vectorize_fn(text, label): def pyfn(text): text = np.char.decode(text.numpy().astype(np.bytes_), 'UTF-8').tolist() 7 A language model is trained on written texts like blogs, news articles and comments tensorflow way to call python code. def nlpaug_fn(aug): def pyfn(text): text = text.numpy().decode("utf-8") text = aug.augment(text) return text def aug_text_fn(text, label): aug_text = tf.py_function(pyfn

makedirs(folder_path) fp = os.path.join(folder_path, 'load_file.txt') with open(fp, 'a+', encoding='utf-8') as fout: fout.write("filepath=%s,len=%s" % (filepath, len(docs))) fout.write('\n') for i in docs:

data_dir = d2l.download_extract('fra-eng') with open(os.path.join(data_dir, 'fra.txt'), 'r', encoding='utf-8') as f: return f.read() raw_text = read_data_nmt() print(raw_text[:75]) Downloading ../data/fra-eng listdir(folder_name): with open(os.path.join(folder_name, file), 'rb') as f: review = f.read().decode('utf-8').replace('\n', '') data.append(review) labels.append(1 if label == 'pos' else 0) return data,