result_xlmr_siqa

Erland/result_xlmr_siqa

result_xlmr_siqa is a multiple choice model from Erland. Use it for the multiple choice task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: multiple choice
Library: transformers
License: mit
Model type: xlm-roberta
Access: Public
Created: Oct 5, 2023
Updated: Oct 5, 2023
SHA: 431d9020

Base models

Datasets

super_glue

Model card

From the Hugging Face model README

result_xlmr_siqa

This model is a fine-tuned version of xlm-roberta-large on the super_glue dataset. It trained first on SIQA 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

Task: multiple choice
Library: transformers
Type: xlm-roberta
License: mit
Created: Oct 5, 2023
Updated: Oct 5, 2023