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-DE
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: xfun
          type: xfun
          config: xfun.de
          split: validation
          args: xfun.de
        metrics:
          - name: Precision
            type: precision
            value: 0.7268232385661311
          - name: Recall
            type: recall
            value: 0.7853962600178095
          - name: F1
            type: f1
            value: 0.7549753905414082
          - name: Accuracy
            type: accuracy
            value: 0.7816669203063968

LiLT-SER-DE

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: 2.1833
Precision: 0.7268
Recall: 0.7854
F1: 0.7550
Accuracy: 0.7817

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
0.2776	10.42	500	0.7098	0.6660	1.4820	0.6266	0.7106
0.0386	20.83	1000	0.7884	0.7195	1.3364	0.6868	0.7556
0.002	31.25	1500	0.8102	0.7350	1.4865	0.7000	0.7738
0.0043	41.67	2000	0.7965	0.7167	1.5473	0.7050	0.7289
0.0009	52.08	2500	0.7797	0.7357	1.8408	0.7371	0.7342
0.0003	62.5	3000	0.7841	0.7279	1.9387	0.7021	0.7556
0.0044	72.92	3500	0.7900	0.7402	1.7595	0.7292	0.7516
0.0005	83.33	4000	0.7677	0.7370	2.0830	0.7084	0.7680
0.0001	93.75	4500	0.7746	0.7555	2.0764	0.7301	0.7827
0.0001	104.17	5000	0.7716	0.7441	2.0912	0.7158	0.7747
0.0	114.58	5500	0.7764	0.7572	2.1803	0.7275	0.7894
0.0	125.0	6000	0.7809	0.7576	2.1028	0.7384	0.7778
0.0001	135.42	6500	0.7812	0.7422	2.0825	0.7240	0.7614
0.0001	145.83	7000	0.7882	0.7481	2.0649	0.7244	0.7734
0.0001	156.25	7500	0.7789	0.7536	2.1535	0.7324	0.7760
0.0	166.67	8000	0.7760	0.7491	2.2120	0.7307	0.7685
0.0	177.08	8500	0.7941	0.7615	1.9997	0.75	0.7734
0.0	187.5	9000	0.7854	0.7588	2.0939	0.7355	0.7836
0.0	197.92	9500	2.1707	0.7262	0.7805	0.7524	0.7825
0.0	208.33	10000	2.1833	0.7268	0.7854	0.7550	0.7817

Framework versions

Transformers 4.39.1
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.1