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-FR
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: xfun
          type: xfun
          config: xfun.fr
          split: validation
          args: xfun.fr
        metrics:
          - name: Precision
            type: precision
            value: 0.6809792843691149
          - name: Recall
            type: recall
            value: 0.7606226335717291
          - name: F1
            type: f1
            value: 0.7186009538950716
          - name: Accuracy
            type: accuracy
            value: 0.7894168898125574

LiLT-SER-FR

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: 2.1399
Precision: 0.6810
Recall: 0.7606
F1: 0.7186
Accuracy: 0.7894

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
0.084	9.8	500	1.0721	0.6674	0.6029	0.6335	0.7616
0.0351	19.61	1000	1.5259	0.6832	0.7148	0.6986	0.7849
0.0017	29.41	1500	1.5810	0.6707	0.7207	0.6948	0.7753
0.0126	39.22	2000	1.8551	0.7075	0.7021	0.7048	0.7817
0.0006	49.02	2500	1.8431	0.6288	0.7539	0.6857	0.7837
0.0284	58.82	3000	1.8819	0.6434	0.7362	0.6867	0.7759
0.0001	68.63	3500	1.7968	0.7088	0.7005	0.7046	0.7824
0.0001	78.43	4000	1.8480	0.6860	0.7324	0.7084	0.7875
0.0	88.24	4500	1.9706	0.6781	0.7257	0.7011	0.7820
0.0004	98.04	5000	2.0033	0.6974	0.7202	0.7086	0.7841
0.0001	107.84	5500	2.0152	0.6790	0.7333	0.7051	0.7831
0.0	117.65	6000	1.9981	0.6968	0.7350	0.7154	0.7763
0.0	127.45	6500	2.0012	0.6844	0.7425	0.7123	0.7799
0.0001	137.25	7000	2.0329	0.6578	0.7383	0.6957	0.7808
0.0	147.06	7500	2.1239	0.6882	0.7362	0.7114	0.7846
0.0	156.86	8000	2.0940	0.6886	0.7387	0.7128	0.7878
0.0	166.67	8500	2.1262	0.6954	0.7463	0.7200	0.7863
0.0	176.47	9000	2.1399	0.6810	0.7606	0.7186	0.7894
0.0	186.27	9500	2.1534	0.6844	0.7535	0.7173	0.7887
0.0	196.08	10000	2.1526	0.6848	0.7514	0.7165	0.7892

Framework versions

Transformers 4.38.2
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.1