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BACnet-Klassifizierung-Gewerke
cm-mueller/BACnet-Klassifizierung-Gewerke
BACnet-Klassifizierung-Gewerke 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
- a87bc455
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Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
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
| 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
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Languages
- de
- Created
- Aug 12, 2022
- Updated
- Aug 12, 2022