metadata

license: mit
base_model: kavg/LiLT-SER-JA
tags:
  - generated_from_trainer
datasets:
  - xfun
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: LiLT-SER-JA-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: 0.7378410438908659
          - name: Recall
            type: recall
            value: 0.7660098522167488
          - name: F1
            type: f1
            value: 0.7516616314199396
          - name: Accuracy
            type: accuracy
            value: 0.8793659699817646

LiLT-SER-JA-SIN

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

Loss: 1.1113
Precision: 0.7378
Recall: 0.7660
F1: 0.7517
Accuracy: 0.8794

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
0.0009	21.74	500	0.8785	0.6584	0.7217	0.6886	0.8505
0.0031	43.48	1000	1.0637	0.7309	0.7291	0.7300	0.8533
0.0046	65.22	1500	0.9166	0.7219	0.7512	0.7363	0.8729
0.0002	86.96	2000	1.0366	0.7212	0.7389	0.7299	0.8721
0.0	108.7	2500	1.0535	0.7191	0.7377	0.7283	0.8662
0.0006	130.43	3000	1.1869	0.7409	0.7291	0.7349	0.8495
0.005	152.17	3500	1.2062	0.7356	0.7401	0.7379	0.8627
0.0002	173.91	4000	1.2067	0.7011	0.7192	0.7100	0.8451
0.0002	195.65	4500	1.1819	0.7290	0.7389	0.7339	0.8578
0.0	217.39	5000	1.1699	0.7463	0.75	0.7482	0.8632
0.0	239.13	5500	1.1548	0.7267	0.7599	0.7429	0.8637
0.0	260.87	6000	1.1867	0.7227	0.7574	0.7396	0.8651
0.0	282.61	6500	1.1614	0.7222	0.7525	0.7370	0.8721
0.0	304.35	7000	1.1884	0.7146	0.7648	0.7388	0.8681
0.0	326.09	7500	1.2186	0.6975	0.7438	0.7199	0.8582
0.0001	347.83	8000	1.0423	0.7313	0.7709	0.7506	0.8754
0.0	369.57	8500	1.1254	0.7278	0.7574	0.7423	0.8705
0.0	391.3	9000	1.1113	0.7378	0.7660	0.7517	0.8794
0.0	413.04	9500	1.1517	0.7424	0.7562	0.7492	0.8732
0.0	434.78	10000	1.1568	0.7413	0.7586	0.7498	0.8726

Framework versions

Transformers 4.39.1
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.1