final-luna-sentiment-analysis for Financial Sentiment Analysis - (2024)
This model provides a ranking of sentiment based on given financial news.
This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Model Description
The base model I used was cardiffnlp/twitter-roberta-base-sentiment-latest. I used Twitter financial news' comments and headlines, with 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 Twitter financial news data for accuracy.
Downloads: 5,667 (all time)
- Developed by: Atoma Media
- Model type: Classification
- Language(s) (NLP): English
- License: Apache-2.0
- Finetuned from model [optional]: [More Information Needed]
How to Get Started with the Model
from transformers import pipeline
pipe = pipeline("text-classification", model="snoneeightfive/luna-sentiment-analysis")
pipe("Defense stocks are steadily rising ") # Your financial headline
[{'label': 'positive', 'score': 0.6553508639335632}] # Example output
Use a pipeline as a high-level helper
Evaluation
Accuracy: 80%
Testing Data, Factors & Metrics
Testing Data
Financial headlines from Twittter.
Model Card Authors
Shreya Nakum
Model Card Contact
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