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-IT
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: xfun
          type: xfun
          config: xfun.it
          split: validation
          args: xfun.it
        metrics:
          - name: Precision
            type: precision
            value: 0.726186733731531
          - name: Recall
            type: recall
            value: 0.7927247769389156
          - name: F1
            type: f1
            value: 0.7579983593109106
          - name: Accuracy
            type: accuracy
            value: 0.768676917924818

LiLT-SER-IT

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: 2.5355
Precision: 0.7262
Recall: 0.7927
F1: 0.7580
Accuracy: 0.7687

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
0.0696	7.46	500	1.0876	0.6322	0.6517	0.6418	0.7584
0.0576	14.93	1000	1.3989	0.6712	0.7601	0.7129	0.7601
0.0096	22.39	1500	1.8059	0.6774	0.7639	0.7181	0.7662
0.0092	29.85	2000	2.0416	0.7266	0.7334	0.7300	0.7652
0.0003	37.31	2500	2.0467	0.7166	0.7539	0.7348	0.7628
0.0013	44.78	3000	2.0159	0.7027	0.7821	0.7403	0.7638
0.0013	52.24	3500	2.2751	0.6961	0.7728	0.7325	0.7575
0.0002	59.7	4000	2.2084	0.7236	0.7563	0.7396	0.7723
0.0002	67.16	4500	2.1843	0.7048	0.7701	0.7360	0.7581
0.0001	74.63	5000	2.2483	0.7366	0.7745	0.7551	0.7770
0.0001	82.09	5500	2.2685	0.7171	0.7752	0.7451	0.7677
0.0005	89.55	6000	2.2877	0.7180	0.7821	0.7487	0.7692
0.0001	97.01	6500	2.2574	0.7308	0.7725	0.7511	0.7721
0.0	104.48	7000	2.4696	0.7255	0.7862	0.7546	0.7660
0.0	111.94	7500	2.3996	0.7140	0.7917	0.7509	0.7725
0.0	119.4	8000	2.4592	0.7261	0.7852	0.7545	0.7665
0.0	126.87	8500	2.4129	0.7336	0.7900	0.7607	0.7718
0.0	134.33	9000	2.5367	0.7316	0.7896	0.7595	0.7666
0.0	141.79	9500	2.5327	0.7278	0.7900	0.7576	0.7663
0.0	149.25	10000	2.5355	0.7262	0.7927	0.7580	0.7687

Framework versions

Transformers 4.38.2
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.1