---
license: apache-2.0
language:
- en
metrics:
- accuracy
base_model:
- cardiffnlp/twitter-roberta-base-sentiment-latest
---
# 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](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## 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

```python
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

snakum@uci.edu