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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: 0.9415680473372781
- name: Recall
type: recall
value: 0.9528443113772455
- name: F1
type: f1
value: 0.947172619047619
- 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: 0.2132
- Precision: 0.9416
- Recall: 0.9528
- F1: 0.9472
- 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 | 1.0604 | 0.7085 | 0.7732 | 0.7394 | 0.7806 |
| 1.4262 | 3.12 | 500 | 0.5754 | 0.8504 | 0.8683 | 0.8593 | 0.8705 |
| 1.4262 | 4.69 | 750 | 0.4026 | 0.8949 | 0.9109 | 0.9028 | 0.9189 |
| 0.4088 | 6.25 | 1000 | 0.3129 | 0.9232 | 0.9356 | 0.9294 | 0.9406 |
| 0.4088 | 7.81 | 1250 | 0.2691 | 0.9290 | 0.9401 | 0.9345 | 0.9452 |
| 0.2193 | 9.38 | 1500 | 0.2260 | 0.9278 | 0.9431 | 0.9354 | 0.9499 |
| 0.2193 | 10.94 | 1750 | 0.2447 | 0.9260 | 0.9371 | 0.9315 | 0.9469 |
| 0.1547 | 12.5 | 2000 | 0.2113 | 0.9394 | 0.9521 | 0.9457 | 0.9601 |
| 0.1547 | 14.06 | 2250 | 0.2138 | 0.9430 | 0.9543 | 0.9487 | 0.9605 |
| 0.1163 | 15.62 | 2500 | 0.2132 | 0.9416 | 0.9528 | 0.9472 | 0.9593 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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