使用🤗Transformers进行NLP的数据增广的4种常用方法

 from transformers import pipeline
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 #English to German using the Pipeline and T5
 translator_en_to_de = pipeline("translation_en_to_de", model='t5-base')
 
 #Germal to English using Bert2Bert model
 tokenizer = AutoTokenizer.from_pretrained("google/bert2bert_L-24_wmt_de_en", pad_token="<pad>", eos_token="</s>", bos_token="<s>")
 model_de_to_en = AutoModelForSeq2SeqLM.from_pretrained("google/bert2bert_L-24_wmt_de_en")
 
 input_text = "I went to see a movie in the theater"
 en_to_de_output = translator_en_to_de(input_text)
 translated_text = en_to_de_output[0]['translation_text']
 print("Translated text->",translated_text)
 #Ich ging ins Kino, um einen Film zu sehen.
 
 input_ids = tokenizer(translated_text, return_tensors="pt", add_special_tokens=False).input_ids
 output_ids = model_de_to_en.generate(input_ids)[0]
 augmented_text = tokenizer.decode(output_ids, skip_special_tokens=True)
 print("Augmented Text->",augmented_text)
 #I went to the cinema to see a film.

 from transformers import pipeline
 import random
 
 unmasker = pipeline('fill-mask', model='bert-base-cased')
 
 input_text = "I went to see a movie in the theater"
 
 orig_text_list = input_text.split()
 len_input = len(orig_text_list)
 #Random index where we want to insert the word except at the start or end
 rand_idx = random.randint(1,len_input-2)
 
 new_text_list = orig_text_list[:rand_idx] + ['[MASK]'] + orig_text_list[rand_idx:]
 new_mask_sent = ' '.join(new_text_list)
 print("Masked sentence->",new_mask_sent)
 #I went to see a [Mask] movie in the theater
 
 augmented_text_list = unmasker(new_mask_sent)
 augmented_text = augmented_text_list[0]['sequence']
 print("Augmented text->",augmented_text)
 #I went to see a new movie in the theater

 from transformers import pipeline
 import random
 
 unmasker = pipeline('fill-mask', model='bert-base-cased')
 
 input_text = "I went to see a movie in the theater"
 
 orig_text_list = input_text.split()
 len_input = len(orig_text_list)
 #Random index where we want to replace the word 
 rand_idx = random.randint(1,len_input-1)
 
 new_text_list = orig_text_list.copy()
 new_text_list[rand_idx] = '[MASK]'
 new_mask_sent = ' '.join(new_text_list)
 print("Masked sentence->",new_mask_sent)
 #I went to [MASK] a movie in the theater
 
 augmented_text_list = unmasker(new_mask_sent)
 #To ensure new word and old word are not name
 for res in augmented_text_list:
   if res['token_str'] != orig_word:
     augmented_text = res['sequence']
     break
 print("Augmented text->",augmented_text)
 #I went to watch a movie in the theater

 from transformers import pipeline
 generator = pipeline('text-generation', model='gpt2')
 
 input_text = "I went to see a movie in the theater"
 input_length = len(input_text.split())
 num_new_words = 5
 output_length = input_length + num_new_words
 gpt_output = generator(input_text, max_length=output_length, num_return_sequences=5)
 augmented_text = gpt_output[0]['generated_text']
 print("Augmented text->",augmented_text)
 #I went to see a movie in the theater, and the director was