Roberta Large Financial News Sentiment En

🚀 用於金融新聞情感分類的roberta - large微調模型（側重加拿大新聞）

本模型基於roberta - large進行微調，用於金融新聞的情感分類，尤其側重於加拿大新聞。它能有效識別金融新聞中的情感傾向，為金融領域的信息分析提供有力支持。

🚀 快速開始

模型介紹

此模型在financial_news_sentiment_mixte_with_phrasebank_75數據集上進行訓練。這是phrasebank數據集的定製版本，其中僅保留了至少75%標註者驗證過的句子。此外，還添加了約2000篇手動驗證的加拿大金融新聞文章。因此，該模型更專門針對加拿大新聞進行了訓練。最終結果顯示，整體F1分數為93.25%，在加拿大新聞上的F1分數為83.6%。

📦 安裝指南

使用HuggingFace加載模型

以下是加載roberta-large-financial-news-sentiment-en模型及其子詞分詞器的代碼示例：

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")

處理文本樣本

以下是使用加載好的模型處理文本樣本的代碼示例：

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}]

💻 使用示例

基礎用法

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")

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}]

📚 詳細文檔

訓練數據

訓練數據的分類情況如下：

模型性能

整體F1分數（宏平均）

按實體劃分

📄 許可證

本項目採用MIT許可證。