fix pipelines
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@ -1,10 +1,6 @@
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from transformers import AutoTokenizer
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from transformers import pipeline
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#from transformers import BartForConditionalGeneration
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tokenizer = AutoTokenizer.from_pretrained("szymonj/polish-simple-error-correction")
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#model = BartForConditionalGeneration.from_pretrained("szymonj/polish-simple-error-correction")
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pipe = pipeline("text2text-generation",model="szymonj/polish-simple-error-correction",tokenizer=tokenizer,max_length=2000)
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pipe = pipeline("text2text-generation",model="szymonj/polish-simple-error-correction", tokenizer="szymonj/polish-simple-error-correction", max_length=2000)
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def errors_correction(data):
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result = pipe(data)
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@ -1,17 +1,12 @@
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from transformers import AutoTokenizer
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from transformers import pipeline, GPT2ForSequenceClassification
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from transformers import pipeline
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import re
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from facebook_scraper import get_posts
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# model = 'application/models/sentiment_model'
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# tokenizer = AutoTokenizer.from_pretrained('application/tokenizers/sentiment_tokenizer')
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#model = GPT2ForSequenceClassification.from_pretrained("Scigi/sentiment-analysis-model", num_labels=3)
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tokenizer = AutoTokenizer.from_pretrained("Scigi/sentiment-analysis-model")
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pipe = pipeline('text-classification', model="Scigi/sentiment-analysis-model", tokenizer = tokenizer)
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pipe = pipeline('text-classification', model="Scigi/sentiment-analysis-model", tokenizer = "Scigi/sentiment-analysis-model")
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def sentiment_prediction(data):
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result = pipe(data)
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return result
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def clear_data(data):
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@ -19,6 +14,7 @@ def clear_data(data):
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data = [x for x in data if x != '']
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data = [i.strip() for i in data]
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data = [i.lower() for i in data]
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return data
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def count_predictions(predictions):
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@ -50,4 +46,5 @@ def scrapp_comments(url):
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comments.append(comment['comment_text'])
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all['post'] = text_post
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all['sentences'] = comments
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return all
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