Update README.md

README.md

@@ -71,6 +71,35 @@ with torch.no_grad():
 
 ```
 
+An approach could be 
+
+```python
+
+def get_feature_vector(text):
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+    )
+
+    with torch.no_grad():
+        features = model(**inputs).logits
+
+    return features.flatten()
+
+def reform(features, how='softmax'):
+    if how == 'softmax':
+        return torch.nn.functional.softmax(features)
+    elif how = 'gumbel':
+        return torch.nn.functional.gumbel_softmax(features)
+    elif how = 'normalize':
+        return torch.nn.functional.normalize(features.reshape(1,-1))
+
+feature_vector = reform(get_feature_vector(text))
+
+
+```
+
 
 
 The macro f1 score is 0.73. We suggest to use it primarily for feature extraction or for use as an *explainable* embedder.