Add Example Usage

README.md

@@ -17,6 +17,35 @@ The model is ideal for:
 + Research in financial sustainability and climate economics.
 + Tracking how banks articulate their climate-related activities.
 
+## Example Usage
+Here is an example of how to use the climateBUG-LM model for classifying text as climate-related or not:
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+
+tokenizer_name = "lumilogic/climateBUG-LM"
+model_name = "lumilogic/climateBUG-LM"
+
+
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, max_len=512)
+
+pipe = pipeline("text-classification", model=model,
+                tokenizer=tokenizer, device_map='auto')
+
+
+# Climate related text
+text = 'In one case, it was decided not to pursue commercial opportunities based on its recommendation, while in other cases, specific conditions for managing environmental and social risks were imposed.'
+output = pipe(text)
+print(output)  # [{'label': 'LABEL_1', 'score': 0.9939790964126587}]
+
+
+# Non-climate related text
+text = 'Major updates were brought to this software in order to optimise the management and monitoring of expenses under the agreement in 2015.'
+output = pipe(text)
+print(output)  # [{'label': 'LABEL_0', 'score': 0.9936821460723877}]
+```
+
 ## Limitations
 + Optimized for EU bank reports; performance may vary for other regions.
 + Primarily focused on climate and finance domains.