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README.md
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pipeline_tag: text-classification
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# Model Card for
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<!-- Provide a quick summary of what the model is/does. -->
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This model was created by fine-tuning the base model on a dataset containing summaries of stock market news along with the corresponding price variations.
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I also compared it with FinBERT, and the results are quite similar.
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Interestingly, it can detect the sentiment of financial news, even though it was trained using a different methodology than the one used with FinBERT.
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Also, as we know, not all stocks react the same way to a specific event. That’s why I created additional models tailored for individual stocks.
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You can find them on my profile, along with the datasets used.
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This particular model is designed for general news — try it with phrases like “The stock market crashed” or “Tesla’s price dropped.”
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When evaluating news related to investment opportunities, this general model might provide a neutral score, whereas the stock-specific models can estimate potential price variations that could occur after such events.
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The predictions are tailored to each individual stock.
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pipeline_tag: text-classification
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---
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# Model Card for SentimentBasedOnPriceVariation
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<!-- Provide a quick summary of what the model is/does. -->
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This model was created by fine-tuning the base model on a dataset containing summaries of stock market news along with the corresponding price variations.
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|
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| 18 |
I also compared it with FinBERT, and the results are quite similar.
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| 19 |
Interestingly, it can detect the sentiment of financial news, even though it was trained using a different methodology than the one used with FinBERT.
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| 20 |
Also, as we know, not all stocks react the same way to a specific event. That’s why I created additional models tailored for individual stocks.
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| 21 |
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You can find them on my profile, along with the datasets used. (SelmaNajih001/PricePredictionForTesla and SelmaNajih001/PricePredictionForMicrosoft)
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This particular model is designed for general news — try it with phrases like “The stock market crashed” or “Tesla’s price dropped.”
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| 23 |
When evaluating news related to investment opportunities, this general model might provide a neutral score, whereas the stock-specific models can estimate potential price variations that could occur after such events.
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| 24 |
The predictions are tailored to each individual stock.
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