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# BACnet-Klassifizierung-Gewerke-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_gewerke" dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0394
- F1: [0.96296296 0.8        0.97297297 1.         0.99469027 0.98979592  0.98969072]

## Model description

This model makes it possible to classify the components of the technical building equipment described with the BACnet standard into different trades. 
The model is based on a German-language data set.

## Intended uses & limitations

The model divides descriptive texts into the following building services trades:
Waste_water_water_gas_systems, Other_systems, Building_automation, Refrigeration_systems, Air_technical_systems, Heavy_current_systems and Heat_supply_systems

## Training and evaluation data

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

### 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: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1                                                                              |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------:|
| 0.4309        | 0.99  | 45   | 0.0736          | [0.89655172 0.84210526 0.97297297 0.98901099 0.9929078  0.99492386 0.98701299] |
| 0.0722        | 1.99  | 90   | 0.0511          | [0.92307692 0.875      0.96       1.         0.99295775 0.98979592 0.98714653] |
| 0.0431        | 2.99  | 135  | 0.0460          | [1.         0.8        0.97297297 1.         0.99469027 0.98979592 0.99224806] |
| 0.0313        | 3.99  | 180  | 0.0365          | [1.         0.84210526 0.97297297 1.         0.99646643 0.98979592 0.99224806] |
| 0.0238        | 4.99  | 225  | 0.0394          | [0.96296296 0.8        0.97297297 1.         0.99469027 0.98979592 0.98969072] |


### 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", "BACnet"], "metrics": ["f1"], "widget": [{"text": "11004KAE901KL1ST15"}, {"text": "Heizkreis Nord Ost"}, {"text": "Raumluftqualitaet RLT Sporthalle"}, {"text": "11004ELT002IS011MW22"}, {"text": "Beschreibung: Abschaltung durch Stoppwert erreicht KM 1; Name: 11004KAE901KL1ST15'"}], "model-index": [{"name": "BACnet-Klassifizierung-Gewerke", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer BACnet de license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #BACnet #de #license-mit #autotrain_compatible #endpoints_compatible #region-us 

BACnet-Klassifizierung-Gewerke-bert-base-german-cased
=====================================================


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


* Loss: 0.0394
* F1: [0.96296296 0.8 0.97297297 1. 0.99469027 0.98979592 0.98969072]


Model description
-----------------


This model makes it possible to classify the components of the technical building equipment described with the BACnet standard into different trades.
The model is based on a German-language data set.


Intended uses & limitations
---------------------------


The model divides descriptive texts into the following building services trades:
Waste\_water\_water\_gas\_systems, Other\_systems, Building\_automation, Refrigeration\_systems, Air\_technical\_systems, Heavy\_current\_systems and Heat\_supply\_systems


Training and evaluation data
----------------------------


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


### 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: 5.0


### 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: 5.0 ### 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 #BACnet #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: 5.0 ### 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 #BACnet #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: 5.0### Training results### Framework versions


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