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
- Xet hash:
- a3b0fb1f8c97eb1ca6ebe9c2de4aada5366a88d84d9308a6dcaa367a6e36a5fe
- Size of remote file:
- 436 MB
- SHA256:
- 16e07c42b1c705d7f8aa3bbb02e86f04e9bda7297cbc295d9a7317f50dd84d84
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