
# KIZervus

This model is a fine-tuned version of [distilbert-base-german-cased](https://huggingface.co/distilbert-base-german-cased).
It is trained to classify german text into the classes "vulgar" speech and "non-vulgar" speech. 
The data set is a collection of other labeled sources in german. For an overview, see the github repository here: https://github.com/NKDataConv/KIZervus
Both data and training procedure are documented in the GitHub repo. Your are welcome to contribute.

It achieves the following results on the evaluation set:
- Train Loss: 0.4640
- Train Accuracy: 0.7744
- Validation Loss: 0.4852
- Validation Accuracy: 0.7937
- Epoch: 1

## Training procedure
For details, see the repo and documentation here: https://github.com/NKDataConv/KIZervus

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 822, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4830     | 0.7617         | 0.5061          | 0.7406              | 0     |
| 0.4640     | 0.7744         | 0.4852          | 0.7937              | 1     |


### Framework versions

- Transformers 4.21.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1

### Supporter

![BMBF Logo](./BMBF_Logo.png)

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "widget": [{"text": "Ich liebe dich!", "example_title": "Non-vulgar"}, {"text": "Leck mich am arsch", "example_title": "Vulgar"}], "model-index": [{"name": "tmp3y468_8j", "results": []}]}
transformers tf distilbert text-classification generated_from_keras_callback license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

KIZervus
========


This model is a fine-tuned version of distilbert-base-german-cased.
It is trained to classify german text into the classes "vulgar" speech and "non-vulgar" speech.
The data set is a collection of other labeled sources in german. For an overview, see the github repository here: URL
Both data and training procedure are documented in the GitHub repo. Your are welcome to contribute.


It achieves the following results on the evaluation set:


* Train Loss: 0.4640
* Train Accuracy: 0.7744
* Validation Loss: 0.4852
* Validation Accuracy: 0.7937
* Epoch: 1


Training procedure
------------------


For details, see the repo and documentation here: URL


### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 5e-05, 'decay\_steps': 822, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.21.2
* TensorFlow 2.8.2
* Datasets 2.2.2
* Tokenizers 0.12.1


### Supporter


!BMBF Logo



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 5e-05, 'decay\_steps': 822, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.21.2
* TensorFlow 2.8.2
* Datasets 2.2.2
* Tokenizers 0.12.1 ### Supporter


!BMBF Logo
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 5e-05, 'decay\_steps': 822, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.21.2
* TensorFlow 2.8.2
* Datasets 2.2.2
* Tokenizers 0.12.1 ### Supporter


!BMBF Logo
passage: TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 5e-05, 'decay\_steps': 822, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.21.2
* TensorFlow 2.8.2
* Datasets 2.2.2
* Tokenizers 0.12.1### Supporter


!BMBF Logo