---
tags:
- generated_from_trainer
datasets:
- cord
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: cord
      type: cord
      args: cord
    metrics:
    - name: Precision
      type: precision
      value: 0.9190581309786607
    - name: Recall
      type: recall
      value: 0.9348802395209581
    - name: F1
      type: f1
      value: 0.9269016697588126
    - name: Accuracy
      type: accuracy
      value: 0.9384550084889643
---

<!-- 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

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3056
- Precision: 0.9191
- Recall: 0.9349
- F1: 0.9269
- Accuracy: 0.9385

## 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: 16
- eval_batch_size: 16
- 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        | 2.0   | 100  | 1.6054          | 0.52      | 0.6130 | 0.5627 | 0.6367   |
| No log        | 4.0   | 200  | 0.9172          | 0.7923    | 0.8278 | 0.8097 | 0.8315   |
| No log        | 6.0   | 300  | 0.6382          | 0.8367    | 0.8630 | 0.8497 | 0.8667   |
| No log        | 8.0   | 400  | 0.4974          | 0.8648    | 0.8907 | 0.8776 | 0.8960   |
| 1.1589        | 10.0  | 500  | 0.4124          | 0.8769    | 0.9064 | 0.8914 | 0.9164   |
| 1.1589        | 12.0  | 600  | 0.3767          | 0.8961    | 0.9169 | 0.9064 | 0.9236   |
| 1.1589        | 14.0  | 700  | 0.3388          | 0.9120    | 0.9304 | 0.9211 | 0.9338   |
| 1.1589        | 16.0  | 800  | 0.3138          | 0.9198    | 0.9356 | 0.9276 | 0.9393   |
| 1.1589        | 18.0  | 900  | 0.3073          | 0.9176    | 0.9334 | 0.9254 | 0.9376   |
| 0.2992        | 20.0  | 1000 | 0.3056          | 0.9191    | 0.9349 | 0.9269 | 0.9385   |


### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6