README.md

---
license: llama3
datasets:
- TimKoornstra/financial-tweets-sentiment
- takala/financial_phrasebank
language:
- en
pipeline_tag: text-classification
tags:
- text-classification
- sequence-classification
widget:
  - text: "I liked this movie"
    output:
      - label: POSITIVE
        score: 0.8
      - label: NEGATIVE
        score: 0.2
---

# Model Card for FinLlama-3-8B

This model card provides details for the FinLlama-3-8B model, which is fine-tuned for sentiment analysis on financial tweets and phrases.

## Model Details

### Model Description

FinLlama-3-8B is a fine-tuned version of the Llama-3-8B model specifically tailored for sentiment analysis in the financial domain. It can classify text into three sentiment categories: positive, neutral, and negative.

- **Model type:** Sequence Classification
- **Language(s) (NLP):** English
- **License:** llama3
- **Finetuned from model [optional]:** Llama-3-8B

## Uses

### Direct Use

FinLlama-3-8B can be directly used for sentiment analysis on financial text, providing sentiment labels (positive, neutral, negative) for given inputs.

### Downstream Use [optional]

The model can be integrated into larger financial analysis systems to provide sentiment insights as part of broader financial data analytics.

### Out-of-Scope Use

This model is not suitable for non-financial text sentiment analysis or for languages other than English.

## Bias, Risks, and Limitations

### Recommendations

Users should be aware of potential biases in the training data, which may affect the model's performance on certain subpopulations or topics. Continuous monitoring and evaluation are recommended.

## How to Get Started with the Model

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("roma2025/FinLlama-3-8B")
tokenizer = AutoTokenizer.from_pretrained("roma2025/FinLlama-3-8B")

def get_sentiment_score(model, tokenizer, text):
    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=75)
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
    probabilities = F.softmax(logits, dim=-1)
    sentiment_score = torch.argmax(probabilities, dim=-1).item()
    return sentiment_score, probabilities

# Example usage
text = "Determine the sentiment of the financial news as negative, neutral or positive:
The stock market is going up!
Sentiment:"

sentiment_score, probabilities = get_sentiment_score(model, tokenizer, text)