---
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: 0.9387001477104875
    - name: Recall
      type: recall
      value: 0.9513473053892215
    - name: F1
      type: f1
      value: 0.9449814126394053
    - name: Accuracy
      type: accuracy
      value: 0.9567062818336163
---

<!-- 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: 0.2137
- Precision: 0.9387
- Recall: 0.9513
- F1: 0.9450
- Accuracy: 0.9567

## 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  | 1.0609          | 0.6596    | 0.7440 | 0.6993 | 0.7687   |
| 1.4193        | 3.12  | 500  | 0.5989          | 0.8403    | 0.8623 | 0.8511 | 0.8663   |
| 1.4193        | 4.69  | 750  | 0.4037          | 0.8795    | 0.9012 | 0.8902 | 0.9087   |
| 0.4182        | 6.25  | 1000 | 0.3264          | 0.8980    | 0.9162 | 0.9070 | 0.9257   |
| 0.4182        | 7.81  | 1250 | 0.2705          | 0.9190    | 0.9341 | 0.9265 | 0.9410   |
| 0.2258        | 9.38  | 1500 | 0.2450          | 0.9311    | 0.9401 | 0.9356 | 0.9461   |
| 0.2258        | 10.94 | 1750 | 0.2350          | 0.9341    | 0.9439 | 0.9389 | 0.9491   |
| 0.1576        | 12.5  | 2000 | 0.2219          | 0.9350    | 0.9476 | 0.9413 | 0.9508   |
| 0.1576        | 14.06 | 2250 | 0.2122          | 0.9373    | 0.9506 | 0.9439 | 0.9559   |
| 0.1207        | 15.62 | 2500 | 0.2137          | 0.9387    | 0.9513 | 0.9450 | 0.9567   |


### Framework versions

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