Making predictions with a SequentialRNN model (Fast.ai)

predictions.py

# Note: ensure you have the latest version of Torchtext by running: pip install torchtext --upgrade

rnn_model = text_data.get_model(opt_fn, 1500, bptt, emb_sz=em_sz, n_hid=nh, n_layers=nl, 
           dropout=0.1, dropouti=0.65, wdrop=0.5, dropoute=0.1, dropouth=0.3)

# ...

rnn_model.data.test_dl.src.sort = False
rnn_model.data.test_dl.src.sort_within_batch = False
rnn_model.data.test_dl.src.shuffle = False

probs = rnn_model.predict(is_test=True)
preds = np.argmax(probs, axis=1)

pd.DataFrame({
    'id': test_df['index'],
    'sentiment': [LABEL_FIELD.vocab.itos[p] for p in preds]}).to_csv('./sub1.csv', index=False)
    
FileLink('./sub1.csv')