---
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_vimal
  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: 0.717948717948718
    - name: Recall
      type: recall
      value: 0.7368421052631579
    - name: F1
      type: f1
      value: 0.7272727272727273
    - name: Accuracy
      type: accuracy
      value: 0.7333333333333333
---

<!-- 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_vimal

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: 1.8321
- Precision: 0.7179
- Recall: 0.7368
- F1: 0.7273
- Accuracy: 0.7333

## 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        | 125.0  | 250  | 1.2027          | 0.7564    | 0.7763 | 0.7662 | 0.7481   |
| 0.8449        | 250.0  | 500  | 1.3990          | 0.7089    | 0.7368 | 0.7226 | 0.7333   |
| 0.8449        | 375.0  | 750  | 1.5343          | 0.7179    | 0.7368 | 0.7273 | 0.7333   |
| 0.0296        | 500.0  | 1000 | 1.6144          | 0.75      | 0.75   | 0.75   | 0.7407   |
| 0.0296        | 625.0  | 1250 | 1.6898          | 0.7179    | 0.7368 | 0.7273 | 0.7333   |
| 0.0134        | 750.0  | 1500 | 1.7402          | 0.7179    | 0.7368 | 0.7273 | 0.7333   |
| 0.0134        | 875.0  | 1750 | 1.7888          | 0.7179    | 0.7368 | 0.7273 | 0.7333   |
| 0.0089        | 1000.0 | 2000 | 1.8041          | 0.7179    | 0.7368 | 0.7273 | 0.7333   |
| 0.0089        | 1125.0 | 2250 | 1.8209          | 0.7179    | 0.7368 | 0.7273 | 0.7333   |
| 0.0073        | 1250.0 | 2500 | 1.8321          | 0.7179    | 0.7368 | 0.7273 | 0.7333   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2