1416 | 12.5 | 2000 | 0.2017 | 0.9509 | 0.9566 | 0.9537 | 0.9610 |
| 0.1416 | 14.06 | 2250 | 0.2006 | 0.9472 | 0.9536 | 0.9504 | 0.9614 |
| 0.1056 | 15.62 | 2500 | 0.1978 | 0.9486 | 0.9528 | 0.9507 | 0.9593 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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