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---
language: en
license: apache-2.0
tags:
- finance
- sentiment-analysis
- roberta
- classification
model-index:
- name: RoBERTa-Large for Financial Sentiment Analysis
results:
- task:
type: text-classification
name: Sentiment Analysis
dataset:
name: Finance News Sentiments (Kaggle)
type: text
metrics:
- type: accuracy
value: 0.7627
- type: multiclass_roc_auc
value: 0.9124
base_model:
- FacebookAI/roberta-large
---
# RoBERTa-Large Fine-Tuned for Financial Sentiment Analysis
This repository contains a RoBERTa-based model for financial sentiment classification. The model predicts whether a financial news headline or sentence is **positive**, **neutral**, or **negative**.
## Model Overview
- **Base model:** RoBERTa-Large
- **Task:** Financial sentiment classification (3 classes)
- **Training data:** Financial news headlines and sentences
- **Dataset source:** [Kaggle - Finance News Sentiments](https://www.kaggle.com/datasets/antobenedetti/finance-news-sentiments/data?select=dataset.csv)
- **Output labels:**
- 0: Negative
- 1: Neutral
- 2: Positive
## Evaluation Results
- **Test Accuracy:** 0.7627
- **Multiclass ROC AUC (macro-average):** 0.9124
## Model Folder Structure
```
roberta_finance_sentiment/
config.json
merges.txt
model.safetensors
special_tokens_map.json
tokenizer_config.json
tokenizer.json
vocab.json
```
**Note:** Only the model files are stored in `roberta_finance_sentiment/`. Scripts and datasets are kept separate and are not included in this folder or in the model upload.
## How to Use the Fine-Tuned Model
### 1. Load and Use the Model in Python
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Directory of the model folder
model_dir = "roberta_finance_sentiment"
# read the model
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForSequenceClassification.from_pretrained(model_dir)
model.eval()
# Example
text = "Apple stock surges after strong earnings report."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
with torch.no_grad():
logits = model(**inputs).logits
pred = torch.argmax(logits, dim=1).item()
label_map = {0: 'negative', 1: 'neutral', 2: 'positive'}
print(f"Predicted sentiment: {label_map[pred]}")
```
## Notes
- The model was trained and evaluated on data from the Kaggle dataset linked above.
- The `roberta_finance_sentiment/` folder contains only the files needed for inference.
- Scripts and datasets are not included in the model folder or in the model upload.
- For best results, use a GPU for inference if available.
## Limitations
- Model is trained on headline-level sentiment.
- Sarcasm, irony, or complex phrasing may reduce prediction accuracy.
---
**Date:** June 2025 |