Text Classification
Transformers
PyTorch
Safetensors
Japanese
bert
financial-sentiment-analysis
sentiment-analysis
Eval Results (legacy)
Instructions to use bardsai/finance-sentiment-ja-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bardsai/finance-sentiment-ja-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bardsai/finance-sentiment-ja-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bardsai/finance-sentiment-ja-base") model = AutoModelForSequenceClassification.from_pretrained("bardsai/finance-sentiment-ja-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model (#5)
- Adding `safetensors` variant of this model (dab6d8ecb95721e62d7b26b00869b87acc6ee711) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>abfd341 -
docs: fix year typo (20014 -> 2014) in Malo et al. citation
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docs: add CC BY-SA 3.0 license (inherited from tohoku bert-base-japanese), base_model + metadata per HF guidelines
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Upload folder using huggingface_hub
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Upload README.md with huggingface_hub
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initial commit
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