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	round sentiment score (#119)
* round sentiment score * Update test_text.py increased assert range
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				| @ -94,7 +94,7 @@ def test_text_sentiment_transformers(): | |||||||
|     mydict["text_english"] = "I am happy that the CI is working again." |     mydict["text_english"] = "I am happy that the CI is working again." | ||||||
|     test_obj.text_sentiment_transformers() |     test_obj.text_sentiment_transformers() | ||||||
|     assert mydict["sentiment"] == "POSITIVE" |     assert mydict["sentiment"] == "POSITIVE" | ||||||
|     assert mydict["sentiment_score"] == pytest.approx(0.99, 0.01) |     assert mydict["sentiment_score"] == pytest.approx(0.99, 0.02) | ||||||
| 
 | 
 | ||||||
| 
 | 
 | ||||||
| @pytest.mark.win_skip | @pytest.mark.win_skip | ||||||
|  | |||||||
| @ -133,7 +133,7 @@ class TextDetector(AnalysisMethod): | |||||||
|         ) |         ) | ||||||
|         result = pipe(self.subdict["text_english"]) |         result = pipe(self.subdict["text_english"]) | ||||||
|         self.subdict["sentiment"] = result[0]["label"] |         self.subdict["sentiment"] = result[0]["label"] | ||||||
|         self.subdict["sentiment_score"] = result[0]["score"] |         self.subdict["sentiment_score"] = round(result[0]["score"], 2) | ||||||
| 
 | 
 | ||||||
|     def text_ner(self): |     def text_ner(self): | ||||||
|         """Perform named entity recognition on the text using the Transformers pipeline.""" |         """Perform named entity recognition on the text using the Transformers pipeline.""" | ||||||
|  | |||||||
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