---
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: 0.8836524300441826
    - name: Recall
      type: recall
      value: 0.8982035928143712
    - name: F1
      type: f1
      value: 0.8908685968819599
    - name: Accuracy
      type: accuracy
      value: 0.9057724957555179
---

<!-- 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: 0.3809
- Precision: 0.8837
- Recall: 0.8982
- F1: 0.8909
- Accuracy: 0.9058

## 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: 1
- eval_batch_size: 1
- 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        | 0.3125 | 250  | 1.4179          | 0.5786    | 0.6751 | 0.6231 | 0.7037   |
| 1.8601        | 0.625  | 500  | 0.9021          | 0.7458    | 0.8016 | 0.7727 | 0.7988   |
| 1.8601        | 0.9375 | 750  | 0.6900          | 0.8096    | 0.8338 | 0.8215 | 0.8294   |
| 0.7675        | 1.25   | 1000 | 0.5915          | 0.8128    | 0.8481 | 0.8300 | 0.8544   |
| 0.7675        | 1.5625 | 1250 | 0.5041          | 0.8381    | 0.8638 | 0.8507 | 0.8722   |
| 0.4979        | 1.875  | 1500 | 0.4669          | 0.8413    | 0.8728 | 0.8567 | 0.8850   |
| 0.4979        | 2.1875 | 1750 | 0.4080          | 0.8628    | 0.8847 | 0.8736 | 0.8990   |
| 0.384         | 2.5    | 2000 | 0.3878          | 0.8731    | 0.8907 | 0.8818 | 0.9003   |
| 0.384         | 2.8125 | 2250 | 0.3880          | 0.8794    | 0.8952 | 0.8872 | 0.9032   |
| 0.3439        | 3.125  | 2500 | 0.3809          | 0.8837    | 0.8982 | 0.8909 | 0.9058   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0