---
license: apache-2.0
---
  The LM is to detect the attitutde to a text on climate changes. The attitutde includes three types: risk, neutral and opportunity, which is 
  similar to negative, neutral and positive in sentmental analysis. We used fine-tuning method to change the last layer of "cardiffnlp/twitter-roberta-base-sentiment-latest" using the 
  training dataset from "climatebert/climate_sentiment".

  Compared with the existing similar models (e.g, climatebert/distilroberta-base-climate-sentiment, XerOpred/twitter-climate-sentiment-model ) with the accuracy ranging from  10% to 30% and F1 score about 15%, our model shows wonderful results: accuracy 89%, and F1 score 89% if we use the test dataset from climatebert/climate_sentiment.

  The following code shows how to test in the model.
  python
  ```
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load the model and tokenizer from the directory where it's saved
model_path = "model"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Function to prepare and make predictions on text
def predict_climate_att(text):
    # Encode the text using the tokenizer
    encoded_input = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=64)
    
    # Evaluate the model on the encoded text
    model.eval()
    with torch.no_grad():
        outputs = model(**encoded_input)
    
    # Extract logits (the outputs of the model before any final activation function)
    logits = outputs.logits.squeeze()

    # (Optional) Apply a final activation function if necessary (e.g., softmax for classification)
    # probabilities = torch.softmax(logits, dim=0)

    # For now, let's just return the raw logits
    return logits

# Example usage
text = "Your example text goes here."
predictions = predict_climate_att(text)
print(predictions)

'''