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--- |
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language: en |
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tags: |
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- financial |
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- stocks |
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- sentiment |
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datasets: |
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- Jean-Baptiste/financial_news_sentiment_mixte_with_phrasebank_75 |
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widget: |
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- text: "Fortuna Silver Mines Inc. (NYSE: FSM) (TSX: FVI) reports solid production results for the third quarter of 2022 from its four operating mines in the Americas and West Africa." |
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- text: "Theratechnologies Reports Financial Results for the Third Quarter of Fiscal 2022 and Provides Business Update -- - Q3 2022 Consolidated Revenue Growth of 17% to $20.8 million" |
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- text: "The Flowr Corporation To File for CCAA Protection" |
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- text: "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" |
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license: mit |
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--- |
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# Model fine-tuned from roberta-large for sentiment classification of financial news (emphasis on Canadian news). |
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### Introduction |
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This model was train on financial_news_sentiment_mixte_with_phrasebank_75 dataset. |
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This is a customized version of the phrasebank dataset in which I kept only sentence validated by at least 75% annotators. |
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In addition I added ~2000 articles validated manually on Canadian financial news. Therefore the model is more specifically trained for Canadian news. |
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Final result is f1 score of 93.25% overall and 83.6% on Canadian news. |
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### Training data |
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Training data was classified as follow: |
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class |Description |
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-|- |
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0 |negative |
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1 |neutral |
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2 |positive |
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### How to use roberta-large-financial-news-sentiment-en with HuggingFace |
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##### Load roberta-large-financial-news-sentiment-en and its sub-word tokenizer : |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/roberta-large-financial-news-sentiment-en") |
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model = AutoModelForSequenceClassification.from_pretrained("Jean-Baptiste/roberta-large-financial-news-sentiment-en") |
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##### Process text sample (from wikipedia) |
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from transformers import pipeline |
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pipe = pipeline("text-classification", model=model, tokenizer=tokenizer) |
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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") |
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[{'label': 'negative', 'score': 0.9399105906486511}] |
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``` |
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### Model performances |
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Overall f1 score (average macro) |
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precision|recall|f1 |
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-|-|- |
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0.9355|0.9299|0.9325 |
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By entity |
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entity|precision|recall|f1 |
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-|-|-|- |
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negative|0.9605|0.9240|0.9419 |
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neutral|0.9538|0.9459|0.9498 |
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positive|0.8922|0.9200|0.9059 |
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