Roberta Large Financial News Sentiment En

🚀 用于金融新闻情感分类的roberta - large微调模型（侧重加拿大新闻）

本模型基于roberta - large进行微调，用于金融新闻的情感分类，尤其侧重于加拿大新闻。它能有效识别金融新闻中的情感倾向，为金融领域的信息分析提供有力支持。

🚀 快速开始

模型介绍

此模型在financial_news_sentiment_mixte_with_phrasebank_75数据集上进行训练。这是phrasebank数据集的定制版本，其中仅保留了至少75%标注者验证过的句子。此外，还添加了约2000篇手动验证的加拿大金融新闻文章。因此，该模型更专门针对加拿大新闻进行了训练。最终结果显示，整体F1分数为93.25%，在加拿大新闻上的F1分数为83.6%。

📦 安装指南

使用HuggingFace加载模型

以下是加载roberta-large-financial-news-sentiment-en模型及其子词分词器的代码示例：

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")

处理文本样本

以下是使用加载好的模型处理文本样本的代码示例：

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

基础用法

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}]

📚 详细文档

训练数据

训练数据的分类情况如下：

模型性能

整体F1分数（宏平均）

按实体划分

📄 许可证

本项目采用MIT许可证。