Update README.md

README.md

@@ -37,6 +37,17 @@ should probably proofread and complete it, then remove this comment. -->
 
 # depression-reddit-distilroberta-base
 
+## Example Pipeline
+
+```python
+from transformers import pipeline
+predict_task = pipeline(model="mrjunos/depression-reddit-distilroberta-base", task="text-classification")
+predict_task("Stop listing your issues here, use forum instead or open ticket.")
+```
+```
+[{'label': 'not_depression', 'score': 0.9813856482505798}]
+```
+
 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the mrjunos/depression-reddit-cleaned dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.0821
@@ -55,7 +66,10 @@ The model has been trained on a dataset of English-language posts, so it may not
 ## Training and evaluation data
 
 The model was trained on the mrjunos/depression-reddit-cleaned dataset, which contains approximately 7,000 labeled instances.
-The data was split into Train and Test using train_test_split(test_size=0.2, seed=42)
+The data was split into Train and Test using:
+```python
+ds = ds['train'].train_test_split(test_size=0.2, seed=42)
+```
 The dataset consists of two main features: 'text' and 'label'. The 'text' feature contains the text data from Reddit posts related to depression, while the 'label' feature indicates whether a post is classified as depression or not.
 
 ## Training procedure