distilbert_sa_GLUE_Experiment_data_aug_mrpc_256

gokuls/distilbert_sa_GLUE_Experiment_data_aug_mrpc_256

distilbert_sa_GLUE_Experiment_data_aug_mrpc_256 is a text classification model from gokuls. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.

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

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At a glance

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: distilbert
Access: Public
Created: Feb 1, 2023
Updated: Feb 2, 2023
SHA: 72299a8d

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Datasets

glue

Model card

From the Hugging Face model README

distilbert_sa_GLUE_Experiment_data_aug_mrpc_256

This model is a fine-tuned version of distilbert-base-uncased on the GLUE MRPC dataset. It achieves the following results on the evaluation set:

Loss: 0.0
Accuracy: 1.0
F1: 1.0
Combined Score: 1.0

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: 256
eval_batch_size: 256
seed: 10
distributed_type: multi-GPU
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Combined Score
0.2052	1.0	980	0.0476	0.9853	0.9894	0.9873
0.0409	2.0	1960	0.0031	1.0	1.0	1.0
0.0211	3.0	2940	0.0006	1.0	1.0	1.0
0.0131	4.0	3920	0.0005	1.0	1.0	1.0
0.0078	5.0	4900	0.0001	1.0	1.0	1.0
0.0058	6.0	5880	0.0002	1.0	1.0	1.0
0.0041	7.0	6860	0.0000	1.0	1.0	1.0
0.0035	8.0	7840	0.0000	1.0	1.0	1.0
0.0029	9.0	8820	0.0000	1.0	1.0	1.0
0.0022	10.0	9800	0.0000	1.0	1.0	1.0
0.0021	11.0	10780	0.0000	1.0	1.0	1.0
0.0015	12.0	11760	0.0000	1.0	1.0	1.0
0.0017	13.0	12740	0.0000	1.0	1.0	1.0
0.0011	14.0	13720	0.0000	1.0	1.0	1.0
0.0013	15.0	14700	0.0000	1.0	1.0	1.0
0.0011	16.0	15680	0.0000	1.0	1.0	1.0
0.0009	17.0	16660	0.0000	1.0	1.0	1.0
0.0008	18.0	17640	0.0000	1.0	1.0	1.0
0.0008	19.0	18620	0.0000	1.0	1.0	1.0
0.0007	20.0	19600	0.0000	1.0	1.0	1.0
0.0006	21.0	20580	0.0000	1.0	1.0	1.0
0.0007	22.0	21560	0.0000	1.0	1.0	1.0
0.0005	23.0	22540	0.0000	1.0	1.0	1.0
0.0003	24.0	23520	0.0000	1.0	1.0	1.0
0.0003	25.0	24500	0.0000	1.0	1.0	1.0
0.0003	26.0	25480	0.0000	1.0	1.0	1.0
0.0003	27.0	26460	0.0000	1.0	1.0	1.0
0.0003	28.0	27440	0.0000	1.0	1.0	1.0
0.0002	29.0	28420	0.0000	1.0	1.0	1.0
0.0002	30.0	29400	0.0000	1.0	1.0	1.0
0.0002	31.0	30380	0.0000	1.0	1.0	1.0
0.0002	32.0	31360	0.0000	1.0	1.0	1.0
0.0001	33.0	32340	0.0000	1.0	1.0	1.0
0.0001	34.0	33320	0.0000	1.0	1.0	1.0
0.0002	35.0	34300	0.0000	1.0	1.0	1.0
0.0001	36.0	35280	0.0000	1.0	1.0	1.0
0.0	37.0	36260	0.0000	1.0	1.0	1.0
0.0	38.0	37240	0.0000	1.0	1.0	1.0
0.0	39.0	38220	0.0000	1.0	1.0	1.0
0.0001	40.0	39200	0.0000	1.0	1.0	1.0
0.0	41.0	40180	0.0	1.0	1.0	1.0
0.0	42.0	41160	0.0	1.0	1.0	1.0
0.0001	43.0	42140	0.0000	1.0	1.0	1.0
0.0	44.0	43120	0.0	1.0	1.0	1.0
0.0	45.0	44100	0.0	1.0	1.0	1.0
0.0	46.0	45080	0.0	1.0	1.0	1.0

Framework versions

Transformers 4.26.0
Pytorch 1.14.0a0+410ce96
Datasets 2.9.0
Tokenizers 0.13.2

Task: Text Classification
Library: transformers
Type: distilbert
License: apache-2.0
Languages: en
Created: Feb 1, 2023
Updated: Feb 2, 2023