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---
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
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