---
library_name: transformers
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: 0.9501488095238095
    - name: Recall
      type: recall
      value: 0.9558383233532934
    - name: F1
      type: f1
      value: 0.9529850746268657
    - name: Accuracy
      type: accuracy
      value: 0.9639219015280136
---

<!-- 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: 0.2004
- Precision: 0.9501
- Recall: 0.9558
- F1: 0.9530
- Accuracy: 0.9639

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 2500

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.5625  | 250  | 1.1111          | 0.6740    | 0.7552 | 0.7123 | 0.7695   |
| 1.4577        | 3.125   | 500  | 0.5810          | 0.8362    | 0.8675 | 0.8516 | 0.8697   |
| 1.4577        | 4.6875  | 750  | 0.3787          | 0.8903    | 0.9109 | 0.9005 | 0.9219   |
| 0.4114        | 6.25    | 1000 | 0.2920          | 0.9167    | 0.9311 | 0.9239 | 0.9406   |
| 0.4114        | 7.8125  | 1250 | 0.2640          | 0.9161    | 0.9311 | 0.9235 | 0.9380   |
| 0.2215        | 9.375   | 1500 | 0.2366          | 0.9297    | 0.9409 | 0.9353 | 0.9474   |
| 0.2215        | 10.9375 | 1750 | 0.2232          | 0.9407    | 0.9491 | 0.9449 | 0.9571   |
| 0.1486        | 12.5    | 2000 | 0.2083          | 0.9450    | 0.9513 | 0.9482 | 0.9601   |
| 0.1486        | 14.0625 | 2250 | 0.1981          | 0.9480    | 0.9551 | 0.9515 | 0.9639   |
| 0.1129        | 15.625  | 2500 | 0.2004          | 0.9501    | 0.9558 | 0.9530 | 0.9639   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0