---
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