BACnet-Klassifizierung-Sanitaertechnik

cm-mueller/BACnet-Klassifizierung-Sanitaertechnik

BACnet-Klassifizierung-Sanitaertechnik is a text classification model from cm-mueller. Use it when you need a label for a piece of text. 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: Text Classification
Library: transformers
License: mit
Model type: bert
Access: Public
Created: Aug 12, 2022
Updated: Aug 12, 2022
SHA: 5389886d

Model card

From the Hugging Face model README

BACnet-Klassifizierung-Sanitaertechnik-bert-base-german-cased

This model is a fine-tuned version of bert-base-german-cased on the gart-labor "klassifizierung_sanitaer_v2" dataset. It achieves the following results on the evaluation set:

Loss: 0.0039
F1: [1. 1. 1.]

Model description

This model makes it possible to classify the sanitary technology components described with the BACnet standard into different categories.
The model is based on a German-language data set.

Intended uses & limitations

The model divides descriptive texts into the following sanitary engineering categories: Other, pressure boosting system, softening system, lifting system, sanitary_general, waste water, drinking water heating system and water meter.

Training and evaluation data

The model is based on a German-language data set.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 40.0

Training results

Training Loss	Epoch	Step	Validation Loss	F1
0.0507	1.0	1	0.1080	[1. 1. 1.]
0.0547	2.0	2	0.0589	[1. 1. 1.]
0.0407	3.0	3	0.0427	[1. 1. 1.]
0.0294	4.0	4	0.0465	[1. 1. 1.]
0.0284	5.0	5	0.0291	[1. 1. 1.]
0.0208	6.0	6	0.0232	[1. 1. 1.]
0.0171	7.0	7	0.0198	[1. 1. 1.]
0.0153	8.0	8	0.0170	[1. 1. 1.]
0.0134	9.0	9	0.0144	[1. 1. 1.]
0.0126	10.0	10	0.0124	[1. 1. 1.]
0.0108	11.0	11	0.0109	[1. 1. 1.]
0.0096	12.0	12	0.0098	[1. 1. 1.]
0.0084	13.0	13	0.0089	[1. 1. 1.]
0.0082	14.0	14	0.0083	[1. 1. 1.]
0.0071	15.0	15	0.0077	[1. 1. 1.]
0.0068	16.0	16	0.0073	[1. 1. 1.]
0.0064	17.0	17	0.0069	[1. 1. 1.]
0.0059	18.0	18	0.0065	[1. 1. 1.]
0.0053	19.0	19	0.0061	[1. 1. 1.]
0.0052	20.0	20	0.0058	[1. 1. 1.]
0.005	21.0	21	0.0056	[1. 1. 1.]
0.0047	22.0	22	0.0053	[1. 1. 1.]
0.0044	23.0	23	0.0051	[1. 1. 1.]
0.0042	24.0	24	0.0050	[1. 1. 1.]
0.0043	25.0	25	0.0048	[1. 1. 1.]
0.004	26.0	26	0.0047	[1. 1. 1.]
0.004	27.0	27	0.0045	[1. 1. 1.]
0.004	28.0	28	0.0044	[1. 1. 1.]
0.0037	29.0	29	0.0044	[1. 1. 1.]
0.0037	30.0	30	0.0043	[1. 1. 1.]
0.0037	31.0	31	0.0042	[1. 1. 1.]
0.0035	32.0	32	0.0042	[1. 1. 1.]
0.0036	33.0	33	0.0041	[1. 1. 1.]
0.0035	34.0	34	0.0041	[1. 1. 1.]
0.0037	35.0	35	0.0040	[1. 1. 1.]
0.0034	36.0	36	0.0040	[1. 1. 1.]
0.0033	37.0	37	0.0040	[1. 1. 1.]
0.0034	38.0	38	0.0040	[1. 1. 1.]
0.0034	39.0	39	0.0040	[1. 1. 1.]
0.0034	40.0	40	0.0039	[1. 1. 1.]

Framework versions

Transformers 4.21.1
Pytorch 1.12.0+cu113
Datasets 2.4.0
Tokenizers 0.12.1

Task: Text Classification
Library: transformers
Type: bert
License: mit
Languages: de
Created: Aug 12, 2022
Updated: Aug 12, 2022