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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: 0.9289415247964471
- name: Recall
type: recall
value: 0.9393712574850299
- name: F1
type: f1
value: 0.9341272794938594
- name: Accuracy
type: accuracy
value: 0.9393039049235993
---
<!-- 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: 0.3066
- Precision: 0.9289
- Recall: 0.9394
- F1: 0.9341
- Accuracy: 0.9393
## 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 | 0.9691 | 0.7365 | 0.7867 | 0.7608 | 0.7992 |
| 1.3706 | 8.33 | 500 | 0.5325 | 0.8645 | 0.8885 | 0.8763 | 0.8858 |
| 1.3706 | 12.5 | 750 | 0.3943 | 0.8939 | 0.9139 | 0.9038 | 0.9151 |
| 0.3211 | 16.67 | 1000 | 0.3364 | 0.9209 | 0.9319 | 0.9263 | 0.9342 |
| 0.3211 | 20.83 | 1250 | 0.3217 | 0.9246 | 0.9364 | 0.9305 | 0.9346 |
| 0.1405 | 25.0 | 1500 | 0.3100 | 0.9296 | 0.9394 | 0.9345 | 0.9355 |
| 0.1405 | 29.17 | 1750 | 0.3113 | 0.9275 | 0.9386 | 0.9330 | 0.9363 |
| 0.076 | 33.33 | 2000 | 0.3183 | 0.9280 | 0.9364 | 0.9322 | 0.9351 |
| 0.076 | 37.5 | 2250 | 0.3125 | 0.9211 | 0.9356 | 0.9283 | 0.9363 |
| 0.0549 | 41.67 | 2500 | 0.3066 | 0.9289 | 0.9394 | 0.9341 | 0.9393 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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