README.md

metadata

license: mit
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: lilt-xlm-roberta-base-finetuned-funsd-iob-original
    results: []

lilt-xlm-roberta-base-finetuned-funsd-iob-original

This model is a fine-tuned version of nielsr/lilt-xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.1573
• Precision: 0.7252
• Recall: 0.7718
• F1: 0.7478
• Accuracy: 0.7676

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: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.33	100	0.8309	0.5157	0.6673	0.5818	0.6594
No log	2.67	200	1.0045	0.6080	0.6699	0.6374	0.7387
No log	4.0	300	0.9127	0.6177	0.7310	0.6696	0.7427
No log	5.33	400	0.9808	0.6478	0.7300	0.6865	0.7521
0.6318	6.67	500	1.2169	0.6863	0.7376	0.7110	0.7547
0.6318	8.0	600	1.1830	0.6918	0.7580	0.7234	0.7326
0.6318	9.33	700	1.3537	0.6955	0.7504	0.7219	0.7426
0.6318	10.67	800	1.3888	0.6994	0.7611	0.7290	0.7507
0.6318	12.0	900	1.5929	0.7204	0.7560	0.7378	0.7553
0.1082	13.33	1000	1.7679	0.6891	0.7397	0.7135	0.7452
0.1082	14.67	1100	1.7197	0.7003	0.7570	0.7275	0.7530
0.1082	16.0	1200	1.8053	0.7188	0.7448	0.7315	0.7616
0.1082	17.33	1300	1.9315	0.7109	0.7728	0.7405	0.7643
0.1082	18.67	1400	2.0142	0.7240	0.7789	0.7504	0.7676
0.0312	20.0	1500	2.0475	0.7264	0.7478	0.7369	0.7654
0.0312	21.33	1600	2.0463	0.7251	0.7539	0.7393	0.7599
0.0312	22.67	1700	2.0648	0.7289	0.7753	0.7514	0.7623
0.0312	24.0	1800	2.1301	0.7272	0.7606	0.7435	0.7667
0.0312	25.33	1900	2.1319	0.7274	0.7585	0.7426	0.7694
0.0064	26.67	2000	2.1499	0.7247	0.7723	0.7477	0.7673
0.0064	28.0	2100	2.1627	0.7235	0.7733	0.7476	0.7670
0.0064	29.33	2200	2.1573	0.7252	0.7718	0.7478	0.7676

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Datasets 2.8.0
• Tokenizers 0.13.2