roberta-financial-news-impact-analysis

Financial News Impact Analysis Using RoBERTa

This is a RoBERTa-base model trained on 15k financial news title from January 1, 2021 to April 22, 2024 and finetuned for market impact analysis. The data is taken from forexfactory.com. This model is suitable for English.

Labels: 0 -> Low, 1 -> Medium, 2 -> High

Example

from transformers import AutoModelForSequenceClassification
from transformers import RobertaTokenizerFast
import torch

label_mapping = {
    0: "Low",
    1: "Medium",
    2: "High"
}

MODEL = "nusret35/roberta-financial-news-impact-analysis"
tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base')
model = AutoModelForSequenceClassification.from_pretrained(MODEL)

input_text = "German Buba President Nagel Speaks"
encoding = tokenizer(input_text, padding='max_length', truncation=True, max_length=128, return_tensors='pt')
input_ids =  encoding['input_ids'].flatten()
attention_mask = encoding['attention_mask'].flatten()
input_ids = input_ids.unsqueeze(0)
attention_mask = attention_mask.unsqueeze(0)

output = model(input_ids,attention_mask)
predicted_class_index = torch.argmax(output.logits)
predicted_label = label_mapping[predicted_class_index.item()]
print("Predicted Impact:", predicted_label)

Output:

Predicted Impact: Low