metadata

license: mit
base_model: kavg/LiLT-SER-DE
tags:
  - generated_from_trainer
datasets:
  - xfun
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: LiLT-SER-DE-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: 0.7494033412887828
          - name: Recall
            type: recall
            value: 0.7733990147783252
          - name: F1
            type: f1
            value: 0.7612121212121213
          - name: Accuracy
            type: accuracy
            value: 0.8555197082339739

LiLT-SER-DE-SIN

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

Loss: 1.2864
Precision: 0.7494
Recall: 0.7734
F1: 0.7612
Accuracy: 0.8555

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
0.0053	21.74	500	0.9346	0.6644	0.7315	0.6964	0.8395
0.0026	43.48	1000	0.9918	0.7216	0.7278	0.7247	0.8587
0.0016	65.22	1500	0.9133	0.7098	0.7291	0.7193	0.8603
0.0001	86.96	2000	1.0041	0.7580	0.7328	0.7451	0.8536
0.0002	108.7	2500	1.1795	0.7312	0.7438	0.7375	0.8461
0.0001	130.43	3000	1.1417	0.7169	0.7328	0.7247	0.8642
0.0	152.17	3500	1.1740	0.7336	0.7562	0.7447	0.8453
0.0	173.91	4000	1.0535	0.7350	0.7414	0.7382	0.8635
0.0	195.65	4500	1.2769	0.7443	0.7241	0.7341	0.8502
0.0	217.39	5000	1.2235	0.7148	0.7438	0.7290	0.8363
0.0004	239.13	5500	1.2500	0.7376	0.7685	0.7527	0.8569
0.0	260.87	6000	1.2864	0.7494	0.7734	0.7612	0.8555
0.0	282.61	6500	1.1766	0.7649	0.7451	0.7548	0.8589
0.0	304.35	7000	1.3060	0.7231	0.7365	0.7297	0.8479
0.0	326.09	7500	1.1780	0.7093	0.7451	0.7267	0.8534
0.0	347.83	8000	1.2882	0.7337	0.75	0.7418	0.8614
0.0	369.57	8500	1.2833	0.7436	0.75	0.7468	0.8644
0.0	391.3	9000	1.4372	0.7372	0.7463	0.7417	0.8522
0.0	413.04	9500	1.4223	0.7382	0.75	0.7440	0.8513
0.0	434.78	10000	1.3686	0.7454	0.75	0.7477	0.8554

Framework versions

Transformers 4.39.1
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.1