利用BERT开发答疑系统

Question

我目前正在开发一个问答系统(印度尼西亚语的，)，我的论文使用了BERT。数据集和提出的问题都是用印度尼西亚文写的。

问题是，我还不清楚如何逐步开发伯特的问答系统。

根据我在阅读了许多研究期刊和论文后得出的结论，这个过程可能是这样的：

model

• Testing

1. 准备主数据集
2. 负载预处理数据
3. 使用列车前数据(以便生成“精调”模型)
4. 集群(向system)
5. Evaluation

提出问题)来训练主数据集。

我想问的是：

• ，这些步骤正确吗？或者如果step(s)?
• Also，提供的默认预处理数据是英文的，而我的主要数据集是印度尼西亚语，那么如何创建自己的印度尼西亚培训前数据呢？
• 它真的需要在BERT

中执行数据/模型聚类吗？

我感谢任何有帮助的回答。先谢谢你。

Answer 1

我想看看Huggingface的问题&回答例子。那至少是个好的开始。

from transformers import AutoTokenizer, AutoModelForQuestionAnswering
import torch

tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
model = AutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")

text = r"""
 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose
architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural
Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between
TensorFlow 2.0 and PyTorch.
"""

questions = [
    "How many pretrained models are available in Transformers?",
    "What does Transformers provide?",
    "Transformers provides interoperability between which frameworks?",
]

for question in questions:
    inputs = tokenizer.encode_plus(question, text, add_special_tokens=True, return_tensors="pt")
    input_ids = inputs["input_ids"].tolist()[0]

    text_tokens = tokenizer.convert_ids_to_tokens(input_ids)
    answer_start_scores, answer_end_scores = model(**inputs)

    answer_start = torch.argmax(
        answer_start_scores
    )  # Get the most likely beginning of answer with the argmax of the score
    answer_end = torch.argmax(answer_end_scores) + 1  # Get the most likely end of answer with the argmax of the score

    answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))

    print(f"Question: {question}")
    print(f"Answer: {answer}\n")