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