# German Sentiment Analysis

This model predicts sentiment for German text. 

# Usage
First set up the model:

```python
# if necessary:
# !pip install transformers
from transformers import pipeline

sentiment_model = pipeline(model="aari1995/German_Sentiment")
```

to use it:

```python
sentence = ["Ich liebe die Bahn. Pünktlich wie immer ... -.-","Krasser Service"]
result = sentiment_model(sentence)
print(result)
#Output:
#[{'label': 'negative', 'score': 0.4935680031776428},{'label': 'positive', 'score': 0.5790663957595825}]
```


# Credits / Special Thanks:
This model was fine-tuned by Aaron Chibb. It is trained on [twitter dataset by tygiangz](https://huggingface.co/datasets/tyqiangz/multilingual-sentiments) and based on gBERT-large by [deepset](https://huggingface.co/deepset/gbert-large).
{"language": ["de"], "tags": ["deepset/gbert-large"]}
transformers pytorch safetensors bert text-classification deepset/gbert-large de autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #deepset/gbert-large #de #autotrain_compatible #endpoints_compatible #region-us 

# German Sentiment Analysis

This model predicts sentiment for German text. 

# Usage
First set up the model:



to use it:




# Credits / Special Thanks:
This model was fine-tuned by Aaron Chibb. It is trained on twitter dataset by tygiangz and based on gBERT-large by deepset.
# German Sentiment Analysis

This model predicts sentiment for German text. # Usage
First set up the model:



to use it: # Credits / Special Thanks:
This model was fine-tuned by Aaron Chibb. It is trained on twitter dataset by tygiangz and based on gBERT-large by deepset.
TAGS
#transformers #pytorch #safetensors #bert #text-classification #deepset/gbert-large #de #autotrain_compatible #endpoints_compatible #region-us 
 # German Sentiment Analysis

This model predicts sentiment for German text. # Usage
First set up the model:



to use it: # Credits / Special Thanks:
This model was fine-tuned by Aaron Chibb. It is trained on twitter dataset by tygiangz and based on gBERT-large by deepset.