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underway to wordcount

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rnsrk 2023-03-15 13:21:44 +01:00
parent fae306916f
commit 3b677e5713
4 changed files with 24 additions and 18 deletions
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17
SentiTooter.py
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@ -41,15 +41,15 @@ class SentiTooter:
output = self.enModel(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
print(scores)
#print(scores)
sentimentIndexWithMaxScore = np.argmax(scores)
sentimentLabel = self.labels[sentimentIndexWithMaxScore]
sentiment = [sentimentLabel, 'twitter-roberta-base-sentiment', max(scores)]
print(sentiment)
#print(sentiment)
return sentiment
case _:
compound = self.sia.polarity_scores(content)['compound']
print(self.sia.polarity_scores(content), 'vaderSentiment')
#print(self.sia.polarity_scores(content), 'vaderSentiment')
if compound > (1 / 3):
return ['positive', 'vaderSentiment']
elif compound < (-1 / 3):
@ -58,7 +58,6 @@ class SentiTooter:
return ['neutral', 'vaderSentiment']
def initModel(self):
# PT
tokenizer = AutoTokenizer.from_pretrained(self.enModelType)
@ -66,13 +65,3 @@ class SentiTooter:
model = AutoModelForSequenceClassification.from_pretrained(self.enModelType)
model.save_pretrained(self.enModelType)
return model, tokenizer
# # TF
# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
# model.save_pretrained(MODEL)
# text = "Good night 😊"
# encoded_input = tokenizer(text, return_tensors='tf')
# output = model(encoded_input)
# scores = output[0][0].numpy()
# scores = softmax(scores)