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

[email protected]