| | --- |
| | license: apache-2.0 |
| | --- |
| | This 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. |
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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%. |
| | |
| | **Note** that you should paste or type a text concerning the **climate change** in the API input bar or using the testing code. |
| | 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. |
| | 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. |
| | " |
| | |
| | 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. |
| | |
| | The project in github (including training code) is available at: https://github.com/fivehills/climate_attitude_LM/ |
| | |
| | The following code shows how to test in the model. |
| | |
| | ``` |
| | from transformers import AutoModelForSequenceClassification, AutoTokenizer |
| | import torch |
| | |
| | # Load model and tokenizer |
| | model_path = "KevSun/climate-attitude-LM" # Ensure this path points to the correct directory |
| | model = AutoModelForSequenceClassification.from_pretrained(model_path) |
| | tokenizer = AutoTokenizer.from_pretrained(model_path) |
| |
|
| | # Define the path to your text file |
| | file_path = 'yourtext.txt' |
| | |
| | # Read the content of the file |
| | with open(file_path, 'r', encoding='utf-8') as file: |
| | new_text = file.read() |
| | |
| | # Encode the text using the tokenizer used during training |
| | encoded_input = tokenizer(new_text, return_tensors='pt', padding=True, truncation=True, max_length=64) |
| |
|
| | # Move the model to the correct device (CPU or GPU if available) |
| | device = "cuda" if torch.cuda.is_available() else "cpu" |
| | model = model.to(device) # Move model to the correct device |
| | encoded_input = {k: v.to(device) for k, v in encoded_input.items()} # Move tensor to the correct device |
| | |
| | model.eval() # Set the model to evaluation mode |
| | |
| | # Perform the prediction |
| | with torch.no_grad(): |
| | outputs = model(**encoded_input) |
| | |
| | # Get the predictions (assumes classification with labels) |
| | predictions = outputs.logits.squeeze() |
| |
|
| | # Assuming softmax is needed to interpret the logits as probabilities |
| | probabilities = torch.softmax(predictions, dim=0) |
| |
|
| | # Define labels for each class index based on your classification categories |
| | labels = ["risk", "neutral", "opportunity"] |
| | predicted_index = torch.argmax(probabilities).item() # Get the index of the max probability |
| | predicted_label = labels[predicted_index] |
| | predicted_probability = probabilities[predicted_index].item() |
| | |
| | # Print the predicted label and its probability |
| | print(f"Predicted Label: {predicted_label}, Probability: {predicted_probability:.4f}") |
| | |
| | ##the output example: predicted Label: neutral, Probability: 0.8377 |
| | |
| | ``` |
