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README.md
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To use this model, first set it up:
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# if necessary:
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# pip install transformers
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from transformers import pipeline
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sentiment_model = pipeline(model=aari1995/German_Sentiment)
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to use it:
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sentence = ["Ich liebe die Bahn. Pünktlich wie immer ... -.-","Meine Beschwerde wurde super abgewickelt"]
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result = sentiment_model(sentence)
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print(result)
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#Output:
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#[{'label': 'negative', 'score': 0.4935680031776428},{'label': 'positive', 'score': 0.4388483762741089}]
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Credits / Special Thanks:
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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).
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To use this model, first set it up:
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```python
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# if necessary:
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# pip install transformers
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from transformers import pipeline
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sentiment_model = pipeline(model=aari1995/German_Sentiment)
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```
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to use it:
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```python
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sentence = ["Ich liebe die Bahn. Pünktlich wie immer ... -.-","Meine Beschwerde wurde super abgewickelt"]
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result = sentiment_model(sentence)
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print(result)
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#Output:
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#[{'label': 'negative', 'score': 0.4935680031776428},{'label': 'positive', 'score': 0.4388483762741089}]
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```
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Credits / Special Thanks:
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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).
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