# German sentiment BERT finetuned on news data

Sentiment analysis model based on https://huggingface.co/oliverguhr/german-sentiment-bert, with additional training on German news texts about migration.

This model is part of the project https://github.com/text-analytics-20/news-sentiment-development, which explores sentiment development in German news articles about migration between 2007 and 2019.

Code for inference (predicting sentiment polarity) on raw text can be found at https://github.com/text-analytics-20/news-sentiment-development/blob/main/sentiment_analysis/bert.py

If you are not interested in polarity but just want to predict discrete class labels (0: positive, 1: negative, 2: neutral), you can also use the model with Oliver Guhr's `germansentiment` package as follows:

First install the package from PyPI:

```bash
pip install germansentiment
```

Then you can use the model in Python:

```python
from germansentiment import SentimentModel

model = SentimentModel('mdraw/german-news-sentiment-bert')

# Examples from our validation dataset
texts = [
    '[...], schwärmt der parteilose Vizebürgermeister und Historiker Christian Matzka von der "tollen Helferszene".',
    'Flüchtlingsheim 11.05 Uhr: Massenschlägerei',
    'Rotterdam habe einen Migrantenanteil von mehr als 50 Prozent.',
]

result = model.predict_sentiment(texts)

print(result)
```

The code above will print:

```python
['positive', 'negative', 'neutral']
```

{}
transformers pytorch jax safetensors bert text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 

# German sentiment BERT finetuned on news data

Sentiment analysis model based on URL with additional training on German news texts about migration.

This model is part of the project URL which explores sentiment development in German news articles about migration between 2007 and 2019.

Code for inference (predicting sentiment polarity) on raw text can be found at URL

If you are not interested in polarity but just want to predict discrete class labels (0: positive, 1: negative, 2: neutral), you can also use the model with Oliver Guhr's 'germansentiment' package as follows:

First install the package from PyPI:



Then you can use the model in Python:



The code above will print:



# German sentiment BERT finetuned on news data

Sentiment analysis model based on URL with additional training on German news texts about migration.

This model is part of the project URL which explores sentiment development in German news articles about migration between 2007 and 2019.

Code for inference (predicting sentiment polarity) on raw text can be found at URL

If you are not interested in polarity but just want to predict discrete class labels (0: positive, 1: negative, 2: neutral), you can also use the model with Oliver Guhr's 'germansentiment' package as follows:

First install the package from PyPI:



Then you can use the model in Python:



The code above will print:
TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
 # German sentiment BERT finetuned on news data

Sentiment analysis model based on URL with additional training on German news texts about migration.

This model is part of the project URL which explores sentiment development in German news articles about migration between 2007 and 2019.

Code for inference (predicting sentiment polarity) on raw text can be found at URL

If you are not interested in polarity but just want to predict discrete class labels (0: positive, 1: negative, 2: neutral), you can also use the model with Oliver Guhr's 'germansentiment' package as follows:

First install the package from PyPI:



Then you can use the model in Python:



The code above will print:
passage: TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
# German sentiment BERT finetuned on news data

Sentiment analysis model based on URL with additional training on German news texts about migration.

This model is part of the project URL which explores sentiment development in German news articles about migration between 2007 and 2019.

Code for inference (predicting sentiment polarity) on raw text can be found at URL

If you are not interested in polarity but just want to predict discrete class labels (0: positive, 1: negative, 2: neutral), you can also use the model with Oliver Guhr's 'germansentiment' package as follows:

First install the package from PyPI:



Then you can use the model in Python:



The code above will print: