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README.md
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The language model is designed to assess the attitude expressed in texts about climate change. It categorizes the attitude into three types: risk, neutral, and opportunity. These categories correspond to the negative, neutral, and positive classifications commonly used in sentiment analysis. We employed a fine-tuning approach to adapt the final layer of the "cardiffnlp/twitter-roberta-base-sentiment-latest" model using a training dataset from "climatebert/climate_sentiment."
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In comparison to similar existing models, such as "climatebert/distilroberta-base-climate-sentiment" and "XerOpred/twitter-climate-sentiment-model," which typically achieve accuracies ranging from 10% to 30% and F1 scores around 15%, our model demonstrates exceptional performance. When
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Note that you should paste or type a text concerning the climate change in the API input bar. Otherwise, the model does not work. e,.g, the example input, "The Deputy Mayor also said that while RRF regulations are clear at EU level, bureaucracy at national level in Italy has led to confusion for cities. “At national level, the implementation of RRF funds is not straightforward, there are ministries that have their own rules and cities get different information from different people,” she explained.
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Adding to the discussion on whether a simpler, faster model, led by national governments, is best for dispersing EU funds, Boni stressed the importance of finding the right balance.“
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The language model is designed to assess the attitude expressed in texts about climate change. It categorizes the attitude into three types: risk, neutral, and opportunity. These categories correspond to the negative, neutral, and positive classifications commonly used in sentiment analysis. We employed a fine-tuning approach to adapt the final layer of the "cardiffnlp/twitter-roberta-base-sentiment-latest" model using a training dataset from "climatebert/climate_sentiment."
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In comparison to similar existing models, such as "climatebert/distilroberta-base-climate-sentiment" and "XerOpred/twitter-climate-sentiment-model," which typically achieve accuracies ranging from 10% to 30% and F1 scores around 15%, our model demonstrates exceptional performance. When evaluated using the test dataset from "climatebert/climate_sentiment," it achieves an accuracy of 89% and an F1 score of 89%.
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Note that you should paste or type a text concerning the climate change in the API input bar. Otherwise, the model does not work. e,.g, the example input, "The Deputy Mayor also said that while RRF regulations are clear at EU level, bureaucracy at national level in Italy has led to confusion for cities. “At national level, the implementation of RRF funds is not straightforward, there are ministries that have their own rules and cities get different information from different people,” she explained.
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Adding to the discussion on whether a simpler, faster model, led by national governments, is best for dispersing EU funds, Boni stressed the importance of finding the right balance.“
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