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Updated the link to the paper (#4570) 

I looks like the conference has changed the link to the paper.
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Oliver Guhr 2020-05-25 21:29:50 +02:00 committed by GitHub
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model_cards/oliverguhr/german-sentiment-bert/README.md
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@ -34,7 +34,7 @@ The code above will output following list:
["negative","negative","positive","positive","neutral", "neutral"]
```
## minimal working Sample
## A minimal working Sample
```python
@ -90,7 +90,7 @@ print(model.predict_sentiment(texts))
## Model and Data
If you are interested in code and data that was used to train this model please have a look at [this repository](https://github.com/oliverguhr/german-sentiment) and our [paper](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.201.pdf). Here is a table of the F1 scores that his model achieves on following datasets. Since we trained this model on a newer version of the transformer library, the results are slightly better than reported in the paper.
If you are interested in code and data that was used to train this model please have a look at [this repository](https://github.com/oliverguhr/german-sentiment) and our [paper](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.202.pdf). Here is a table of the F1 scores that his model achieves on following datasets. Since we trained this model on a newer version of the transformer library, the results are slightly better than reported in the paper.
| Dataset | F1 micro Score |
| :----------------------------------------------------------- | -------------: |