---
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: 0.9328908554572272
    - name: Recall
      type: recall
      value: 0.9468562874251497
    - name: F1
      type: f1
      value: 0.9398216939078752
    - name: Accuracy
      type: accuracy
      value: 0.9516129032258065
---

<!-- 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: 0.2213
- Precision: 0.9329
- Recall: 0.9469
- F1: 0.9398
- Accuracy: 0.9516

## 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        | 1.56  | 250  | 1.0664          | 0.6765    | 0.7530 | 0.7127 | 0.7818   |
| 1.4379        | 3.12  | 500  | 0.6115          | 0.8199    | 0.8518 | 0.8355 | 0.8646   |
| 1.4379        | 4.69  | 750  | 0.4192          | 0.8794    | 0.9004 | 0.8898 | 0.9028   |
| 0.4232        | 6.25  | 1000 | 0.3239          | 0.9180    | 0.9296 | 0.9238 | 0.9304   |
| 0.4232        | 7.81  | 1250 | 0.2840          | 0.9197    | 0.9341 | 0.9268 | 0.9389   |
| 0.2273        | 9.38  | 1500 | 0.2562          | 0.9217    | 0.9341 | 0.9279 | 0.9376   |
| 0.2273        | 10.94 | 1750 | 0.2574          | 0.9304    | 0.9401 | 0.9352 | 0.9410   |
| 0.157         | 12.5  | 2000 | 0.2327          | 0.9293    | 0.9439 | 0.9365 | 0.9482   |
| 0.157         | 14.06 | 2250 | 0.2217          | 0.9351    | 0.9491 | 0.9421 | 0.9520   |
| 0.1208        | 15.62 | 2500 | 0.2213          | 0.9329    | 0.9469 | 0.9398 | 0.9516   |


### Framework versions

- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1