---
tags:
- generated_from_trainer
datasets:
- mp-02/funsd
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-funsd
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: mp-02/funsd
      type: mp-02/funsd
    metrics:
    - name: Precision
      type: precision
      value: 0.8746976294146106
    - name: Recall
      type: recall
      value: 0.904
    - name: F1
      type: f1
      value: 0.8891074502089993
    - name: Accuracy
      type: accuracy
      value: 0.8368167202572347
---

<!-- 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-funsd

This model is a fine-tuned version of [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6541
- Precision: 0.8747
- Recall: 0.904
- F1: 0.8891
- Accuracy: 0.8368

## 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: 6
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 400

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 25   | 1.2831          | 0.4033    | 0.4795 | 0.4381 | 0.6092   |
| No log        | 2.0   | 50   | 0.8178          | 0.7266    | 0.7935 | 0.7586 | 0.7748   |
| No log        | 3.0   | 75   | 0.6843          | 0.7951    | 0.8345 | 0.8143 | 0.7990   |
| No log        | 4.0   | 100  | 0.6317          | 0.8024    | 0.861  | 0.8307 | 0.8161   |
| No log        | 5.0   | 125  | 0.5964          | 0.8260    | 0.897  | 0.8600 | 0.8234   |
| No log        | 6.0   | 150  | 0.6050          | 0.8204    | 0.87   | 0.8445 | 0.8207   |
| No log        | 7.0   | 175  | 0.6281          | 0.8203    | 0.8765 | 0.8475 | 0.8168   |
| No log        | 8.0   | 200  | 0.6228          | 0.8449    | 0.8985 | 0.8709 | 0.8235   |
| No log        | 9.0   | 225  | 0.6213          | 0.8345    | 0.88   | 0.8567 | 0.8266   |
| No log        | 10.0  | 250  | 0.6173          | 0.8450    | 0.897  | 0.8702 | 0.8357   |
| No log        | 11.0  | 275  | 0.6476          | 0.8388    | 0.8895 | 0.8634 | 0.8299   |
| No log        | 12.0  | 300  | 0.6359          | 0.8584    | 0.8945 | 0.8761 | 0.8382   |
| No log        | 13.0  | 325  | 0.6469          | 0.8759    | 0.907  | 0.8912 | 0.8395   |
| No log        | 14.0  | 350  | 0.6510          | 0.8729    | 0.9035 | 0.8880 | 0.8373   |
| No log        | 15.0  | 375  | 0.6555          | 0.8656    | 0.902  | 0.8834 | 0.8354   |
| No log        | 16.0  | 400  | 0.6541          | 0.8747    | 0.904  | 0.8891 | 0.8368   |


### Framework versions

- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 2.13.2
- Tokenizers 0.10.1