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# BACnet-Klassifizierung-Heizungstechnik-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_heizung_v2" dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0798
- F1: [1.         1.         0.99       0.94117647 1.         0.90909091  1.         1.         1.         1.        ]

## Model description

This model makes it possible to classify the heating 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 categories of heating technology: 
CHP, District heating, Heating_circuit_consumer, Heating_system_general, Boiler, Circuit_generator(feeder), Buffer storage, Hot water preparation, Heat pump, Heat exchanger, Heat exchanger and Meter.

## 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: 7.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1                                                                                                               |
|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------------:|
| 0.0281        | 0.9   | 8    | 0.0798          | [1.         1.         0.99       0.94117647 1.         0.90909091   1.         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-Heizungstechnik-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-Heizungstechnik-bert-base-german-cased
=============================================================


This model is a fine-tuned version of bert-base-german-cased on the gart-labor "klassifizierung\_heizung\_v2" dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0798
* F1: [1. 1. 0.99 0.94117647 1. 0.90909091 1. 1. 1. 1. ]


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


This model makes it possible to classify the heating 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 categories of heating technology:
CHP, District heating, Heating\_circuit\_consumer, Heating\_system\_general, Boiler, Circuit\_generator(feeder), Buffer storage, Hot water preparation, Heat pump, Heat exchanger, Heat exchanger and Meter.


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: 7.0


### 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: 7.0 ### 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: 7.0 ### 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: 7.0### Training results### Framework versions


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