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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- funsd
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-test
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: funsd
type: funsd
metrics:
- name: Precision
type: precision
value: 0.8972868217054264
- name: Recall
type: recall
value: 0.920019870839543
- name: F1
type: f1
value: 0.9085111601667893
- name: Accuracy
type: accuracy
value: 0.8480922382027815
---
<!-- 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-test
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8036
- Precision: 0.8973
- Recall: 0.9200
- F1: 0.9085
- Accuracy: 0.8481
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 5.26 | 100 | 0.5115 | 0.8071 | 0.8624 | 0.8338 | 0.8407 |
| No log | 10.53 | 200 | 0.4661 | 0.8730 | 0.9086 | 0.8905 | 0.8546 |
| No log | 15.79 | 300 | 0.5613 | 0.8914 | 0.9091 | 0.9001 | 0.8552 |
| No log | 21.05 | 400 | 0.6767 | 0.8937 | 0.8982 | 0.8959 | 0.8507 |
| 0.3022 | 26.32 | 500 | 0.7020 | 0.8935 | 0.9165 | 0.9049 | 0.8626 |
| 0.3022 | 31.58 | 600 | 0.7108 | 0.9040 | 0.9220 | 0.9129 | 0.8591 |
| 0.3022 | 36.84 | 700 | 0.7378 | 0.9049 | 0.9175 | 0.9112 | 0.8517 |
| 0.3022 | 42.11 | 800 | 0.7892 | 0.9026 | 0.9210 | 0.9117 | 0.8537 |
| 0.3022 | 47.37 | 900 | 0.8133 | 0.8995 | 0.9205 | 0.9099 | 0.8490 |
| 0.0223 | 52.63 | 1000 | 0.8036 | 0.8973 | 0.9200 | 0.9085 | 0.8481 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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