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Parent(s):
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progress more 24
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app.py
CHANGED
@@ -20,7 +20,9 @@ mystem = Mystem()
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finbert = pipeline("sentiment-analysis", model="ProsusAI/finbert")
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roberta = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
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finbert_tone = pipeline("sentiment-analysis", model="yiyanghkust/finbert-tone")
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# Function for lemmatizing Russian text
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def lemmatize_text(text):
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@@ -69,10 +71,13 @@ def get_mapped_sentiment(result):
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return "Negative"
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return "Neutral"
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def
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result =
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return get_mapped_sentiment(result)
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def get_finbert_sentiment(text):
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result = finbert(text, truncation=True, max_length=512)[0]
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@@ -133,26 +138,28 @@ def process_file(uploaded_file):
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progress_text.text(f"{i + 1} из {total_news} сообщений переведено")
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# Perform sentiment analysis
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finbert_results = [get_finbert_sentiment(text) for text in translated_texts]
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roberta_results = [get_roberta_sentiment(text) for text in translated_texts]
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finbert_tone_results = [get_finbert_tone_sentiment(text) for text in translated_texts]
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# Add results to DataFrame
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df['
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df['FinBERT'] = finbert_results
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df['RoBERTa'] = roberta_results
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df['FinBERT-Tone'] = finbert_tone_results
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df['Translated'] = translated_texts
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# Reorder columns
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columns_order = ['Объект', '
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df = df[columns_order]
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return df
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def main():
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st.title("... приступим к анализу... версия
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uploaded_file = st.file_uploader("Выбирайте Excel-файл", type="xlsx")
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finbert = pipeline("sentiment-analysis", model="ProsusAI/finbert")
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roberta = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
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finbert_tone = pipeline("sentiment-analysis", model="yiyanghkust/finbert-tone")
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rubert1 = pipeline("sentiment-analysis", model = "DeepPavlov/rubert-base-cased")
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rubert2 = pipeline("sentiment-analysis", model = "blanchefort/rubert-base-cased-sentiment")
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# Function for lemmatizing Russian text
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def lemmatize_text(text):
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return "Negative"
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return "Neutral"
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def get_rubert1_sentiment(text):
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result = rubert(text, truncation=True, max_length=512)[0]
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return get_mapped_sentiment(result)
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def get_rubert2_sentiment(text):
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result = rubert(text, truncation=True, max_length=512)[0]
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return get_mapped_sentiment(result)
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def get_finbert_sentiment(text):
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result = finbert(text, truncation=True, max_length=512)[0]
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progress_text.text(f"{i + 1} из {total_news} сообщений переведено")
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# Perform sentiment analysis
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rubert1_results = [get_rubert1_sentiment(text) for text in texts]
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rubert2_results = [get_rubert2_sentiment(text) for text in texts]
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finbert_results = [get_finbert_sentiment(text) for text in translated_texts]
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roberta_results = [get_roberta_sentiment(text) for text in translated_texts]
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finbert_tone_results = [get_finbert_tone_sentiment(text) for text in translated_texts]
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# Add results to DataFrame
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df['ruBERT1'] = rubert1_results
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df['ruBERT2'] = rubert2_results
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df['FinBERT'] = finbert_results
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df['RoBERTa'] = roberta_results
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df['FinBERT-Tone'] = finbert_tone_results
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df['Translated'] = translated_texts
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# Reorder columns
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columns_order = ['Объект', 'ruBERT1', 'ruBERT2','FinBERT', 'RoBERTa', 'FinBERT-Tone', 'Выдержки из текста', 'Translated' ]
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df = df[columns_order]
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return df
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def main():
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st.title("... приступим к анализу... версия 24")
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uploaded_file = st.file_uploader("Выбирайте Excel-файл", type="xlsx")
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