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paragon-analytics
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d415981
1
Parent(s):
d193831
Update app.py
Browse files
app.py
CHANGED
@@ -41,24 +41,10 @@ from transformers_interpret import SequenceClassificationExplainer
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cls_explainer = SequenceClassificationExplainer(
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model,
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tokenizer)
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# load the model from disk
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#filename = 'resil_lstm_model.sav'
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#lmodel = pickle.load(open(filename, 'rb'))
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# load the model from disk
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#filename = 'tokenizer.pickle'
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#tok = pickle.load(open(filename, 'rb'))
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def process_final_text(text):
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X_test = str(text).lower()
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#l.append(X_test)
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#test_sequences = tok.texts_to_sequences(l)
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#test_sequences_matrix = sequence.pad_sequences(test_sequences,maxlen=max_len)
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#lstm_prob = lmodel.predict(test_sequences_matrix.tolist()).flatten()
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#lstm_pred = np.where(lstm_prob>=0.5,1,0)
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encoded_input = tokenizer(X_test, return_tensors='pt')
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output = model(**encoded_input)
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scores = output[0][0].detach().numpy()
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@@ -114,11 +100,6 @@ def process_final_text(text):
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word_attributions = [(letter[i], score[i]) for i in range(0, len(letter))]
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# # Paraphraser:
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# batch = para_tokenizer(X_test, return_tensors='pt')
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# generated_ids = para_model.generate(batch['input_ids'])
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# para_list = para_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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return {"Persuasive": float(scores.numpy()[1]), "Non-Persuasive": float(scores.numpy()[0])},keywords,NER,word_attributions
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def main(prob1):
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cls_explainer = SequenceClassificationExplainer(
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model,
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tokenizer)
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def process_final_text(text):
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X_test = str(text).lower()
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encoded_input = tokenizer(X_test, return_tensors='pt')
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output = model(**encoded_input)
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scores = output[0][0].detach().numpy()
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word_attributions = [(letter[i], score[i]) for i in range(0, len(letter))]
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return {"Persuasive": float(scores.numpy()[1]), "Non-Persuasive": float(scores.numpy()[0])},keywords,NER,word_attributions
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def main(prob1):
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