rlt – AI Model by jschmock | AlphaNeural AI | AlphaNeural AI

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jschmock/
rlt

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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rlt

This model is a fine-tuned version of svalabs/gbert-large-zeroshot-nli on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0381
F1: 0.9901

Label-Übersetzungen

0 Abluft
1 Abluftfilter
2 Abluftventilator
3 Andere
4 Aussenluftklappe
5 Befeuchter
6 Brandschutz
7 Brandschutzklappe
8 Erhitzer
9 Kühler
10 Nacherhitzer
11 RLT_Anlage
12 Raum
13 Ventilator
14 Vorerhitzer
15 Wärmerückgewinnung
16 Zuluft
17 Zuluftuftfilter
18 Zuluftventilator

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: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
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	F1
No log	0.99	36	1.7080	0.4733
No log	1.99	72	0.3576	0.9228
No log	2.99	108	0.1345	0.9689
No log	3.99	144	0.0519	0.9883
No log	4.99	180	0.0410	0.9917
No log	5.99	216	0.0633	0.9881
No log	6.99	252	0.0398	0.9932
No log	7.99	288	0.0415	0.9897
No log	8.99	324	0.0424	0.9901
No log	9.99	360	0.0381	0.9901

Framework versions

Transformers 4.22.2
Pytorch 1.12.1+cu113
Datasets 2.5.1
Tokenizers 0.12.1