test / README.md
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funsd-layout3
195e1db
metadata
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - funsd-layoutlmv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: test
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: funsd-layoutlmv3
          type: funsd-layoutlmv3
          config: funsd
          split: test
          args: funsd
        metrics:
          - name: Precision
            type: precision
            value: 0.8925979680696662
          - name: Recall
            type: recall
            value: 0.9165424739195231
          - name: F1
            type: f1
            value: 0.9044117647058824
          - name: Accuracy
            type: accuracy
            value: 0.86009746820397

test

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

  • Loss: 0.6509
  • Precision: 0.8926
  • Recall: 0.9165
  • F1: 0.9044
  • Accuracy: 0.8601

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: 1000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.33 100 0.7445 0.7475 0.7869 0.7667 0.7630
No log 2.67 200 0.5447 0.8075 0.8793 0.8419 0.8194
No log 4.0 300 0.5183 0.8425 0.8957 0.8683 0.8418
No log 5.33 400 0.5603 0.8281 0.8952 0.8603 0.8307
0.5735 6.67 500 0.5571 0.8535 0.9001 0.8762 0.8376
0.5735 8.0 600 0.5647 0.8824 0.9096 0.8958 0.8536
0.5735 9.33 700 0.5896 0.8802 0.9121 0.8958 0.8547
0.5735 10.67 800 0.6298 0.8935 0.9165 0.9049 0.8587
0.5735 12.0 900 0.6280 0.8965 0.9210 0.9086 0.8615
0.1395 13.33 1000 0.6509 0.8926 0.9165 0.9044 0.8601

Framework versions

  • Transformers 4.34.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3