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--- |
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license: apache-2.0 |
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language: |
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- en |
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metrics: |
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- accuracy |
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base_model: |
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- cardiffnlp/twitter-roberta-base-sentiment-latest |
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--- |
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# final-luna-sentiment-analysis for Financial Sentiment Analysis - (2024) |
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This model provides a ranking of sentiment based on given financial news. |
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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). |
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## Model Details |
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### Model Description |
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The base model I used was cardiffnlp/twitter-roberta-base-sentiment-latest. I used Twitter financial news' comments and headlines, with |
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sentiment ranging from 1 to 10 and positive, negative, or neutral to describe it. I then fine-tuned the model and tested it from more |
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Twitter financial news data for accuracy. |
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Downloads: 5,667 (all time) |
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- **Developed by:** Atoma Media |
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- **Model type:** Classification |
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- **Language(s) (NLP):** English |
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- **License:** Apache-2.0 |
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- **Finetuned from model [optional]:** [More Information Needed] |
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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="snoneeightfive/luna-sentiment-analysis") |
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pipe("Defense stocks are steadily rising ") # Your financial headline |
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[{'label': 'positive', 'score': 0.6553508639335632}] # Example output |
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``` |
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# Use a pipeline as a high-level helper |
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## Evaluation |
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Accuracy: 80% |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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Financial headlines from Twittter. |
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## Model Card Authors |
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Shreya Nakum |
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## Model Card Contact |
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[email protected] |
