文章起源 | 恒源云社区(恒源云，专一 AI 行业的共享算力平台)
原文地址 | 文本数据加强
原文作者 | 角灰
最近在做新闻标题分类,找了篇数据加强的文章学习学习:
一篇就够！数据加强办法综述
本文实现了EDA(简略数据加强)和回译:
一. EDA
1.1 随机替换
import randomimport jiebaimport numpy as npimport paddlefrom paddlenlp.embeddings import TokenEmbedding# 从词向量中按余弦类似度找与某个词的topk近义词def get_similar_tokens_raw(query_token, k, token_embedding): W = np.asarray(token_embedding.weight.numpy()) x = np.asarray(token_embedding.search(query_token).reshape(-1)) cos = np.dot(W, x) / np.sqrt(np.sum(W * W, axis=1) * np.sum(x * x) + 1e-9) flat = cos.flatten() # argpartition在k个地位放第k大的索引，右边比他小，左边比他大,复杂度仅o(n) # 取-k则在-k和他左边的为topk,对他们再排秩序就好了 indices = np.argpartition(flat, -k)[-k:] indices = indices[np.argsort(-flat[indices])] # 取负从大到小排 return token_embedding.vocab.to_tokens(indices)# 随机替换def random_replace(words,token_embedding,prob=0.1,max_change=3): change_num=0 for idx in range(len(words)): prob_i=prob*(len(words[idx])-0.5) # -0.5使得长度1的词概率乘2,不易选中 if random.uniform(0,1)<prob_i: # 词越长，越容易被替换 sim_words=get_similar_tokens_raw(words[idx],k=5,token_embedding=token_embedding) words[idx]=random.choice(sim_words) change_num+=1 if change_num>=max_change: break return words因为get_similar_tokens_raw一次只能取一个词的近义词较慢,于是改成了一次取多个词的近义词,成果如下:
# 查问多个词的topk近义词def get_similar_tokens_multi(query_tokens, k, token_embedding): n_tokens=len(query_tokens) W = paddle.to_tensor(token_embedding.weight.detach(),dtype='float16') q_idx=token_embedding.search(query_tokens) x = paddle.to_tensor(q_idx,dtype='float16').transpose((1,0)) cos = paddle.matmul(W, x) / paddle.sqrt(paddle.sum(W * W, axis=1,keepdim=True) * paddle.sum(x * x,keepdim=True) + 1e-9) def sort_row_by_idx(input, indices): assert input.shape == indices.shape row, col = input.shape indices = indices * col + np.arange(0, col) indices = indices.reshape(-1) input = input.reshape(-1)[indices].reshape(row, -1) return input part_indices = np.argpartition(cos.numpy(), -k, axis=0) out = sort_row_by_idx(cos.numpy(), part_indices)[-k:, :] new_idx = np.argsort(-out, axis=0) # 用新的索引对旧的part的索引排序 indices = sort_row_by_idx(part_indices[-k:, :], new_idx).reshape(-1) sim_tokens=token_embedding.vocab.to_tokens(indices) sim_tokens=np.array(sim_tokens).reshape(k,n_tokens) if k>=2:sim_tokens=sim_tokens[:-1,:] return sim_tokens.transpose()# 相应的随机替换(此函数会多返回个近义词列表,供随机插入应用)def random_replace(words,token_embedding,prob=0.1,max_change=3): words=np.array(words) probs=np.random.uniform(0,1,(len(words),)) words_len=np.array([len(word) for word in words])-0.5 # 惩办1的 probs=probs/words_len mask=probs<prob if sum(mask)>1: replace_words=words[mask].tolist() sim_words=get_similar_tokens_multi(query_tokens=replace_words,k=5,token_embedding=token_embedding) choosed=[] for row in sim_words: choosed.append(np.random.choice(row)) words[mask]=np.array(choosed) return words.tolist(),sim_words.flatten().tolist() return words.tolist(),[]if __name__ == '__main__': token_embedding=TokenEmbedding(embedding_name="w2v.baidu_encyclopedia.target.word-word.dim300") # 近义词查找 words=['苹果','美国','国王','总统','台风','雷电','奥特曼'] sim_words=get_similar_tokens_multi(query_tokens=words,k=5,token_embedding=token_embedding) print('raw words:',words) print('sim_words:',sim_words)1.2 随机插入
随机在语句中插入n个词 (从随机替换返回的近义词列表sim_words采样,如果sim_words=None,则从原句中随机采样)
def random_insertion(words,sim_words=None,n=3): new_words = words.copy() for _ in range(n): add_word(new_words,sim_words) return new_wordsdef add_word(new_words,sim_words=None): random_synonym = random.choice(sim_words) if sim_words else random.choice(new_words) random_idx = random.randint(0, len(new_words) - 1) new_words.insert(random_idx, random_synonym) # 随机插入1.3 随机删除
对句子中每个词依概率p随机删除,此处按词长度加权,越长越不易被删除,代码如下:
def random_deletion(words,prob=0.1): probs=np.random.uniform(0,1,(len(words),)) words_len=np.array([len(word) for word in words]) # 对长词加大权重，避免被删除重要词 probs=probs*words_len mask=probs>prob return np.array(words)[mask].tolist()1.4 随机置换邻近词
人在读阅句子时,往往乱打程序也能理句解意,不信您回过来再读一遍哈哈,代码如下:
# 先获取词索引,再对某个词增加个噪声noise∈[0,n],n(window_size)个别取3,而后# 从新排序后就能达到目标了def random_permute(words,window_size): noise=np.random.uniform(0,window_size,size=(len(words),)) idx=np.arange(0,len(words)) new_idx=np.argsort(noise+idx) return np.array(words)[new_idx].tolist()二. 回译
回译是机器翻译里罕用的对单语语料进行加强办法:对指标端单语语料t,利用反向翻译模型(tgt2src)生成源端的伪数据s’,从而让正向的src2tgt翻译模型应用伪平行语料(s’,t)持续训练。
本文应用预训练的mbart50(50种语言)进行回译,能够对原始语料zh,进行如下方向翻译:
中->法->xxxx->英->中,简略起见本文就进行中英中回译:
回译示例:
import torchfrom transformers import MBartForConditionalGeneration,MBart50TokenizerFastdevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")model.eval()batch_sentences=['网易第三季度业绩低于分析师预期', '巴萨1年前天堂重现这次却是地狱 再赴魔鬼客场必翻盘', '美国称反对向朝鲜提供紧急人道主义支援', '蔡少芬要补交税款几十万 圣诞节拼命赚外快(图)']print('input:','\n'.join(batch_sentences))# 中->英tokenizer.src_lang='zh_CN' # 设置输出为中文batch_tokenized = tokenizer.batch_encode_plus(batch_sentences, add_special_tokens=True,padding=True, pad_to_max_length=True)input_dict = {'input_ids':torch.LongTensor(batch_tokenized['input_ids']).to(device), "attention_mask":torch.LongTensor(batch_tokenized['attention_mask']).to(device)}batch_tokens=model.generate(**input_dict,forced_bos_token_id=tokenizer.lang_code_to_id['en_XX']) # 输入为英文en_sent=tokenizer.batch_decode(batch_tokens, skip_special_tokens=True)print('en:','\n'.join(en_sent))# 英->中tokenizer.src_lang='en_XX' # 设置输出为英文batch_tokenized = tokenizer.batch_encode_plus(en_sent, add_special_tokens=True,padding=True, pad_to_max_length=True)input_dict = {'input_ids':torch.LongTensor(batch_tokenized['input_ids']).to(device), "attention_mask":torch.LongTensor(batch_tokenized['attention_mask']).to(device)}batch_tokens=model.generate(**input_dict,forced_bos_token_id=tokenizer.lang_code_to_id['zh_CN']) # 输入为中文zh_sent=tokenizer.batch_decode(batch_tokens, skip_special_tokens=True)print('zh:','\n'.join(zh_sent))'''mbart50笼罩如下语言:Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI'''# 离线回译加强,将文本文件按行回译,import torchfrom functools import partialfrom transformers import MBartForConditionalGeneration,MBart50TokenizerFastfrom tqdm import tqdmdef get_data_iterator(input_path): with open(input_path, 'r', encoding="utf-8") as f: for line in f.readlines(): line=line.strip() yield line# 迭代器: 生成一个batch的数据def get_batch_iterator(data_path, batch_size=32,drop_last=False): keras_bert_iter = get_data_iterator(data_path) continue_iterator = True while True: batch_data = [] for _ in range(batch_size): try: data = next(keras_bert_iter) batch_data.append(data) except StopIteration: continue_iterator = False break if continue_iterator:# 刚好一个batch yield batch_data else: # 有余一batch if not drop_last: yield batch_data return StopIteration@torch.no_grad()def batch_translation(batch_sentences,model,tokenizer,src_lang,tgt_lang,max_len=128): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) model.eval() tokenizer.src_lang=src_lang # token2id encoded_inputs=tokenizer.batch_encode_plus(batch_sentences, add_special_tokens=True, padding=True, pad_to_max_length=True)# max_length=max_len, pad_to_max_length=True) # list->tensor encoded_inputs['input_ids']=torch.LongTensor(encoded_inputs['input_ids']).to(device) encoded_inputs['attention_mask']=torch.LongTensor(encoded_inputs['attention_mask']).to(device) # generate batch_tokens = model.generate(**encoded_inputs, forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang]) # decode tgt_sentences = tokenizer.batch_decode(batch_tokens, skip_special_tokens=True) return tgt_sentencesdef translate_file(src_path,tgt_path,src_lang,tgt_lang,batch_size=32,max_len=128): # data batch_iter=get_batch_iterator(src_path,batch_size=batch_size) # model model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt") tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt") src2tgt_fn = partial(batch_translation, model=model, tokenizer=tokenizer, src_lang=src_lang, tgt_lang=tgt_lang,max_len=None) result=[] i=0 for batch_sentences in tqdm(batch_iter): tgt_sentences = src2tgt_fn(batch_sentences) result.extend(tgt_sentences) if i%100==0: print(f'src:{batch_sentences[0]}==>tgt:{tgt_sentences[0]}') i+=1 # write 2 file with open(tgt_path,'w',encoding='utf-8') as f: f.write('\n'.join(result)) print(f'write 2 {tgt_path} success.')if __name__ == '__main__': src_path='train.txt' mid_path='train.en' tgt_path='train_back.txt' # translate zh to en translate_file(src_path, mid_path, src_lang='zh_CN', tgt_lang='en_XX', batch_size=16) # translate en to zh translate_file(mid_path, tgt_path, src_lang='en_XX', tgt_lang='zh_CN', batch_size=16)总结:
数据加强作用无限,接下来筹备在相干工作数据上持续预训练。
参考:
1.一篇就够！数据加强办法综述
2.回译
3.mbart50
4.机器翻译:根底和模型