【深度学习 | Transformer】Transformers 教程：pipeline一键预测

た入场券 2023-09-27 19:12 128阅读 0赞

#### 文章目录 ####

*  一、前言
 *  二、Computer vision
 *   *  2.1 Image classification
     *  2.2 Object detection
     *  2.3 Image segmentation
     *  2.4 Depth estimation
 *  三、NLP
 *   *  3.1 Text classification
     *  3.2 Token classification
     *  3.3 Question answering
     *  3.4 Summarization
     *  3.5 Translation
     *  3.6 Language modeling
     *   *  3.6.1 预测序列中的下一个单词
         *  3.6.2 预测一个序列中的一个被屏蔽的token

## 一、前言 ##

Transformers 是用于自然语言处理 (NLP)、计算机视觉以及音频和语音处理任务的预训练最先进模型库。该库不仅包含 Transformer 模型，还包含非 Transformer 模型，例如用于计算机视觉任务的现代卷积网络。

`pipeline()`可以加载多个模型让进行推理变得简单，即使没有使用特定模态的经验或不熟悉模型背后的底层代码，仍然可以使用它们通过`pipeline()`进行推理。

## 二、Computer vision ##

### 2.1 Image classification ###

从一组预定义的类中标记图像。

from transformers import pipeline
    classifier = pipeline(task="image-classification")
    preds = classifier(
        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
    )
    
    preds = [{
        "score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]

输出结果为：

{
        'score': 0.4335, 'label': 'lynx, catamount'}
    {
        'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}
    {
        'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}
    {
        'score': 0.0239, 'label': 'Egyptian cat'}
    {
        'score': 0.0229, 'label': 'tiger cat'}

### 2.2 Object detection ###

目标检测识别图像对象以及对象在图像中的位置。

from transformers import pipeline
    detector = pipeline(task="object-detection")
    preds = detector(
        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
    )
    
    preds = [{
        "score": round(pred["score"], 4), "label": pred["label"], "box": pred["box"]} for pred in preds]

输出结果为：

[{
        'score': 0.9865,
      'label': 'cat',
      'box': {
        'xmin': 178, 'ymin': 154, 'xmax': 882, 'ymax': 598}}]

### 2.3 Image segmentation ###

图像分割是一项像素级任务，它将图像中的每个像素分配给一个类别。

from transformers import pipeline
    segmenter = pipeline(task="image-segmentation")
    preds = segmenter(
        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
    )
    
    preds = [{
        "score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]

输出结果为：

{
        'score': 0.9879, 'label': 'LABEL_184'}
    {
        'score': 0.9973, 'label': 'snow'}
    {
        'score': 0.9972, 'label': 'cat'}

### 2.4 Depth estimation ###

预测图像中每个像素与相机的距离。

from transformers import pipeline
    depth_estimator = pipeline(task="depth-estimation")
    preds = depth_estimator(
        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
    )

## 三、NLP ##

### 3.1 Text classification ###

从一组预定义的类中标记一系列文本。

from transformers import pipeline
    classifier = pipeline(task="sentiment-analysis")
    preds = classifier("Hugging Face is the best thing since sliced bread!")

### 3.2 Token classification ###

为每个token分配定义类别中的标签。

from transformers import pipeline
    classifier = pipeline(task="ner")
    preds = classifier("Hugging Face is a French company based in New York City.")

### 3.3 Question answering ###

返回问题的答案，有时有上下文（开放域），有时没有上下文（封闭域）。

from transformers import pipeline
    question_answerer = pipeline(task="question-answering")
    preds = question_answerer(
        question="What is the name of the repository?",
        context="The name of the repository is huggingface/transformers",
    )

### 3.4 Summarization ###

从较长的文本创建较短的版本，同时试图保留原始文档的大部分含义。

from transformers import pipeline
    summarizer = pipeline(task="summarization")
    summarizer(
        "In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention. For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles."
    )

### 3.5 Translation ###

将一种语言的转换为另一种语言。

from transformers import pipeline
    text = "translate English to French: Hugging Face is a community-based open-source platform for machine learning."
    translator = pipeline(task="translation", model="t5-small")

### 3.6 Language modeling ###

#### 3.6.1 预测序列中的下一个单词 ####

from transformers import pipeline
    prompt = "Hugging Face is a community-based open-source platform for machine learning."
    generator = pipeline(task="text-generation")

#### 3.6.2 预测一个序列中的一个被屏蔽的token ####

text = "Hugging Face is a community-based open-source <mask> for machine learning."
    fill_mask = pipeline(task="fill-mask")