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

This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the [gart-labor](https://huggingface.co/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

{"language": ["de"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "BACnet-Klassifizierung-Sanitaertechnik-bert-base-german-cased", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer de license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #de #license-mit #autotrain_compatible #endpoints_compatible #region-us 

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



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### 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 ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #de #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #de #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1