---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlm-CC-7
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: layoutlmv3
      type: layoutlmv3
      config: FormsDataset
      split: test
      args: FormsDataset
    metrics:
    - name: Precision
      type: precision
      value: 0.12529002320185614
    - name: Recall
      type: recall
      value: 0.20224719101123595
    - name: F1
      type: f1
      value: 0.15472779369627507
    - name: Accuracy
      type: accuracy
      value: 0.19654427645788336
---

<!-- 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. -->

# layoutlm-CC-7

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1612
- Precision: 0.1253
- Recall: 0.2022
- F1: 0.1547
- Accuracy: 0.1965

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 4.8141        | 1.0   | 1    | 4.7205          | 0.0921    | 0.1311 | 0.1082 | 0.0821   |
| 4.7028        | 2.0   | 2    | 4.6365          | 0.1414    | 0.2022 | 0.1664 | 0.1425   |
| 4.6011        | 3.0   | 3    | 4.5617          | 0.1230    | 0.2022 | 0.1530 | 0.1274   |
| 4.5126        | 4.0   | 4    | 4.4931          | 0.1174    | 0.2022 | 0.1486 | 0.1231   |
| 4.4376        | 5.0   | 5    | 4.4390          | 0.1166    | 0.2022 | 0.1479 | 0.1166   |
| 4.3778        | 6.0   | 6    | 4.3926          | 0.1166    | 0.2022 | 0.1479 | 0.1188   |
| 4.3224        | 7.0   | 7    | 4.3454          | 0.1166    | 0.2022 | 0.1479 | 0.1210   |
| 4.2658        | 8.0   | 8    | 4.3058          | 0.1166    | 0.2022 | 0.1479 | 0.1253   |
| 4.2182        | 9.0   | 9    | 4.2708          | 0.1179    | 0.2022 | 0.1490 | 0.1425   |
| 4.1796        | 10.0  | 10   | 4.2415          | 0.1208    | 0.2022 | 0.1513 | 0.1641   |
| 4.1423        | 11.0  | 11   | 4.2165          | 0.1222    | 0.2022 | 0.1523 | 0.1728   |
| 4.1197        | 12.0  | 12   | 4.1951          | 0.1230    | 0.2022 | 0.1530 | 0.1793   |
| 4.0976        | 13.0  | 13   | 4.1782          | 0.1241    | 0.2022 | 0.1538 | 0.1922   |
| 4.0801        | 14.0  | 14   | 4.1669          | 0.1253    | 0.2022 | 0.1547 | 0.1965   |
| 4.0627        | 15.0  | 15   | 4.1612          | 0.1253    | 0.2022 | 0.1547 | 0.1965   |


### Framework versions

- Transformers 4.47.0.dev0
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3