---
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: test
      args: cord
    metrics:
    - name: Precision
      type: precision
      value: 0.9407407407407408
    - name: Recall
      type: recall
      value: 0.9505988023952096
    - name: F1
      type: f1
      value: 0.9456440804169769
    - name: Accuracy
      type: accuracy
      value: 0.9584040747028862
---

<!-- 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: 0.2012
- Precision: 0.9407
- Recall: 0.9506
- F1: 0.9456
- Accuracy: 0.9584

## 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  | 1.0522          | 0.6964    | 0.7657 | 0.7294 | 0.7831   |
| 1.4089        | 3.12  | 500  | 0.5725          | 0.84      | 0.8645 | 0.8521 | 0.8786   |
| 1.4089        | 4.69  | 750  | 0.3936          | 0.8720    | 0.9027 | 0.8871 | 0.9104   |
| 0.3977        | 6.25  | 1000 | 0.3240          | 0.9204    | 0.9349 | 0.9276 | 0.9397   |
| 0.3977        | 7.81  | 1250 | 0.2827          | 0.9244    | 0.9341 | 0.9293 | 0.9414   |
| 0.2176        | 9.38  | 1500 | 0.2381          | 0.9225    | 0.9349 | 0.9286 | 0.9452   |
| 0.2176        | 10.94 | 1750 | 0.2497          | 0.9161    | 0.9319 | 0.9239 | 0.9419   |
| 0.1565        | 12.5  | 2000 | 0.2149          | 0.9392    | 0.9484 | 0.9438 | 0.9520   |
| 0.1565        | 14.06 | 2250 | 0.2075          | 0.9348    | 0.9446 | 0.9397 | 0.9542   |
| 0.1192        | 15.62 | 2500 | 0.2012          | 0.9407    | 0.9506 | 0.9456 | 0.9584   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3