---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
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: 0.953125
    - name: Recall
      type: recall
      value: 0.9588323353293413
    - name: F1
      type: f1
      value: 0.9559701492537314
    - name: Accuracy
      type: accuracy
      value: 0.965195246179966
---

<!-- 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: 0.1913
- Precision: 0.9531
- Recall: 0.9588
- F1: 0.9560
- Accuracy: 0.9652

## 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  | 1.0033          | 0.7434    | 0.7957 | 0.7686 | 0.8060   |
| 1.3714        | 3.12  | 500  | 0.5413          | 0.8534    | 0.8757 | 0.8644 | 0.8769   |
| 1.3714        | 4.69  | 750  | 0.3792          | 0.9013    | 0.9162 | 0.9087 | 0.9219   |
| 0.3763        | 6.25  | 1000 | 0.2743          | 0.9333    | 0.9431 | 0.9382 | 0.9457   |
| 0.3763        | 7.81  | 1250 | 0.2404          | 0.9313    | 0.9439 | 0.9375 | 0.9495   |
| 0.2026        | 9.38  | 1500 | 0.2479          | 0.9325    | 0.9409 | 0.9367 | 0.9431   |
| 0.2026        | 10.94 | 1750 | 0.2001          | 0.9338    | 0.9499 | 0.9417 | 0.9559   |
| 0.1349        | 12.5  | 2000 | 0.2102          | 0.9407    | 0.9499 | 0.9453 | 0.9571   |
| 0.1349        | 14.06 | 2250 | 0.1961          | 0.9560    | 0.9603 | 0.9582 | 0.9648   |
| 0.104         | 15.62 | 2500 | 0.1913          | 0.9531    | 0.9588 | 0.9560 | 0.9652   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3