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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_vimal
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: 0.717948717948718
- name: Recall
type: recall
value: 0.7368421052631579
- name: F1
type: f1
value: 0.7272727272727273
- name: Accuracy
type: accuracy
value: 0.7333333333333333
---
<!-- 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_vimal
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: 1.8321
- Precision: 0.7179
- Recall: 0.7368
- F1: 0.7273
- Accuracy: 0.7333
## 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 | 125.0 | 250 | 1.2027 | 0.7564 | 0.7763 | 0.7662 | 0.7481 |
| 0.8449 | 250.0 | 500 | 1.3990 | 0.7089 | 0.7368 | 0.7226 | 0.7333 |
| 0.8449 | 375.0 | 750 | 1.5343 | 0.7179 | 0.7368 | 0.7273 | 0.7333 |
| 0.0296 | 500.0 | 1000 | 1.6144 | 0.75 | 0.75 | 0.75 | 0.7407 |
| 0.0296 | 625.0 | 1250 | 1.6898 | 0.7179 | 0.7368 | 0.7273 | 0.7333 |
| 0.0134 | 750.0 | 1500 | 1.7402 | 0.7179 | 0.7368 | 0.7273 | 0.7333 |
| 0.0134 | 875.0 | 1750 | 1.7888 | 0.7179 | 0.7368 | 0.7273 | 0.7333 |
| 0.0089 | 1000.0 | 2000 | 1.8041 | 0.7179 | 0.7368 | 0.7273 | 0.7333 |
| 0.0089 | 1125.0 | 2250 | 1.8209 | 0.7179 | 0.7368 | 0.7273 | 0.7333 |
| 0.0073 | 1250.0 | 2500 | 1.8321 | 0.7179 | 0.7368 | 0.7273 | 0.7333 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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