pytorch tensorboard layoutlmv2 token-classification dataset:sroie transformers generated_from_trainer license:cc-by-nc-sa-4.0 model-index autotrain_compatible
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- sroie
model-index:
- name: layoutlmv2-finetuned-sroie
  results: []
---

<!-- 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. -->

# layoutlmv2-finetuned-sroie

This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0291
- Address Precision: 0.9341
- Address Recall: 0.9395
- Address F1: 0.9368
- Address Number: 347
- Company Precision: 0.9570
- Company Recall: 0.9625
- Company F1: 0.9598
- Company Number: 347
- Date Precision: 0.9885
- Date Recall: 0.9885
- Date F1: 0.9885
- Date Number: 347
- Total Precision: 0.9253
- Total Recall: 0.9280
- Total F1: 0.9266
- Total Number: 347
- Overall Precision: 0.9512
- Overall Recall: 0.9546
- Overall F1: 0.9529
- Overall Accuracy: 0.9961

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Address Precision | Address Recall | Address F1 | Address Number | Company Precision | Company Recall | Company F1 | Company Number | Date Precision | Date Recall | Date F1 | Date Number | Total Precision | Total Recall | Total F1 | Total Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------------:|:--------------:|:----------:|:--------------:|:--------------:|:-----------:|:-------:|:-----------:|:---------------:|:------------:|:--------:|:------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| No log        | 0.05  | 157  | 0.8162          | 0.3670            | 0.7233         | 0.4869     | 347            | 0.0617            | 0.0144         | 0.0234     | 347            | 0.0            | 0.0         | 0.0     | 347         | 0.0             | 0.0          | 0.0      | 347          | 0.3346            | 0.1844         | 0.2378     | 0.9342           |
| No log        | 1.05  | 314  | 0.3490          | 0.8564            | 0.8934         | 0.8745     | 347            | 0.8610            | 0.9280         | 0.8932     | 347            | 0.7297         | 0.8559      | 0.7878  | 347         | 0.0             | 0.0          | 0.0      | 347          | 0.8128            | 0.6693         | 0.7341     | 0.9826           |
| No log        | 2.05  | 471  | 0.1845          | 0.7970            | 0.9049         | 0.8475     | 347            | 0.9211            | 0.9424         | 0.9316     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.0             | 0.0          | 0.0      | 347          | 0.8978            | 0.7089         | 0.7923     | 0.9835           |
| 0.7027        | 3.05  | 628  | 0.1194          | 0.9040            | 0.9222         | 0.9130     | 347            | 0.8880            | 0.9135         | 0.9006     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.0             | 0.0          | 0.0      | 347          | 0.9263            | 0.7061         | 0.8013     | 0.9853           |
| 0.7027        | 4.05  | 785  | 0.0762          | 0.9397            | 0.9424         | 0.9410     | 347            | 0.8889            | 0.9222         | 0.9052     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.7740          | 0.9078       | 0.8355   | 347          | 0.8926            | 0.9402         | 0.9158     | 0.9928           |
| 0.7027        | 5.05  | 942  | 0.0564          | 0.9282            | 0.9308         | 0.9295     | 347            | 0.9296            | 0.9510         | 0.9402     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.7801          | 0.8588       | 0.8176   | 347          | 0.9036            | 0.9323         | 0.9177     | 0.9946           |
| 0.0935        | 6.05  | 1099 | 0.0548          | 0.9222            | 0.9222         | 0.9222     | 347            | 0.6975            | 0.7378         | 0.7171     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.8608          | 0.8732       | 0.8670   | 347          | 0.8648            | 0.8804         | 0.8725     | 0.9921           |
| 0.0935        | 7.05  | 1256 | 0.0410          | 0.92              | 0.9280         | 0.9240     | 347            | 0.9486            | 0.9568         | 0.9527     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.9091          | 0.9222       | 0.9156   | 347          | 0.9414            | 0.9488         | 0.9451     | 0.9961           |
| 0.0935        | 8.05  | 1413 | 0.0369          | 0.9368            | 0.9395         | 0.9381     | 347            | 0.9569            | 0.9597         | 0.9583     | 347            | 0.9772         | 0.9885      | 0.9828  | 347         | 0.9143          | 0.9222       | 0.9182   | 347          | 0.9463            | 0.9524         | 0.9494     | 0.9960           |
| 0.038         | 9.05  | 1570 | 0.0343          | 0.9282            | 0.9308         | 0.9295     | 347            | 0.9624            | 0.9597         | 0.9610     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.9206          | 0.9020       | 0.9112   | 347          | 0.9500            | 0.9452         | 0.9476     | 0.9958           |
| 0.038         | 10.05 | 1727 | 0.0317          | 0.9395            | 0.9395         | 0.9395     | 347            | 0.9598            | 0.9625         | 0.9612     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.9280          | 0.9280       | 0.9280   | 347          | 0.9539            | 0.9546         | 0.9543     | 0.9963           |
| 0.038         | 11.05 | 1884 | 0.0312          | 0.9368            | 0.9395         | 0.9381     | 347            | 0.9514            | 0.9597         | 0.9555     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.9226          | 0.9280       | 0.9253   | 347          | 0.9498            | 0.9539         | 0.9518     | 0.9960           |
| 0.0236        | 12.05 | 2041 | 0.0318          | 0.9368            | 0.9395         | 0.9381     | 347            | 0.9570            | 0.9625         | 0.9598     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.9043          | 0.8991       | 0.9017   | 347          | 0.9467            | 0.9474         | 0.9471     | 0.9956           |
| 0.0236        | 13.05 | 2198 | 0.0291          | 0.9337            | 0.9337         | 0.9337     | 347            | 0.9598            | 0.9625         | 0.9612     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.9164          | 0.9164       | 0.9164   | 347          | 0.9496            | 0.9503         | 0.9499     | 0.9960           |
| 0.0236        | 14.05 | 2355 | 0.0300          | 0.9286            | 0.9366         | 0.9326     | 347            | 0.9459            | 0.9568         | 0.9513     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.9275          | 0.9222       | 0.9249   | 347          | 0.9476            | 0.9510         | 0.9493     | 0.9959           |
| 0.0178        | 15.05 | 2512 | 0.0307          | 0.9366            | 0.9366         | 0.9366     | 347            | 0.9513            | 0.9568         | 0.9540     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.9275          | 0.9222       | 0.9249   | 347          | 0.9510            | 0.9510         | 0.9510     | 0.9959           |
| 0.0178        | 16.05 | 2669 | 0.0300          | 0.9312            | 0.9366         | 0.9339     | 347            | 0.9543            | 0.9625         | 0.9584     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.9171          | 0.9251       | 0.9211   | 347          | 0.9477            | 0.9532         | 0.9504     | 0.9959           |
| 0.0178        | 17.05 | 2826 | 0.0292          | 0.9368            | 0.9395         | 0.9381     | 347            | 0.9570            | 0.9625         | 0.9598     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.9253          | 0.9280       | 0.9266   | 347          | 0.9519            | 0.9546         | 0.9532     | 0.9961           |
| 0.0178        | 18.05 | 2983 | 0.0291          | 0.9341            | 0.9395         | 0.9368     | 347            | 0.9570            | 0.9625         | 0.9598     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.9253          | 0.9280       | 0.9266   | 347          | 0.9512            | 0.9546         | 0.9529     | 0.9961           |
| 0.0149        | 19.01 | 3000 | 0.0291          | 0.9341            | 0.9395         | 0.9368     | 347            | 0.9570            | 0.9625         | 0.9598     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.9253          | 0.9280       | 0.9266   | 347          | 0.9512            | 0.9546         | 0.9529     | 0.9961           |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.8.0+cu101
- Datasets 1.18.4.dev0
- Tokenizers 0.11.6
