metadata

license: mit
base_model: kavg/LiLT-SER-PT
tags:
  - generated_from_trainer
datasets:
  - xfun
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: LiLT-SER-PT-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: 0.7639225181598063
          - name: Recall
            type: recall
            value: 0.7770935960591133
          - name: F1
            type: f1
            value: 0.7704517704517705
          - name: Accuracy
            type: accuracy
            value: 0.8626735867583111

LiLT-SER-PT-SIN

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

Loss: 1.2074
Precision: 0.7639
Recall: 0.7771
F1: 0.7705
Accuracy: 0.8627

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	Accuracy	F1	Validation Loss	Precision	Recall
0.0124	21.74	500	0.8590	0.7403	0.8082	0.7381	0.7426
0.0017	43.48	1000	0.8301	0.7272	1.2608	0.75	0.7057
0.0004	65.22	1500	0.8694	0.7323	0.8843	0.7098	0.7562
0.0	86.96	2000	0.8617	0.7532	1.0638	0.7419	0.7648
0.0001	108.7	2500	0.8580	0.7674	1.1504	0.7689	0.7660
0.0006	130.43	3000	0.8677	0.7479	0.9865	0.7230	0.7746
0.0	152.17	3500	0.8617	0.7558	1.1492	0.7494	0.7623
0.0001	173.91	4000	0.8385	0.7590	1.3124	0.7485	0.7697
0.0055	195.65	4500	1.1331	0.7295	0.7869	0.7571	0.8479
0.0	217.39	5000	1.2061	0.7392	0.7611	0.7500	0.8500
0.0001	239.13	5500	1.2572	0.7253	0.7672	0.7457	0.8482
0.0	260.87	6000	1.3558	0.7494	0.7734	0.7612	0.8569
0.0	282.61	6500	1.4382	0.7598	0.7672	0.7635	0.8589
0.0	304.35	7000	1.4720	0.7537	0.7574	0.7555	0.8533
0.0	326.09	7500	1.3835	0.7524	0.7783	0.7651	0.8579
0.0	347.83	8000	1.2693	0.7534	0.7599	0.7566	0.8599
0.0	369.57	8500	1.2005	0.7417	0.7709	0.7560	0.8600
0.0	391.3	9000	1.2175	0.7560	0.7820	0.7688	0.8601
0.0	413.04	9500	1.2339	0.7556	0.7845	0.7698	0.8601
0.0	434.78	10000	1.2074	0.7639	0.7771	0.7705	0.8627

Framework versions

Transformers 4.39.1
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.1