Instructions to use SelmaNajih001/SentimentBasedOnPriceVariation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SelmaNajih001/SentimentBasedOnPriceVariation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SelmaNajih001/SentimentBasedOnPriceVariation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SelmaNajih001/SentimentBasedOnPriceVariation") model = AutoModelForSequenceClassification.from_pretrained("SelmaNajih001/SentimentBasedOnPriceVariation") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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### Direct Use
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The model can be used directly to estimate price movement signals from news headlines or summaries. Users can input general market news or specific company news, and the model will return a predicted price movement direction or sentiment.
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## How to Get Started with the Model
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pipe("Apple Stock Rises On New U.S. Investment Commitment")
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### Results
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The accuracy was about 97%
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### Direct Use
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The model can be used directly to estimate price movement signals from news headlines or summaries. Users can input general market news or specific company news, and the model will return a predicted price movement direction or sentiment.
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### Results
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The accuracy was about 97%
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## How to Get Started with the Model
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pipe("Apple Stock Rises On New U.S. Investment Commitment")
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