metadata

license: mit
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: roberta-large-finetuned-ours-DS
    results: []

roberta-large-finetuned-ours-DS

This model is a fine-tuned version of roberta-large on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.3369
Accuracy: 0.75
Precision: 0.7054
Recall: 0.6949
F1: 0.6974

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: 1e-05
train_batch_size: 16
eval_batch_size: 16
seed: 43
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.0561	0.99	99	0.8773	0.615	0.4054	0.5584	0.4591
0.762	1.98	198	0.6514	0.715	0.6735	0.6672	0.6588
0.5661	2.97	297	0.6806	0.71	0.6764	0.6608	0.6435
0.3699	3.96	396	0.8358	0.71	0.6611	0.6691	0.6570
0.2184	4.95	495	1.1627	0.7	0.6597	0.6337	0.6414
0.1743	5.94	594	1.0544	0.725	0.6831	0.6949	0.6831
0.098	6.93	693	1.4757	0.73	0.6885	0.6902	0.6892
0.0813	7.92	792	1.8146	0.73	0.6840	0.6772	0.6800
0.0435	8.91	891	1.6697	0.755	0.7141	0.7127	0.7132
0.0209	9.9	990	1.8931	0.755	0.7102	0.7070	0.7082
0.0201	10.89	1089	2.1934	0.74	0.6971	0.6866	0.6907
0.0095	11.88	1188	2.1389	0.75	0.7014	0.6915	0.6932
0.0141	12.87	1287	2.1902	0.74	0.6942	0.6943	0.6936
0.0112	13.86	1386	2.5021	0.73	0.6889	0.6669	0.6741
0.0054	14.85	1485	2.3840	0.73	0.6819	0.6715	0.6746
0.0088	15.84	1584	2.3224	0.74	0.6909	0.6825	0.6787
0.003	16.83	1683	2.2641	0.75	0.7054	0.6949	0.6974
0.0017	17.82	1782	2.3361	0.75	0.7077	0.6968	0.7012
0.0014	18.81	1881	2.3041	0.755	0.7131	0.7009	0.7051
0.0083	19.8	1980	2.3369	0.75	0.7054	0.6949	0.6974

Framework versions

Transformers 4.20.1
Pytorch 1.10.1+cu111
Datasets 2.3.2
Tokenizers 0.12.1