Update README.md

README.md

@@ -58,6 +58,9 @@ import numpy as np  # only used for the postprocessing sigmoid
 
 sentences = ["hello world"]  # for example a batch of 1
 
+# labels as (ordered) list - from the go_emotions dataset
+labels = ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral']
+
 tokenizer = Tokenizer.from_pretrained("SamLowe/roberta-base-go_emotions")
 
 # optional - set pad to only pad to longest in batch, not a fixed length. Without this, the model will run slower, esp for shorter input strings.
@@ -83,10 +86,14 @@ input_feed_dict = {
 def sigmoid(_outputs):
   return 1.0 / (1.0 + np.exp(-_outputs))
 
-model_output = model.run(output_names=output_names, input_feed=input_feed_dict)[0]
+logits = model.run(output_names=output_names, input_feed=input_feed_dict)[0]
+
+model_outputs = sigmoid(logits)  # produces a numpy array, one row per input item, one col per label
 
-embeddings = sigmoid(model_output)
-print(embeddings)
+# for example, just to show the top result per input item
+for probas in model_outputs:
+  top_result_index = np.argmax(probas)
+  print(labels[top_result_index], "with score:", probas[top_result_index])
 ```
 
 ### Example notebook: showing usage, accuracy & performance