Update README.md

README.md

@@ -37,7 +37,7 @@ It achieves Unweighed Average Recall (UAR) values of .8001 and .8084 on the deve
 - **Paper [optional]:** [More Information Needed]
 
 
-## Uses
+## Usage
 
 The following snippet illustrates the usage of the model. 
 ```python
@@ -49,13 +49,15 @@ import librosa
 checkpoint = "chrlukas/flattery_prediction_speech"
 processor = AutoFeatureExtractor.from_pretrained(checkpoint)
 model = Wav2Vec2ForSequenceClassification.from_pretrained(checkpoint)
+model.eval()
 
 # predict flattery in a sentence
 example_file = 'example.wav'
 # audio must be resampled to 16Hz
 y, _ = librosa.load(test_file, sr=16000)
 inp = processor(y, sampling_rate=16000, return_tensors='pt')
-logits = model(**inp).logits
+with torch.no_grad():
+  logits = model(**inp).logits
 prediction = sigmoid(logits).item()
 flattery = prediction >= 0.5
 print(f'Flattery detected? {flattery}')