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language:
- de
license: mit
library_name: transformers
pipeline_tag: text-classification
tags:
- text-classification
- financial-sentiment-analysis
- sentiment-analysis
base_model: google-bert/bert-base-german-cased
datasets:
- financial_phrasebank
metrics:
- f1
- accuracy
- precision
- recall
widget:
- text: "Der Nettoumsatz stieg um 30 % auf 36 Mio. EUR."
example_title: "Example 1"
- text: "Der schwarze Freitag beginnt. Liste der Werbeaktionen in den Geschäften."
example_title: "Example 2"
- text: "Die CDPROJEKT-Aktie verzeichnete den stärksten Rückgang unter den an der WSE notierten Unternehmen."
example_title: "Example 3"
model-index:
- name: finance-sentiment-de-base
results:
- task:
type: text-classification
name: Financial Sentiment Analysis
dataset:
name: Financial PhraseBank (translated to German)
type: financial_phrasebank
metrics:
- type: f1
value: 0.955
name: F1 (macro)
- type: precision
value: 0.960
name: Precision (macro)
- type: recall
value: 0.950
name: Recall (macro)
- type: accuracy
value: 0.966
name: Accuracy
---
# Finance Sentiment DE (base)
Finance Sentiment DE (base) is a model based on [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) for analyzing sentiment of German financial news. It was trained on the translated version of [Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts) by Malo et al. (2014) for 10 epochs on single RTX3090 gpu.
The model will give you a three labels: positive, negative and neutral.
## How to use
You can use this model directly with a pipeline for sentiment-analysis:
```python
from transformers import pipeline
nlp = pipeline("sentiment-analysis", model="bardsai/finance-sentiment-de-base")
nlp("Der Nettoumsatz stieg um 30 % auf 36 Mio. EUR.")
```
```bash
[{'label': 'positive', 'score': 0.9987998807375955}]
```
## Performance
| Metric | Value |
| --- | ----------- |
| f1 macro | 0.955 |
| precision macro | 0.960 |
| recall macro | 0.950 |
| accuracy | 0.966 |
| samples per second | 135.2 |
(The performance was evaluated on RTX 3090 gpu)
## Changelog
- 2023-09-18: Initial release
## License
This model is released under the **[MIT License](https://opensource.org/licenses/MIT)**, inherited from the base model [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) (MIT).
Attribution: bert-base-german-cased — deepset; Finance Sentiment DE (base) — bards.ai.
## About bards.ai
At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: [bards.ai](https://bards.ai/)
Let us know if you use our model :). Also, if you need any help, feel free to contact us at info@bards.ai
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