metadata

license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - my_csv_dataset3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: pharma_label_v3.1
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: my_csv_dataset3
          type: my_csv_dataset3
          config: discharge
          split: test
          args: discharge
        metrics:
          - name: Precision
            type: precision
            value: 0.9623287671232876
          - name: Recall
            type: recall
            value: 0.9740034662045061
          - name: F1
            type: f1
            value: 0.9681309216192937
          - name: Accuracy
            type: accuracy
            value: 0.9890616004605642

pharma_label_v3.1

This model is a fine-tuned version of microsoft/layoutlmv3-base on the my_csv_dataset3 dataset. It achieves the following results on the evaluation set:

Loss: 0.0671
Precision: 0.9623
Recall: 0.9740
F1: 0.9681
Accuracy: 0.9891

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: 1500

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.2987	100	0.5492	0.7759	0.7140	0.7437	0.9102
No log	2.5974	200	0.1522	0.9281	0.9393	0.9337	0.9747
No log	3.8961	300	0.1063	0.9332	0.9445	0.9388	0.9793
No log	5.1948	400	0.0891	0.9448	0.9497	0.9473	0.9810
0.375	6.4935	500	0.0879	0.9435	0.9549	0.9492	0.9839
0.375	7.7922	600	0.0908	0.9485	0.9584	0.9534	0.9822
0.375	9.0909	700	0.0764	0.9636	0.9636	0.9636	0.9862
0.375	10.3896	800	0.0819	0.9671	0.9671	0.9671	0.9873
0.375	11.6883	900	0.0802	0.9686	0.9636	0.9661	0.9873
0.0225	12.9870	1000	0.0602	0.9722	0.9705	0.9714	0.9902
0.0225	14.2857	1100	0.0989	0.9438	0.9601	0.9519	0.9816
0.0225	15.5844	1200	0.0859	0.9538	0.9671	0.9604	0.9839
0.0225	16.8831	1300	0.0781	0.9554	0.9653	0.9603	0.9856
0.0225	18.1818	1400	0.0653	0.9605	0.9705	0.9655	0.9891
0.0105	19.4805	1500	0.0671	0.9623	0.9740	0.9681	0.9891

Framework versions

Transformers 4.40.1
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.19.1