Instructions to use scherrmann/GermanFinBert_SC_Sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scherrmann/GermanFinBert_SC_Sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="scherrmann/GermanFinBert_SC_Sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("scherrmann/GermanFinBert_SC_Sentiment") model = AutoModelForSequenceClassification.from_pretrained("scherrmann/GermanFinBert_SC_Sentiment") - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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- text: "Zukünftig soll jedoch je Geschäftsjahr eine Mindestdividende in Höhe von EUR 2,00 je dividendenberechtigter Aktie an die Aktionärinnen und Aktionäre ausgeschüttet werden."
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# German FinBERT For
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German FinBERT is a BERT language model focusing on the financial domain within the German language. In my [paper](https://arxiv.org/pdf/2311.08793.pdf), I describe in more detail the steps taken to train the model and show that it outperforms its generic benchmarks for finance specific downstream tasks.
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- text: "Zukünftig soll jedoch je Geschäftsjahr eine Mindestdividende in Höhe von EUR 2,00 je dividendenberechtigter Aktie an die Aktionärinnen und Aktionäre ausgeschüttet werden."
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- text: "Comet passt Jahresprognose nach Q3 unter Erwartungen an"
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# German FinBERT For Sentiment Analysis (Pre-trained From Scratch Version, Fine-Tuned for Financial Sentiment Analysis)
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German FinBERT is a BERT language model focusing on the financial domain within the German language. In my [paper](https://arxiv.org/pdf/2311.08793.pdf), I describe in more detail the steps taken to train the model and show that it outperforms its generic benchmarks for finance specific downstream tasks.
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