new commits

celebbot.py

@@ -12,7 +12,7 @@ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModel
 import pickle
 import streamlit as st
 from sklearn.metrics.pairwise import cosine_similarity
-import run_tts
+
 
 # Build the AI
 class CelebBot():
@@ -55,6 +55,7 @@ class CelebBot():
         return True if "hey " + self.name in text.lower() else False
 
     def text_to_speech(self, autoplay=True):
+        import run_tts
         return run_tts.tts(self.text, "_".join(self.name.split(" ")), self.spacy_model, autoplay)
 
     def sentence_embeds_inference(self, texts: list):

run_eval.py

@@ -85,7 +85,7 @@ def evaluate_system():
     print(f"ROUGE: {round(results['rougeL'], 2)}")
 
     bertscore = evaluate.load("bertscore")
-    results = bertscore.compute(predictions=predictions, references=references, lang="en")
+    results = bertscore.compute(predictions=predictions, references=references, rescale_with_baseline=True, lang="en")
     print(f"F1: {round(sum(results['f1'])/len(results['f1']))}")
     
 if __name__ == "__main__":

test.py

@@ -0,0 +1,5 @@
+import evaluate
+
+bertscore = evaluate.load("bertscore")
+results = bertscore.compute(predictions=["I am from Toronto."], references=["Hey"],rescale_with_baseline=True,  lang="en")
+print(results)