layoutlmv1-er-ner / README.md
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metadata
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv1-er-ner
    results: []

layoutlmv1-er-ner

This model is a fine-tuned version of renjithks/layoutlmv1-cord-ner on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2936
  • Precision: 0.6097
  • Recall: 0.6192
  • F1: 0.6144
  • Accuracy: 0.9479

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: 5e-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
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 22 0.3856 0.3047 0.1453 0.1968 0.8885
No log 2.0 44 0.2637 0.3725 0.3625 0.3674 0.9197
No log 3.0 66 0.2184 0.5117 0.4612 0.4852 0.9361
No log 4.0 88 0.2321 0.4714 0.5585 0.5113 0.9361
No log 5.0 110 0.2183 0.5453 0.5853 0.5646 0.9440
No log 6.0 132 0.2243 0.5977 0.5867 0.5922 0.9459
No log 7.0 154 0.2451 0.5716 0.5910 0.5811 0.9410
No log 8.0 176 0.2387 0.5881 0.5839 0.5860 0.9474
No log 9.0 198 0.2702 0.5794 0.6023 0.5906 0.9430
No log 10.0 220 0.2450 0.5920 0.6079 0.5999 0.9480
No log 11.0 242 0.2697 0.6151 0.5994 0.6071 0.9467
No log 12.0 264 0.2607 0.6022 0.6234 0.6126 0.9497
No log 13.0 286 0.2737 0.6172 0.6276 0.6224 0.9488
No log 14.0 308 0.2840 0.6117 0.6333 0.6223 0.9474
No log 15.0 330 0.2833 0.6030 0.6192 0.6110 0.9476
No log 16.0 352 0.3009 0.6161 0.6135 0.6148 0.9449
No log 17.0 374 0.2920 0.6098 0.6150 0.6124 0.9473
No log 18.0 396 0.2931 0.6017 0.6135 0.6075 0.9471
No log 19.0 418 0.2935 0.6103 0.6206 0.6154 0.9476
No log 20.0 440 0.2936 0.6097 0.6192 0.6144 0.9479

Framework versions

  • Transformers 4.18.0
  • Pytorch 1.11.0
  • Datasets 2.1.0
  • Tokenizers 0.12.1