印象中觉得transformers是一个庞然大物，但实际接触后，却是极其友好，感谢huggingface大神。
安装
我的版本号：python 3.6.9；pytorch 1.2.0；CUDA 10.0。
pip install transformers
pip之前确保安装pytorch1.1.0+。
测试
验证代码与结果
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I hate you'))"
在命令行输入如上命令后，transformers会自动下载依赖模型。输出以下结果，安装成果。
[{'label': 'NEGATIVE', 'score': 0.9991129040718079}]
transformer pipeline下载模型文件说明
transformers自动下载模型的保存位置：C:\Users\username\.cache\torch\，在模型下载以后，可以保存到其他位置。各文件的说明如下：
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json文件包含对应文件的‘url’和‘etag’标签。
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‘a41…’为配置文件：distilbert-base-uncased-config。
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‘26b…’为词典文件：bert-base-uncased-vocab。
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‘437…’为finetuned-sst-2的配置文件：distilbert-base-uncased-finetuned-sst-2-english-config，注意其与‘a41…’文件的不同。
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‘57d…’为Modelcard文件：distilbert-base-uncased-finetuned-sst-2-english-modelcard。
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‘dd7…’为模型参数文件：distilbert-base-uncased-finetuned-sst-2-english-pytorch_model.bin。
pipeline()简介
可以看到，通过执行pipeline('sentiment-analysis')('I hate you')，transformers自动下载GLUE中sst2数据集的distilbert-base-uncased-finetuned-sst-2模型，对’I hate you’进行情感分析。
Pipeline是一个简捷的NLP任务接口，执行Input -> Tokenization -> Model Inference -> Post-Processing (Task dependent) -> Output一系列操作。目前支持Named Entity Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction and Question Answering等任务。
以Question Answering为例：
from transformers import pipeline
nlp = pipeline("question-answering")
context = "Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune a model on a SQuAD task, you may leverage the `run_squad.py`."
print(nlp(question="What is extractive question answering?", context=context))
print(nlp(question="What is a good example of a question answering dataset?", context=context))
对QA任务，transformers使用SQuAD数据集的distilbert-base-cased-distilled-squad模型，模型文件同上文介绍。
移动模型到自定义文件夹
以QA为例：
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首先我们建立一个文件夹，命名为distilbert-base-cased-distilled-squad，然后将词典文件、模型配置文件、模型参数文件三个文件放入这个文件夹，并且将文件重命名为config.json、vocab.txt、pytorch_model.bin即可。
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在代码中定义模型目录，
DISTILLED = './distilbert-base-cased-distilled-squad'，完整代码如下。from transformers import AutoTokenizer, AutoModelForQuestionAnswering import torch DISTILLED = './distilbert-base-cased-distilled-squad' tokenizer = AutoTokenizer.from_pretrained(DISTILLED) model = AutoModelForQuestionAnswering.from_pretrained(DISTILLED) text = """ 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")
