metadata

license: mit
base_model: kavg/LiLT-SER-EN
tags:
  - generated_from_trainer
datasets:
  - xfun
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: LiLT-SER-EN-SIN
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: xfun
          type: xfun
          config: xfun.sin
          split: validation
          args: xfun.sin
        metrics:
          - name: Precision
            type: precision
            value: 0.7420494699646644
          - name: Recall
            type: recall
            value: 0.7758620689655172
          - name: F1
            type: f1
            value: 0.7585791691751957
          - name: Accuracy
            type: accuracy
            value: 0.8473839248141394

LiLT-SER-EN-SIN

This model is a fine-tuned version of kavg/LiLT-SER-EN on the xfun dataset. It achieves the following results on the evaluation set:

Loss: 1.3790
Precision: 0.7420
Recall: 0.7759
F1: 0.7586
Accuracy: 0.8474

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: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 10000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0089	21.74	500	0.8362	0.6606	0.7118	0.6852	0.8545
0.0013	43.48	1000	1.3605	0.7269	0.7081	0.7174	0.8230
0.0051	65.22	1500	0.9220	0.7113	0.7525	0.7313	0.8725
0.0054	86.96	2000	1.2086	0.6965	0.7291	0.7124	0.8467
0.0001	108.7	2500	1.1308	0.6843	0.7315	0.7071	0.8449
0.0001	130.43	3000	1.0934	0.7362	0.7044	0.7199	0.8606
0.0	152.17	3500	1.0390	0.7297	0.7512	0.7403	0.8590
0.0001	173.91	4000	1.1448	0.7128	0.7672	0.7390	0.8599
0.0	195.65	4500	1.1902	0.7393	0.7229	0.7310	0.8551
0.0001	217.39	5000	1.1164	0.7141	0.7783	0.7448	0.8555
0.0001	239.13	5500	1.4359	0.7197	0.7241	0.7219	0.8313
0.0	260.87	6000	1.4027	0.7256	0.7426	0.7340	0.8376
0.0	282.61	6500	1.4112	0.7085	0.7574	0.7321	0.8524
0.0	304.35	7000	1.5045	0.7627	0.7599	0.7613	0.8432
0.0	326.09	7500	1.4482	0.7390	0.7672	0.7529	0.8398
0.0	347.83	8000	1.5717	0.7155	0.7525	0.7335	0.8360
0.0	369.57	8500	1.3845	0.7348	0.7746	0.7542	0.8422
0.0	391.3	9000	1.3238	0.7283	0.7660	0.7467	0.8499
0.0	413.04	9500	1.3677	0.7321	0.7672	0.7492	0.8492
0.0001	434.78	10000	1.3790	0.7420	0.7759	0.7586	0.8474

Framework versions

Transformers 4.39.1
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.1