metadata

license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
  - generated_from_trainer
datasets:
  - xfun
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: LiLT-SER-JA
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: xfun
          type: xfun
          config: xfun.ja
          split: validation
          args: xfun.ja
        metrics:
          - name: Precision
            type: precision
            value: 0.7244408945686901
          - name: Recall
            type: recall
            value: 0.8754826254826255
          - name: F1
            type: f1
            value: 0.7928321678321678
          - name: Accuracy
            type: accuracy
            value: 0.7835245046923879

LiLT-SER-JA

This model is a fine-tuned version of nielsr/lilt-xlm-roberta-base on the xfun dataset. It achieves the following results on the evaluation set:

Loss: 2.3482
Precision: 0.7244
Recall: 0.8755
F1: 0.7928
Accuracy: 0.7835

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
0.0726	10.2	500	1.0347	0.6824	0.8359	0.7514	0.7829
0.0015	20.41	1000	1.6415	0.6828	0.8808	0.7692	0.7700
0.0062	30.61	1500	1.7000	0.7063	0.8427	0.7685	0.7828
0.0145	40.82	2000	1.9098	0.6979	0.8885	0.7817	0.7729
0.0014	51.02	2500	1.6868	0.7117	0.8509	0.7751	0.7859
0.0009	61.22	3000	1.8930	0.7087	0.8441	0.7705	0.7782
0.0001	71.43	3500	2.0325	0.7217	0.8736	0.7904	0.7845
0.0006	81.63	4000	1.8854	0.7032	0.8769	0.7805	0.7904
0.0001	91.84	4500	2.2205	0.6977	0.8721	0.7752	0.7577
0.0002	102.04	5000	2.1731	0.7090	0.8702	0.7814	0.7786
0.0	112.24	5500	2.3198	0.7150	0.8707	0.7852	0.7681
0.0003	122.45	6000	1.9680	0.7188	0.8649	0.7851	0.7896
0.0	132.65	6500	2.2202	0.7316	0.8523	0.7873	0.7815
0.0	142.86	7000	2.2800	0.7013	0.8818	0.7813	0.7727
0.0	153.06	7500	2.2149	0.7202	0.8784	0.7915	0.7790
0.0	163.27	8000	2.2384	0.7264	0.8663	0.7902	0.7834
0.0001	173.47	8500	2.2177	0.7269	0.8682	0.7913	0.7842
0.0	183.67	9000	2.2768	0.7333	0.8731	0.7971	0.7872
0.0	193.88	9500	2.2996	0.7344	0.8716	0.7972	0.7878
0.0	204.08	10000	2.3482	0.7244	0.8755	0.7928	0.7835

Framework versions

Transformers 4.39.1
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.1