Instructions to use yonichi/cbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yonichi/cbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yonichi/cbert", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yonichi/cbert", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("yonichi/cbert", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
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README.md
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language:
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- en
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tags:
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- text-classification
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- financial-sentiment-analysis
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- sentiment analysis
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widget:
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- text: "Energy prices, which surged by 31.7 per cent in February, continue to be the main reason for this high rate of inflation and are also pushing up prices across many other sectors"
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Sentiment classification / Text classification.
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Pre-trained by Yiyang with a financial corpus and fine-tuned with FED speak for hawkish/dovish classification.
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