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--- |
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tags: |
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- finance |
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- stock |
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license: cc-by-4.0 |
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datasets: |
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- SelmaNajih001/EventStockPriceVariation |
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language: |
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- en |
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base_model: |
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- allenai/longformer-base-4096 |
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pipeline_tag: text-classification |
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--- |
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# Model Card for PricePredictionForTesla |
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This model was created by fine-tuning a base transformer model on a dataset containing summaries of Tesla stock news along with corresponding price variations. |
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It is tailored specifically for predicting Tesla stock price movements after news events, providing more precise predictions than a general market model. |
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## Model Details |
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### Model Description |
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- **Developed by:** Salma Najih |
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- **Model type:** Text-Classification |
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- **Language(s) (NLP):** EN |
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- **License:** CC-BY-4.0 |
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- **Finetuned from model:** allenai/longformer-base-4096 |
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## Uses |
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The model can be used directly to estimate price movement signals from Tesla news headlines or summaries. |
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### Direct Use |
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Users can input news about Tesla, and the model will return a predicted price movement. |
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It provides more accurate predictions for Tesla than the general news model. |
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### Downstream Use |
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The model can be integrated into trading analysis pipelines, financial dashboards, or event-driven investment strategies, specifically for Tesla stock. |
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## How to Get Started with the Model |
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```python |
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from transformers import pipeline |
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pipe = pipeline("text-classification", model="SelmaNajih001/PricePredictionForTesla") |
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pipe("Tesla announces new electric vehicle") |
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``` |
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Here an example of output |
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