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---
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: 0.9296817172464841
- name: Recall
type: recall
value: 0.9401197604790419
- name: F1
type: f1
value: 0.9348716040193524
- name: Accuracy
type: accuracy
value: 0.9435483870967742
---
<!-- 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: 0.2908
- Precision: 0.9297
- Recall: 0.9401
- F1: 0.9349
- Accuracy: 0.9435
## 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 | 4.17 | 250 | 1.0995 | 0.6869 | 0.7635 | 0.7231 | 0.7789 |
| 1.4568 | 8.33 | 500 | 0.5676 | 0.8382 | 0.8765 | 0.8569 | 0.8773 |
| 1.4568 | 12.5 | 750 | 0.4044 | 0.8920 | 0.9147 | 0.9032 | 0.9202 |
| 0.3562 | 16.67 | 1000 | 0.3518 | 0.9086 | 0.9229 | 0.9157 | 0.9270 |
| 0.3562 | 20.83 | 1250 | 0.3060 | 0.9245 | 0.9349 | 0.9297 | 0.9372 |
| 0.1509 | 25.0 | 1500 | 0.3032 | 0.9261 | 0.9379 | 0.9319 | 0.9419 |
| 0.1509 | 29.17 | 1750 | 0.2980 | 0.9261 | 0.9386 | 0.9323 | 0.9368 |
| 0.0848 | 33.33 | 2000 | 0.2996 | 0.9226 | 0.9371 | 0.9298 | 0.9385 |
| 0.0848 | 37.5 | 2250 | 0.2924 | 0.9276 | 0.9394 | 0.9334 | 0.9440 |
| 0.0619 | 41.67 | 2500 | 0.2908 | 0.9297 | 0.9401 | 0.9349 | 0.9435 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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