<jupyter_start><jupyter_text>Testing<jupyter_code>testdf = pd.read_csv("../data/test.tsv", sep="	")
testdf.head()
testdf['Phrase'] = testdf['Phrase'].apply(lambda x: x.lower())
testdf['Phrase'] = testdf['Phrase'].apply((lambda x: re.sub('[^A-z\s]','',x)))
testdf['clean_test'] = clean_text(testdf.Phrase.values)
test_sequences = tokenizer.texts_to_sequences(testdf['clean_test'].values)
test_data = pad_sequences(test_sequences, maxlen=48)
y_pred = model.predict_classes(test_data, verbose=1)
subdf = pd.DataFrame({'PhraseId': testdf['PhraseId'], 'Sentiment': y_pred})
subdf.head()
subdf.to_csv("../data/sampleSubmission.csv", index=False)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.20029895366218237, 'fraction_numerical': 0.004484304932735426, 'mean_word_length': 9.307692307692308, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '557190', 'license': 'MIT', 'n_tokens_mistral': 273, 'n_tokens_neox': 257, 'n_words': 30, 'path': 'code/Movie Review Sentiment Analysis - EDA.ipynb', 'repo_name': 'Subh1m/Kaggle-Movie-Review-Sentiment-Analysis', 'size': '669'}