metadata

license: mit
base_model: xlm-roberta-large
tags:
  - generated_from_trainer
datasets:
  - super_glue
metrics:
  - accuracy
model-index:
  - name: result_xlmr_siqa
    results: []

result_xlmr_siqa

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

Loss: 1.4143
Accuracy: 0.79

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: 8
eval_batch_size: 8
seed: 44
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0152	0.2	10	1.0207	0.77
0.001	0.4	20	0.7651	0.82
0.0013	0.6	30	0.7756	0.79
0.0012	0.8	40	1.2054	0.8
0.0005	1.0	50	1.3034	0.79
0.0008	1.2	60	1.1920	0.76
0.0138	1.4	70	0.9139	0.76
0.0003	1.6	80	0.9160	0.78
0.0001	1.8	90	1.1525	0.8
0.0085	2.0	100	0.8657	0.79
0.0033	2.2	110	0.8925	0.79
0.0055	2.4	120	1.2264	0.78
0.0014	2.6	130	1.4958	0.8
0.0031	2.8	140	1.4250	0.79
0.0138	3.0	150	1.4240	0.81
0.0304	3.2	160	1.4179	0.8
0.0	3.4	170	1.4685	0.8
0.0	3.6	180	1.4897	0.8
0.0015	3.8	190	1.2689	0.8
0.0001	4.0	200	1.0355	0.78
0.0007	4.2	210	1.1339	0.77
0.0002	4.4	220	1.1915	0.79
0.0001	4.6	230	1.1300	0.8
0.001	4.8	240	1.1464	0.79
0.0001	5.0	250	1.2227	0.78
0.0	5.2	260	1.3048	0.81
0.0	5.4	270	1.3418	0.79
0.0093	5.6	280	1.3442	0.78
0.0004	5.8	290	1.2721	0.8
0.0035	6.0	300	1.1852	0.77
0.0016	6.2	310	1.1745	0.77
0.0003	6.4	320	1.1138	0.8
0.0002	6.6	330	1.2342	0.79
0.0055	6.8	340	1.3594	0.79
0.0	7.0	350	1.4109	0.79
0.0	7.2	360	1.4677	0.78
0.0	7.4	370	1.4951	0.77
0.0	7.6	380	1.4987	0.77
0.0004	7.8	390	1.4517	0.77
0.0	8.0	400	1.4632	0.77
0.0	8.2	410	1.4825	0.78
0.0008	8.4	420	1.4486	0.79
0.0	8.6	430	1.4426	0.79
0.0	8.8	440	1.4216	0.79
0.0	9.0	450	1.4166	0.79
0.0	9.2	460	1.4161	0.79
0.0	9.4	470	1.4172	0.79
0.0003	9.6	480	1.4179	0.79
0.0286	9.8	490	1.4155	0.79
0.0	10.0	500	1.4143	0.79

Framework versions

Transformers 4.34.0
Pytorch 2.1.0
Datasets 2.14.5
Tokenizers 0.14.0