metadata
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- doc_lay_net-small
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LayoutLMv3-DocLayNet-small
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: doc_lay_net-small
type: doc_lay_net-small
config: DocLayNet_2022.08_processed_on_2023.01
split: validation
args: DocLayNet_2022.08_processed_on_2023.01
metrics:
- name: Precision
type: precision
value: 0.12834224598930483
- name: Recall
type: recall
value: 0.0759493670886076
- name: F1
type: f1
value: 0.09542743538767395
- name: Accuracy
type: accuracy
value: 0.6476804585348379
LayoutLMv3-DocLayNet-small
This model is a fine-tuned version of microsoft/layoutlmv3-base on the doc_lay_net-small dataset. It achieves the following results on the evaluation set:
- Loss: 1.2781
- Precision: 0.1283
- Recall: 0.0759
- F1: 0.0954
- Accuracy: 0.6477
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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- total_eval_batch_size: 16
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.51.3
- Pytorch 2.4.1
- Datasets 3.5.1
- Tokenizers 0.21.1