DistilBERT-TC1000new-10epochs

youlun77/DistilBERT-TC1000new-10epochs

DistilBERT-TC1000new-10epochs is a text classification model from youlun77. 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: Jul 28, 2023
Updated: Jul 28, 2023
SHA: e588064f

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Base models

Model card

From the Hugging Face model README

DistilBERT-TC1000new-10epochs

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

Loss: 0.1041
Recall: {'recall': 0.98}
Precision: {'precision': 0.9803125}

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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
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	Recall	Precision
1.0487	0.35	20	0.9344	{'recall': 0.65}	{'precision': 0.7268554095045501}
0.8188	0.7	40	0.5972	{'recall': 0.94}	{'precision': 0.9445447154471545}
0.4832	1.05	60	0.3348	{'recall': 0.92}	{'precision': 0.9216638655462185}
0.2652	1.4	80	0.1883	{'recall': 0.95}	{'precision': 0.9504561403508772}
0.1615	1.75	100	0.1381	{'recall': 0.94}	{'precision': 0.9407058791178574}
0.128	2.11	120	0.0923	{'recall': 0.98}	{'precision': 0.981025641025641}
0.0625	2.46	140	0.1014	{'recall': 0.97}	{'precision': 0.9700347222222223}
0.0318	2.81	160	0.0715	{'recall': 0.98}	{'precision': 0.9803125}
0.0187	3.16	180	0.0968	{'recall': 0.98}	{'precision': 0.9803125}
0.0106	3.51	200	0.0843	{'recall': 0.98}	{'precision': 0.9803125}
0.0268	3.86	220	0.0860	{'recall': 0.98}	{'precision': 0.9803125}
0.0074	4.21	240	0.1058	{'recall': 0.98}	{'precision': 0.9803125}
0.0194	4.56	260	0.1108	{'recall': 0.98}	{'precision': 0.9803125}
0.0056	4.91	280	0.1048	{'recall': 0.98}	{'precision': 0.9803125}
0.0044	5.26	300	0.1034	{'recall': 0.98}	{'precision': 0.9803125}
0.0063	5.61	320	0.1034	{'recall': 0.98}	{'precision': 0.9803125}
0.0032	5.96	340	0.1033	{'recall': 0.98}	{'precision': 0.9803125}
0.0031	6.32	360	0.1039	{'recall': 0.98}	{'precision': 0.9803125}
0.003	6.67	380	0.1012	{'recall': 0.98}	{'precision': 0.9803125}
0.0027	7.02	400	0.1013	{'recall': 0.98}	{'precision': 0.9803125}
0.0026	7.37	420	0.1019	{'recall': 0.98}	{'precision': 0.9803125}
0.0024	7.72	440	0.1043	{'recall': 0.98}	{'precision': 0.9803125}
0.0023	8.07	460	0.1060	{'recall': 0.98}	{'precision': 0.9803125}
0.0022	8.42	480	0.1052	{'recall': 0.98}	{'precision': 0.9803125}
0.0022	8.77	500	0.1038	{'recall': 0.98}	{'precision': 0.9803125}
0.0021	9.12	520	0.1037	{'recall': 0.98}	{'precision': 0.9803125}
0.0021	9.47	540	0.1038	{'recall': 0.98}	{'precision': 0.9803125}
0.0021	9.82	560	0.1041	{'recall': 0.98}	{'precision': 0.9803125}

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.1
Tokenizers 0.13.3

Task: Text Classification
Library: transformers
Type: distilbert
License: apache-2.0
Created: Jul 28, 2023
Updated: Jul 28, 2023