NLP——Task5|NLP——Task5 基于深度学习的文本分类2-1Word2Vec NLP——Task5基于深度学习的文本

import logging import randomimport numpy as np import torchlogging.basicConfig(level=logging.INFO, format='%(asctime)-15s %(levelname)s: %(message)s')# set seed seed = 666 random.seed(seed) np.random.seed(seed) torch.cuda.manual_seed(seed) torch.manual_seed(seed)

# split data to 10 fold fold_num = 10 data_file = '../data/train_set.csv' import pandas as pddef all_data2fold(fold_num, num=10000): fold_data = https://www.it610.com/article/[] f = pd.read_csv(data_file, sep='\t', encoding='UTF-8') texts = f['text'].tolist()[:num] labels = f['label'].tolist()[:num]total = len(labels)index = list(range(total)) np.random.shuffle(index)all_texts = [] all_labels = [] for i in index: all_texts.append(texts[i]) all_labels.append(labels[i])label2id = {} for i in range(total): label = str(all_labels[i]) if label not in label2id: label2id[label] = [i] else: label2id[label].append(i)all_index = [[] for _ in range(fold_num)] for label, data in label2id.items(): # print(label, len(data)) batch_size = int(len(data) / fold_num) other = len(data) - batch_size * fold_num for i in range(fold_num): cur_batch_size = batch_size + 1 if i < other else batch_size # print(cur_batch_size) batch_data = https://www.it610.com/article/[data[i * batch_size + b] for b in range(cur_batch_size)] all_index[i].extend(batch_data)batch_size = int(total / fold_num) other_texts = [] other_labels = [] other_num = 0 start = 0 for fold in range(fold_num): num = len(all_index[fold]) texts = [all_texts[i] for i in all_index[fold]] labels = [all_labels[i] for i in all_index[fold]]if num> batch_size: fold_texts = texts[:batch_size] other_texts.extend(texts[batch_size:]) fold_labels = labels[:batch_size] other_labels.extend(labels[batch_size:]) other_num += num - batch_size elif num < batch_size: end = start + batch_size - num fold_texts = texts + other_texts[start: end] fold_labels = labels + other_labels[start: end] start = end else: fold_texts = texts fold_labels = labelsassert batch_size == len(fold_labels)# shuffle index = list(range(batch_size)) np.random.shuffle(index)shuffle_fold_texts = [] shuffle_fold_labels = [] for i in index: shuffle_fold_texts.append(fold_texts[i]) shuffle_fold_labels.append(fold_labels[i])data = https://www.it610.com/article/{'label': shuffle_fold_labels, 'text': shuffle_fold_texts} fold_data.append(data)logging.info("Fold lens %s", str([len(data['label']) for data in fold_data]))return fold_datafold_data = https://www.it610.com/article/all_data2fold(10)

2020-07-18 23:30:04,912 INFO: Fold lens [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]

# build train data for word2vec fold_id = 9train_texts = [] for i in range(0, fold_id): data = https://www.it610.com/article/fold_data[i] train_texts.extend(data['text'])logging.info('Total %d docs.' % len(train_texts))

2020-07-18 23:30:04,929 INFO: Total 9000 docs.

logging.info('Start training...') from gensim.models.word2vec import Word2Vecnum_features = 100# Word vector dimensionality num_workers = 8# Number of threads to run in paralleltrain_texts = list(map(lambda x: list(x.split()), train_texts)) model = Word2Vec(train_texts, workers=num_workers, size=num_features) model.init_sims(replace=True)# save model model.save("./word2vec.bin")

结果得：
2020-07-18 23:30:04,938 INFO: Start training…
2020-07-18 23:30:05,545 INFO: collecting all words and their counts
2020-07-18 23:30:05,546 INFO: PROGRESS: at sentence #0, processed 0 words, keeping 0 word types
2020-07-18 23:30:06,231 INFO: collected 5295 word types from a corpus of 8191447 raw words and 9000 sentences
2020-07-18 23:30:06,231 INFO: Loading a fresh vocabulary
2020-07-18 23:30:06,305 INFO: effective_min_count=5 retains 4335 unique words (81% of original 5295, drops 960)
2020-07-18 23:30:06,305 INFO: effective_min_count=5 leaves 8189498 word corpus (99% of original 8191447, drops 1949)
2020-07-18 23:30:06,314 INFO: deleting the raw counts dictionary of 5295 items
2020-07-18 23:30:06,316 INFO: sample=0.001 downsamples 61 most-common words
2020-07-18 23:30:06,316 INFO: downsampling leaves estimated 7070438 word corpus (86.3% of prior 8189498)
2020-07-18 23:30:06,324 INFO: estimated required memory for 4335 words and 100 dimensions: 5635500 bytes
2020-07-18 23:30:06,325 INFO: resetting layer weights
2020-07-18 23:30:06,356 INFO: training model with 8 workers on 4335 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=5 window=5
2020-07-18 23:30:07,360 INFO: EPOCH 1 - PROGRESS: at 63.20% examples, 4434412 words/s, in_qsize 15, out_qsize 0
2020-07-18 23:30:07,959 INFO: worker thread finished; awaiting finish of 7 more threads
2020-07-18 23:30:07,960 INFO: worker thread finished; awaiting finish of 6 more threads
2020-07-18 23:30:07,963 INFO: worker thread finished; awaiting finish of 5 more threads
2020-07-18 23:30:07,963 INFO: worker thread finished; awaiting finish of 4 more threads
2020-07-18 23:30:07,964 INFO: worker thread finished; awaiting finish of 3 more threads
2020-07-18 23:30:07,965 INFO: worker thread finished; awaiting finish of 2 more threads
2020-07-18 23:30:07,968 INFO: worker thread finished; awaiting finish of 1 more threads
2020-07-18 23:30:07,969 INFO: worker thread finished; awaiting finish of 0 more threads
2020-07-18 23:30:07,970 INFO: EPOCH - 1 : training on 8191447 raw words (7021120 effective words) took 1.6s, 4357567 effective words/s
2020-07-18 23:30:08,979 INFO: EPOCH 2 - PROGRESS: at 59.39% examples, 4143643 words/s, in_qsize 15, out_qsize 0
2020-07-18 23:30:09,661 INFO: worker thread finished; awaiting finish of 7 more threads
2020-07-18 23:30:09,663 INFO: worker thread finished; awaiting finish of 6 more threads
2020-07-18 23:30:09,663 INFO: worker thread finished; awaiting finish of 5 more threads
2020-07-18 23:30:09,664 INFO: worker thread finished; awaiting finish of 4 more threads
2020-07-18 23:30:09,667 INFO: worker thread finished; awaiting finish of 3 more threads
2020-07-18 23:30:09,667 INFO: worker thread finished; awaiting finish of 2 more threads
2020-07-18 23:30:09,670 INFO: worker thread finished; awaiting finish of 1 more threads
2020-07-18 23:30:09,672 INFO: worker thread finished; awaiting finish of 0 more threads
2020-07-18 23:30:09,672 INFO: EPOCH - 2 : training on 8191447 raw words (7021506 effective words) took 1.7s, 4144060 effective words/s
2020-07-18 23:30:10,681 INFO: EPOCH 3 - PROGRESS: at 59.52% examples, 4154672 words/s, in_qsize 15, out_qsize 0
2020-07-18 23:30:11,356 INFO: worker thread finished; awaiting finish of 7 more threads
2020-07-18 23:30:11,356 INFO: worker thread finished; awaiting finish of 6 more threads
2020-07-18 23:30:11,358 INFO: worker thread finished; awaiting finish of 5 more threads
2020-07-18 23:30:11,359 INFO: worker thread finished; awaiting finish of 4 more threads
2020-07-18 23:30:11,362 INFO: worker thread finished; awaiting finish of 3 more threads
2020-07-18 23:30:11,362 INFO: worker thread finished; awaiting finish of 2 more threads
2020-07-18 23:30:11,365 INFO: worker thread finished; awaiting finish of 1 more threads
2020-07-18 23:30:11,366 INFO: worker thread finished; awaiting finish of 0 more threads
2020-07-18 23:30:11,367 INFO: EPOCH - 3 : training on 8191447 raw words (7020706 effective words) took 1.7s, 4163417 effective words/s
2020-07-18 23:30:12,378 INFO: EPOCH 4 - PROGRESS: at 58.80% examples, 4102329 words/s, in_qsize 15, out_qsize 0
2020-07-18 23:30:13,072 INFO: worker thread finished; awaiting finish of 7 more threads
2020-07-18 23:30:13,078 INFO: worker thread finished; awaiting finish of 6 more threads
2020-07-18 23:30:13,079 INFO: worker thread finished; awaiting finish of 5 more threads
2020-07-18 23:30:13,079 INFO: worker thread finished; awaiting finish of 4 more threads
2020-07-18 23:30:13,080 INFO: worker thread finished; awaiting finish of 3 more threads
2020-07-18 23:30:13,080 INFO: worker thread finished; awaiting finish of 2 more threads
2020-07-18 23:30:13,081 INFO: worker thread finished; awaiting finish of 1 more threads
2020-07-18 23:30:13,082 INFO: worker thread finished; awaiting finish of 0 more threads
2020-07-18 23:30:13,083 INFO: EPOCH - 4 : training on 8191447 raw words (7021984 effective words) took 1.7s, 4117851 effective words/s
2020-07-18 23:30:14,091 INFO: EPOCH 5 - PROGRESS: at 58.99% examples, 4115963 words/s, in_qsize 16, out_qsize 0
2020-07-18 23:30:14,769 INFO: worker thread finished; awaiting finish of 7 more threads
2020-07-18 23:30:14,770 INFO: worker thread finished; awaiting finish of 6 more threads
2020-07-18 23:30:14,770 INFO: worker thread finished; awaiting finish of 5 more threads
2020-07-18 23:30:14,771 INFO: worker thread finished; awaiting finish of 4 more threads
2020-07-18 23:30:14,773 INFO: worker thread finished; awaiting finish of 3 more threads
2020-07-18 23:30:14,776 INFO: worker thread finished; awaiting finish of 2 more threads
2020-07-18 23:30:14,777 INFO: worker thread finished; awaiting finish of 1 more threads
2020-07-18 23:30:14,779 INFO: worker thread finished; awaiting finish of 0 more threads
2020-07-18 23:30:14,779 INFO: EPOCH - 5 : training on 8191447 raw words (7021532 effective words) took 1.7s, 4156171 effective words/s
2020-07-18 23:30:14,780 INFO: training on a 40957235 raw words (35106848 effective words) took 8.4s, 4167675 effective words/s
2020-07-18 23:30:14,780 INFO: precomputing L2-norms of word weight vectors
2020-07-18 23:30:14,782 INFO: saving Word2Vec object under ./word2vec.bin, separately None
2020-07-18 23:30:14,783 INFO: not storing attribute vectors_norm
2020-07-18 23:30:14,783 INFO: not storing attribute cum_table
2020-07-18 23:30:14,820 INFO: saved ./word2vec.bin

# load model model = Word2Vec.load("./word2vec.bin")# convert format model.wv.save_word2vec_format('./word2vec.txt', binary=False)

结果得：
2020-07-18 23:30:14,825 INFO: loading Word2Vec object from ./word2vec.bin
2020-07-18 23:30:14,958 INFO: loading wv recursively from ./word2vec.bin.wv.* with mmap=None
2020-07-18 23:30:14,958 INFO: setting ignored attribute vectors_norm to None
2020-07-18 23:30:14,959 INFO: loading vocabulary recursively from ./word2vec.bin.vocabulary.* with mmap=None
2020-07-18 23:30:14,959 INFO: loading trainables recursively from ./word2vec.bin.trainables.* with mmap=None
2020-07-18 23:30:14,959 INFO: setting ignored attribute cum_table to None
2020-07-18 23:30:14,959 INFO: loaded ./word2vec.bin
【NLP——Task5|NLP——Task5 基于深度学习的文本分类2-1Word2Vec】2020-07-18 23:30:14,965 INFO: storing 4335x100 projection weights into ./word2vec.txt