layoutlmv3-finetuned-cne_nvidia_100

This model is a fine-tuned version of microsoft/layoutlmv3-base on the cne-layoutlmv3 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0064
  • Precision: 0.9951
  • Recall: 0.9951
  • F1: 0.9951
  • Accuracy: 0.9993

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: 3
  • eval_batch_size: 3
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 2500

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 7.81 250 0.0143 0.9951 0.9951 0.9951 0.9993
0.1596 15.62 500 0.0085 0.9951 0.9951 0.9951 0.9993
0.1596 23.44 750 0.0074 0.9951 0.9951 0.9951 0.9993
0.0195 31.25 1000 0.0068 0.9951 0.9951 0.9951 0.9993
0.0195 39.06 1250 0.0067 0.9951 0.9951 0.9951 0.9993
0.008 46.88 1500 0.0067 0.9951 0.9951 0.9951 0.9993
0.008 54.69 1750 0.0064 0.9951 0.9951 0.9951 0.9993
0.0034 62.5 2000 0.0063 0.9951 0.9951 0.9951 0.9993
0.0034 70.31 2250 0.0063 0.9951 0.9951 0.9951 0.9993
0.0023 78.12 2500 0.0064 0.9951 0.9951 0.9951 0.9993

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1
  • Datasets 2.14.3
  • Tokenizers 0.13.3
Downloads last month
21
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for lchiang/layoutlmv3-finetuned-cne_nvidia_100

Finetuned
(217)
this model

Evaluation results