---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: project-ocr
  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: 0.7515745276417075
    - name: Recall
      type: recall
      value: 0.8038922155688623
    - name: F1
      type: f1
      value: 0.7768535262206148
    - name: Accuracy
      type: accuracy
      value: 0.8102716468590832
---

<!-- 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. -->

# project-ocr

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: 0.9877
- Precision: 0.7516
- Recall: 0.8039
- F1: 0.7769
- Accuracy: 0.8103

## 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: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.83  | 50   | 2.6184          | 0.4355    | 0.5404 | 0.4823 | 0.4338   |
| No log        | 1.67  | 100  | 1.8766          | 0.5912    | 0.6018 | 0.5964 | 0.5620   |
| No log        | 2.5   | 150  | 1.6165          | 0.5737    | 0.6347 | 0.6027 | 0.6150   |
| No log        | 3.33  | 200  | 1.4317          | 0.5732    | 0.6737 | 0.6194 | 0.6944   |
| No log        | 4.17  | 250  | 1.2787          | 0.6190    | 0.7126 | 0.6625 | 0.7347   |
| No log        | 5.0   | 300  | 1.1632          | 0.6729    | 0.7560 | 0.7120 | 0.7759   |
| No log        | 5.83  | 350  | 1.0990          | 0.6980    | 0.7665 | 0.7306 | 0.7857   |
| No log        | 6.67  | 400  | 1.0327          | 0.7125    | 0.7792 | 0.7444 | 0.7946   |
| No log        | 7.5   | 450  | 0.9994          | 0.7526    | 0.8016 | 0.7764 | 0.8065   |
| 1.6589        | 8.33  | 500  | 0.9877          | 0.7516    | 0.8039 | 0.7769 | 0.8103   |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2