Update README.md

README.md

@@ -1,3 +1,45 @@
----
-license: apache-2.0
----
+---
+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)
+
+'''