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-ES
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: xfun
          type: xfun
          config: xfun.es
          split: validation
          args: xfun.es
        metrics:
          - name: Precision
            type: precision
            value: 0.6718889883616831
          - name: Recall
            type: recall
            value: 0.6733961417676088
          - name: F1
            type: f1
            value: 0.6726417208155948
          - name: Accuracy
            type: accuracy
            value: 0.7462640815388152

LiLT-SER-ES

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: 2.5588
Precision: 0.6719
Recall: 0.6734
F1: 0.6726
Accuracy: 0.7463

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
0.2279	8.2	500	0.6790	0.5205	1.2508	0.4589	0.6012
0.032	16.39	1000	0.6936	0.5885	1.9637	0.6321	0.5505
0.0073	24.59	1500	0.7351	0.6175	1.6711	0.5795	0.6608
0.0479	32.79	2000	0.7405	0.6422	1.8259	0.6265	0.6586
0.0666	40.98	2500	0.7424	0.6349	1.8343	0.5937	0.6824
0.0006	49.18	3000	0.7475	0.6536	2.0575	0.6512	0.6559
0.0084	57.38	3500	0.7138	0.6415	2.4488	0.6758	0.6106
0.0002	65.57	4000	0.7571	0.6468	1.9641	0.6406	0.6532
0.0005	73.77	4500	2.2976	0.6699	0.6429	0.6561	0.7413
0.0003	81.97	5000	2.1562	0.6287	0.6653	0.6465	0.7468
0.0007	90.16	5500	2.2806	0.6435	0.6689	0.6560	0.7435
0.0002	98.36	6000	2.0508	0.6294	0.6734	0.6506	0.7538
0.0	106.56	6500	2.2626	0.6602	0.6765	0.6683	0.7498
0.0	114.75	7000	2.3467	0.6687	0.6492	0.6588	0.7409
0.0	122.95	7500	2.4430	0.6773	0.6734	0.6754	0.7447
0.0	131.15	8000	2.3653	0.6643	0.6765	0.6704	0.7476
0.0	139.34	8500	2.2903	0.6567	0.6824	0.6693	0.7498
0.0	147.54	9000	2.4458	0.6536	0.6824	0.6677	0.7440
0.0	155.74	9500	2.5953	0.6703	0.6685	0.6694	0.7423
0.0	163.93	10000	2.5588	0.6719	0.6734	0.6726	0.7463

Framework versions

Transformers 4.38.2
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.1