# Load model directly
from transformers import AutoProcessor, AutoModelForTokenClassification
processor = AutoProcessor.from_pretrained("Saed2023/layoutlmv3-finetuned-generated")
model = AutoModelForTokenClassification.from_pretrained("Saed2023/layoutlmv3-finetuned-generated", device_map="auto")Quick Links
layoutlmv3-finetuned-generated
This model is a fine-tuned version of microsoft/layoutlmv3-base on the generated dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.0009
- eval_precision: 1.0
- eval_recall: 1.0
- eval_f1: 1.0
- eval_accuracy: 1.0
- eval_runtime: 4.3549
- eval_samples_per_second: 11.481
- eval_steps_per_second: 5.741
- epoch: 30.0
- step: 1500
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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Saed2023/layoutlmv3-finetuned-generated")