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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: music_layoutlmv3_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: sroie
type: sroie
config: discharge
split: test
args: discharge
metrics:
- name: Precision
type: precision
value: 0.9626865671641791
- name: Recall
type: recall
value: 0.9772727272727273
- name: F1
type: f1
value: 0.9699248120300752
- name: Accuracy
type: accuracy
value: 0.9990407673860912
---
<!-- 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. -->
# music_layoutlmv3_model
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0083
- Precision: 0.9627
- Recall: 0.9773
- F1: 0.9699
- Accuracy: 0.9990
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 8.33 | 100 | 0.0191 | 0.9338 | 0.9621 | 0.9478 | 0.9981 |
| No log | 16.67 | 200 | 0.0120 | 0.9412 | 0.9697 | 0.9552 | 0.9981 |
| No log | 25.0 | 300 | 0.0125 | 0.9412 | 0.9697 | 0.9552 | 0.9981 |
| No log | 33.33 | 400 | 0.0101 | 0.9412 | 0.9697 | 0.9552 | 0.9981 |
| 0.0527 | 41.67 | 500 | 0.0121 | 0.9412 | 0.9697 | 0.9552 | 0.9981 |
| 0.0527 | 50.0 | 600 | 0.0083 | 0.9627 | 0.9773 | 0.9699 | 0.9990 |
| 0.0527 | 58.33 | 700 | 0.0082 | 0.9627 | 0.9773 | 0.9699 | 0.9990 |
| 0.0527 | 66.67 | 800 | 0.0082 | 0.9627 | 0.9773 | 0.9699 | 0.9990 |
| 0.0527 | 75.0 | 900 | 0.0083 | 0.9627 | 0.9773 | 0.9699 | 0.9990 |
| 0.0006 | 83.33 | 1000 | 0.0083 | 0.9627 | 0.9773 | 0.9699 | 0.9990 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.2.2
- Tokenizers 0.13.2
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