Text Classification
Transformers
PyTorch
Safetensors
German
bert
financial-sentiment-analysis
sentiment-analysis
Eval Results (legacy)
text-embeddings-inference
Instructions to use bardsai/finance-sentiment-de-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bardsai/finance-sentiment-de-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bardsai/finance-sentiment-de-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bardsai/finance-sentiment-de-base") model = AutoModelForSequenceClassification.from_pretrained("bardsai/finance-sentiment-de-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
docs: fix year typo (20014 -> 2014) in Malo et al. citation
Browse files
README.md
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# Finance Sentiment DE (base)
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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. (
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The model will give you a three labels: positive, negative and neutral.
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# Finance Sentiment DE (base)
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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.
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The model will give you a three labels: positive, negative and neutral.
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