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-PT
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: xfun
          type: xfun
          config: xfun.pt
          split: validation
          args: xfun.pt
        metrics:
          - name: Precision
            type: precision
            value: 0.6997755331088664
          - name: Recall
            type: recall
            value: 0.7550711474417197
          - name: F1
            type: f1
            value: 0.72637250618902
          - name: Accuracy
            type: accuracy
            value: 0.7709534665415047

LiLT-SER-PT

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: 2.1403
Precision: 0.6998
Recall: 0.7551
F1: 0.7264
Accuracy: 0.7710

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
0.0838	8.47	500	0.7697	0.6542	1.0006	0.6081	0.7078
0.0366	16.95	1000	0.7606	0.6795	1.4063	0.6533	0.7078
0.0173	25.42	1500	0.7848	0.7047	1.4681	0.6752	0.7369
0.0036	33.9	2000	0.7706	0.7003	1.6267	0.6577	0.7487
0.0023	42.37	2500	1.6728	0.6839	0.7172	0.7002	0.7698
0.0001	50.85	3000	1.6210	0.6742	0.7493	0.7098	0.7941
0.0001	59.32	3500	1.6883	0.6962	0.7505	0.7223	0.7929
0.0007	67.8	4000	1.8709	0.6730	0.7590	0.7134	0.7811
0.0003	76.27	4500	1.9387	0.6884	0.7151	0.7015	0.7690
0.0034	84.75	5000	1.8042	0.6927	0.7554	0.7227	0.7787
0.0	93.22	5500	2.0395	0.6954	0.7596	0.7261	0.7527
0.0003	101.69	6000	1.9295	0.6861	0.7511	0.7172	0.7790
0.0001	110.17	6500	1.9690	0.6813	0.7611	0.7190	0.7694
0.0	118.64	7000	1.9217	0.6974	0.7520	0.7237	0.7754
0.0001	127.12	7500	2.0703	0.6885	0.7536	0.7196	0.7694
0.0002	135.59	8000	2.0438	0.6915	0.7635	0.7258	0.7770
0.0	144.07	8500	2.0429	0.6980	0.7599	0.7276	0.7782
0.0	152.54	9000	2.1403	0.6998	0.7551	0.7264	0.7710
0.0	161.02	9500	2.1786	0.6986	0.7578	0.7270	0.7726
0.0	169.49	10000	2.1782	0.6965	0.7560	0.7250	0.7721

Framework versions

Transformers 4.38.2
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.1