classifier_adapter

karinegabsschon/classifier_adapter

classifier_adapter is a machine learning model from karinegabsschon. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

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

Downloads · 30 days

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Public

Updated Apr 3, 2024

Repo size

98.3 MB

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Public

Hugging Face

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At a glance

Access: Public
Created: Apr 3, 2024
Updated: Apr 3, 2024
SHA: 4209a31b

Base models

Model card

From the Hugging Face model README

classifier_adapter

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

Loss: 0.0386
Accuracy: 0.9875
Precision: 0.8841
Recall: 0.7947
F1: 0.8283
Ap: 0.8850

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: 0.0001
train_batch_size: 16
eval_batch_size: 16
seed: 0
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Ap
No log	0.38	100	0.1590	0.9571	0.0	0.0	0.0	0.1046
No log	0.75	200	0.1578	0.9571	0.0	0.0	0.0	0.1808
No log	1.13	300	0.1185	0.9653	0.0899	0.0599	0.0680	0.4391
No log	1.51	400	0.0898	0.9724	0.2199	0.1409	0.1617	0.6479
0.1405	1.89	500	0.0774	0.9750	0.3319	0.2273	0.2575	0.7417
0.1405	2.26	600	0.0683	0.9771	0.4118	0.3002	0.3294	0.7791
0.1405	2.64	700	0.0616	0.9804	0.6207	0.4336	0.4810	0.8187
0.1405	3.02	800	0.0556	0.9821	0.7210	0.4875	0.5435	0.8380
0.1405	3.4	900	0.0519	0.9830	0.7329	0.5224	0.5839	0.8566
0.0598	3.77	1000	0.0486	0.9846	0.7818	0.6063	0.6615	0.8629
0.0598	4.15	1100	0.0469	0.9853	0.8223	0.6807	0.7248	0.8633
0.0598	4.53	1200	0.0457	0.9856	0.8521	0.7235	0.7663	0.8666
0.0598	4.91	1300	0.0439	0.9859	0.8436	0.6955	0.7435	0.8753
0.0598	5.28	1400	0.0424	0.9862	0.8715	0.6964	0.7496	0.8739
0.0399	5.66	1500	0.0415	0.9869	0.8695	0.7621	0.7994	0.8772
0.0399	6.04	1600	0.0416	0.9865	0.8700	0.7670	0.8039	0.8853
0.0399	6.42	1700	0.0401	0.9871	0.8687	0.7686	0.8047	0.8846
0.0399	6.79	1800	0.0405	0.9867	0.8734	0.7851	0.8167	0.8848
0.0399	7.17	1900	0.0410	0.9865	0.8600	0.7708	0.8057	0.8770
0.0315	7.55	2000	0.0393	0.9873	0.8869	0.7718	0.8158	0.8819
0.0315	7.92	2100	0.0385	0.9871	0.8747	0.7861	0.8196	0.8856
0.0315	8.3	2200	0.0386	0.9877	0.8863	0.7856	0.8227	0.8857
0.0315	8.68	2300	0.0390	0.9869	0.8695	0.7949	0.8221	0.8830
0.0315	9.06	2400	0.0391	0.9872	0.8685	0.8081	0.8311	0.8830
0.026	9.43	2500	0.0386	0.9875	0.8841	0.7947	0.8283	0.8850
0.026	9.81	2600	0.0390	0.9871	0.8615	0.8064	0.8264	0.8840
0.026	10.19	2700	0.0386	0.9873	0.8689	0.8023	0.8264	0.8859
0.026	10.57	2800	0.0386	0.9873	0.8737	0.7986	0.8265	0.8860

Framework versions

Transformers 4.36.2
Pytorch 2.2.1+cu121
Tokenizers 0.15.2

Created: Apr 3, 2024
Updated: Apr 3, 2024