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-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: 0.7058139534883721
          - name: Recall
            type: recall
            value: 0.7475369458128078
          - name: F1
            type: f1
            value: 0.7260765550239234
          - name: Accuracy
            type: accuracy
            value: 0.8621124982465984

LiLT-SER-SIN

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: 1.1967
Precision: 0.7058
Recall: 0.7475
F1: 0.7261
Accuracy: 0.8621

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
0.0739	21.74	500	0.8268	0.5620	0.7143	0.5	0.6416
0.0509	43.48	1000	0.8324	0.5839	0.8499	0.5348	0.6429
0.0004	65.22	1500	0.8398	0.6521	0.9889	0.6256	0.6810
0.0004	86.96	2000	0.8461	0.6678	1.0577	0.6251	0.7167
0.003	108.7	2500	0.8561	0.6929	1.0734	0.6532	0.7377
0.0006	130.43	3000	0.8569	0.6924	1.1114	0.6686	0.7180
0.0022	152.17	3500	0.8245	0.6749	1.4184	0.6774	0.6724
0.0001	173.91	4000	0.8502	0.6937	1.0524	0.6546	0.7377
0.001	195.65	4500	0.8493	0.6900	1.1949	0.6663	0.7155
0.0001	217.39	5000	0.8460	0.6885	1.1462	0.6790	0.6983
0.0001	239.13	5500	0.8641	0.6970	1.1296	0.6697	0.7266
0.0	260.87	6000	0.8529	0.7046	1.2585	0.6929	0.7167
0.0037	282.61	6500	0.8634	0.7139	1.2292	0.6917	0.7377
0.0	304.35	7000	0.8621	0.7261	1.1967	0.7058	0.7475
0.0	326.09	7500	0.8585	0.7230	1.2144	0.7089	0.7377
0.0	347.83	8000	0.8609	0.7180	1.2117	0.6918	0.7463
0.0	369.57	8500	0.8628	0.7135	1.1961	0.6755	0.7562
0.0	391.3	9000	0.8624	0.7220	1.2292	0.7059	0.7389
0.0	413.04	9500	0.8611	0.7262	1.2278	0.7071	0.7463
0.0	434.78	10000	0.8609	0.7242	1.2317	0.7056	0.7438

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2