---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-funsd
  results: []
---

<!-- 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 [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1951
- Precision: 0.9104
- Recall: 0.9086
- F1: 0.9095
- Accuracy: 0.8530

## 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: 2000

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 3.3333  | 100  | 0.8172          | 0.8957    | 0.9046 | 0.9001 | 0.8418   |
| No log        | 6.6667  | 200  | 0.8379          | 0.8870    | 0.9160 | 0.9013 | 0.8381   |
| No log        | 10.0    | 300  | 0.9611          | 0.8887    | 0.9041 | 0.8963 | 0.8328   |
| No log        | 13.3333 | 400  | 0.9324          | 0.8888    | 0.9091 | 0.8988 | 0.8438   |
| 0.0651        | 16.6667 | 500  | 0.9475          | 0.8928    | 0.9185 | 0.9055 | 0.8511   |
| 0.0651        | 20.0    | 600  | 1.1234          | 0.8834    | 0.9031 | 0.8931 | 0.8343   |
| 0.0651        | 23.3333 | 700  | 1.1130          | 0.8921    | 0.8957 | 0.8939 | 0.8254   |
| 0.0651        | 26.6667 | 800  | 1.0760          | 0.8931    | 0.9175 | 0.9052 | 0.8416   |
| 0.0651        | 30.0    | 900  | 1.1777          | 0.8894    | 0.9031 | 0.8962 | 0.8336   |
| 0.0115        | 33.3333 | 1000 | 1.2102          | 0.9025    | 0.9101 | 0.9063 | 0.8387   |
| 0.0115        | 36.6667 | 1100 | 1.1602          | 0.9012    | 0.9111 | 0.9061 | 0.8467   |
| 0.0115        | 40.0    | 1200 | 1.1819          | 0.9011    | 0.9101 | 0.9056 | 0.8433   |
| 0.0115        | 43.3333 | 1300 | 1.2095          | 0.9051    | 0.9051 | 0.9051 | 0.8452   |
| 0.0115        | 46.6667 | 1400 | 1.1687          | 0.9064    | 0.9185 | 0.9124 | 0.8570   |
| 0.0031        | 50.0    | 1500 | 1.1951          | 0.9104    | 0.9086 | 0.9095 | 0.8530   |
| 0.0031        | 53.3333 | 1600 | 1.1967          | 0.9041    | 0.9131 | 0.9086 | 0.8530   |
| 0.0031        | 56.6667 | 1700 | 1.1989          | 0.9015    | 0.9091 | 0.9053 | 0.8531   |
| 0.0031        | 60.0    | 1800 | 1.1973          | 0.9000    | 0.9126 | 0.9063 | 0.8549   |
| 0.0031        | 63.3333 | 1900 | 1.2135          | 0.9015    | 0.9096 | 0.9055 | 0.8490   |
| 0.0012        | 66.6667 | 2000 | 1.2210          | 0.9015    | 0.9091 | 0.9053 | 0.8469   |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1