---
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: 0.9243884358784284
    - name: Recall
      type: recall
      value: 0.9333832335329342
    - name: F1
      type: f1
      value: 0.9288640595903166
    - name: Accuracy
      type: accuracy
      value: 0.9363327674023769
---

<!-- 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: 0.3467
- Precision: 0.9244
- Recall: 0.9334
- F1: 0.9289
- Accuracy: 0.9363

## 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        | 4.17  | 250  | 0.5174          | 0.8469    | 0.8735 | 0.8600 | 0.8790   |
| 0.5511        | 8.33  | 500  | 0.3975          | 0.8999    | 0.9147 | 0.9072 | 0.9194   |
| 0.5511        | 12.5  | 750  | 0.3872          | 0.9015    | 0.9184 | 0.9099 | 0.9189   |
| 0.1802        | 16.67 | 1000 | 0.3416          | 0.9180    | 0.9296 | 0.9238 | 0.9338   |
| 0.1802        | 20.83 | 1250 | 0.3311          | 0.9159    | 0.9289 | 0.9223 | 0.9359   |
| 0.0836        | 25.0  | 1500 | 0.3457          | 0.9192    | 0.9281 | 0.9236 | 0.9334   |
| 0.0836        | 29.17 | 1750 | 0.3347          | 0.9202    | 0.9319 | 0.9260 | 0.9291   |
| 0.0473        | 33.33 | 2000 | 0.3677          | 0.9194    | 0.9304 | 0.9249 | 0.9253   |
| 0.0473        | 37.5  | 2250 | 0.3433          | 0.9279    | 0.9341 | 0.9310 | 0.9376   |
| 0.0342        | 41.67 | 2500 | 0.3467          | 0.9244    | 0.9334 | 0.9289 | 0.9363   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3