Update README.md

README.md

@@ -6,6 +6,28 @@ The obtained macro f1-score is 0.54, on the development set of the competition.
 This model is trained to classify the given text into one of the following classes: *moderate*, *severe*, or *not depressed*.
 It corresponds to a **multiclass classification** task.
 
+# How to use
+
+You can use this model directly with a pipeline for text classification:
+
+```python
+>>> from transformers import pipeline
+>>> classifier = pipeline("text-classification", model="paulagarciaserrano/roberta-depression-detection")
+>>> classifier ("I am very sad.")
+```
+
+Here is how to use this model with PyTorch:
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+model_checkpoint = "paulagarciaserrano/roberta-depression-detection"
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=3)
+text = "I am very sad."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```
+
 # Training and evaluation data
 The **train** dataset characteristics are: