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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: 0.9022777369581191
- name: Recall
type: recall
value: 0.9191616766467066
- name: F1
type: f1
value: 0.9106414534668149
- name: Accuracy
type: accuracy
value: 0.9202037351443124
---
<!-- 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: 0.3848
- Precision: 0.9023
- Recall: 0.9192
- F1: 0.9106
- Accuracy: 0.9202
## 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 | 6.25 | 250 | 0.9576 | 0.7878 | 0.8196 | 0.8034 | 0.8166 |
| 1.3167 | 12.5 | 500 | 0.5210 | 0.8536 | 0.8772 | 0.8653 | 0.8846 |
| 1.3167 | 18.75 | 750 | 0.4077 | 0.8798 | 0.9042 | 0.8918 | 0.9113 |
| 0.2603 | 25.0 | 1000 | 0.3943 | 0.8902 | 0.9102 | 0.9001 | 0.9147 |
| 0.2603 | 31.25 | 1250 | 0.3691 | 0.8980 | 0.9162 | 0.9070 | 0.9194 |
| 0.1009 | 37.5 | 1500 | 0.3496 | 0.9130 | 0.9274 | 0.9202 | 0.9266 |
| 0.1009 | 43.75 | 1750 | 0.3700 | 0.9078 | 0.9214 | 0.9146 | 0.9266 |
| 0.056 | 50.0 | 2000 | 0.3724 | 0.9065 | 0.9214 | 0.9139 | 0.9215 |
| 0.056 | 56.25 | 2250 | 0.3773 | 0.9051 | 0.9207 | 0.9128 | 0.9202 |
| 0.0413 | 62.5 | 2500 | 0.3848 | 0.9023 | 0.9192 | 0.9106 | 0.9202 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
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