def construct_qa_transformer(options: KaggleEvaluationOptions) -> Reranker:
# We load a sequence classification model first -- again, as a workaround. Refactor.
try:
model = AutoModelForSequenceClassification.from_pretrained(options.model_name)
except OSError:
model = AutoModelForSequenceClassification.from_pretrained(options.model_name, from_tf=True)
fixed_model = BertForQuestionAnswering(model.config)
fixed_model.qa_outputs = model.classifier
fixed_model.bert = model.bert
device = torch.device(options.device)
model = fixed_model.to(device).eval()
tokenizer = AutoTokenizer.from_pretrained(options.tokenizer_name, do_lower_case=options.do_lower_case)
return QuestionAnsweringTransformerReranker(model, tokenizer)
def demo4():
from transformers import BertTokenizer, BertForQuestionAnswering
import torch
MODEL_PATH = r"D:\transformr_files\bert-base-uncased/"
# 实例化tokenizer
tokenizer = BertTokenizer.from_pretrained(
r"D:\transformr_files\bert-base-uncased\bert-base-uncased-vocab.txt")
# 导入bert的model_config
model_config = transformers.BertConfig.from_pretrained(MODEL_PATH)
# 首先新建bert_model
bert_model = transformers.BertModel.from_pretrained(MODEL_PATH,
config=model_config)
# 最终有两个输出，初始位置和结束位置（下面有解释）
model_config.num_labels = 2
# 同样根据bert的model_config新建BertForQuestionAnswering
model = BertForQuestionAnswering(model_config)
model.bert = bert_model
# 设定模式
model.eval()
question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
# 获取input_ids编码
input_ids = tokenizer.encode(question, text)
# 手动进行token_type_ids编码，可用encode_plus代替
# input_ids = tokenizer.encode_plus("i like you", "but not him")
token_type_ids = [
0 if i <= input_ids.index(102) else 1 for i in range(len(input_ids))
]
# 得到评分,
start_scores, end_scores = model(torch.tensor([input_ids]),
token_type_ids=torch.tensor(
[token_type_ids]))
# 进行逆编码，得到原始的token
all_tokens = tokenizer.convert_ids_to_tokens(input_ids)
# ['[CLS]', 'who', 'was', 'jim', 'henson', '?', '[SEP]', 'jim', 'henson', 'was', 'a', 'nice', 'puppet', '[SEP]']
# 对输出的答案进行解码的过程
answer = ' '.join(
all_tokens[torch.argmax(start_scores):torch.argmax(end_scores) + 1])
# assert answer == "a nice puppet"
# 这里因为没有经过微调，所以效果不是很好，输出结果不佳。
print(answer)
