Model fine-tuned from roberta-large for sentiment classification of financial news (emphasis on Canadian news).

Introduction

This model was train on financial_news_sentiment_mixte_with_phrasebank_75 dataset. This is a customized version of the phrasebank dataset in which I kept only sentence validated by at least 75% annotators. In addition I added ~2000 articles validated manually on Canadian financial news. Therefore the model is more specifically trained for Canadian news. Final result is f1 score of 93.25% overall and 83.6% on Canadian news.

Training data

Training data was classified as follow:

How to use roberta-large-financial-news-sentiment-en with HuggingFace

Load roberta-large-financial-news-sentiment-en and its sub-word tokenizer :

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/roberta-large-financial-news-sentiment-en")
model = AutoModelForSequenceClassification.from_pretrained("Jean-Baptiste/roberta-large-financial-news-sentiment-en")


##### Process text sample (from wikipedia)

from transformers import pipeline

pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
pipe("Melcor REIT (TSX: MR.UN) today announced results for the third quarter ended September 30, 2022. Revenue was stable in the quarter and year-to-date. Net operating income was down 3% in the quarter at $11.61 million due to the timing of operating expenses and inflated costs including utilities like gas/heat and power")

[{'label': 'negative', 'score': 0.9399105906486511}]

Model performances

Overall f1 score (average macro)

By entity