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license: apache-2.0 |
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This language model is designed to assess the attitude expressed in texts about **climate change**. |
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It categorizes the attitude into three types: risk, neutral, and opportunity. |
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These categories correspond to the negative, neutral, and positive classifications commonly used in sentiment analysis. |
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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 or using the testing code. |
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Otherwise, the model does not work so well. e,.g, An example input could be, "Major oil companies have misled Americans for decades about the threat of human-caused climate change, according to a new report released Tuesday by Democrats in Congress. |
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The 65-page report was the result of a three-year investigation and was made public hours before a Senate Budget Committee hearing about the role that oil and gas companies have played in global warming. |
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" |
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Please **cite**: "Sun., K, and Wang, R. 2024. The fine-tuned language model for detecting human attitudes to climate changes" if you use this model. |
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The project in github (including training code) is available at: https://github.com/fivehills/climate_attitude_LM/blob/main/README.md |
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The following code shows how to test in the model. |
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``` |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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import torch |
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# Load model and tokenizer |
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model_path = "KevSun/climate-attitude-LM" # Ensure this path points to the correct directory |
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model = AutoModelForSequenceClassification.from_pretrained(model_path) |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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# Define the path to your text file |
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file_path = 'yourtext.txt' |
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# Read the content of the file |
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with open(file_path, 'r', encoding='utf-8') as file: |
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new_text = file.read() |
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# Encode the text using the tokenizer used during training |
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encoded_input = tokenizer(new_text, return_tensors='pt', padding=True, truncation=True, max_length=64) |
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# Move the model to the correct device (CPU or GPU if available) |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model = model.to(device) # Move model to the correct device |
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encoded_input = {k: v.to(device) for k, v in encoded_input.items()} # Move tensor to the correct device |
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model.eval() # Set the model to evaluation mode |
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# Perform the prediction |
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with torch.no_grad(): |
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outputs = model(**encoded_input) |
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# Get the predictions (assumes classification with labels) |
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predictions = outputs.logits.squeeze() |
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# Assuming softmax is needed to interpret the logits as probabilities |
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probabilities = torch.softmax(predictions, dim=0) |
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# Define labels for each class index based on your classification categories |
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labels = ["risk", "neutral", "opportunity"] |
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predicted_index = torch.argmax(probabilities).item() # Get the index of the max probability |
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predicted_label = labels[predicted_index] |
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predicted_probability = probabilities[predicted_index].item() |
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# Print the predicted label and its probability |
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print(f"Predicted Label: {predicted_label}, Probability: {predicted_probability:.4f}") |
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##the output example: predicted Label: neutral, Probability: 0.8377 |
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``` |
