从给定的问题和上下文中提取提取式问答。在 SQuAD（斯坦福问答数据集）上微调的 DistilBERT 模型。

    let qa_model = QuestionAnsweringModel::new(Default::default())?;
                                                        
    let question = String::from("Where does Amy live ?");
    let context = String::from("Amy lives in Amsterdam");

    let answers = qa_model.predict(&[QaInput { question, context }], 1, 32);

输出

[Answer { score: 0.9976, start: 13, end: 21, answer: "Amsterdam" }]

支持广泛源语言和目标语言的翻译管道。利用两种主要架构进行翻译任务

• 基于 Marian 的模型，用于特定的源/目标组合
• M2M100 模型允许在 100 种语言之间直接翻译（对于某些选定的语言，计算成本更高，性能更低）

库中提供了以下语言对基于 Marian 的预训练模型，但用户可以导入任何 Pytorch 模型进行预测

• 英语 <-> 法语
• 英语 <-> 西班牙语
• 英语 <-> 葡萄牙语
• 英语 <-> 意大利语
• 英语 <-> 加泰罗尼亚语
• 英语 <-> 德语
• 英语 <-> 俄语
• 英语 <-> 中文
• 英语 <-> 荷兰语
• 英语 <-> 瑞典语
• 英语 <-> 阿拉伯语
• 英语 <-> 希伯来语
• 英语 <-> 印地语
• 法语 <-> 德语

对于不支持预训练 Marian 模型的语言，用户可以依赖支持 100 种语言之间直接翻译（不通过英语翻译）的 M2M100 模型。支持的语言完整列表可在 软件包文档 中找到。

use rust_bert::pipelines::translation::{Language, TranslationModelBuilder};
fn main() -> anyhow::Result<()> {
let model = TranslationModelBuilder::new()
        .with_source_languages(vec![Language::English])
        .with_target_languages(vec![Language::Spanish, Language::French, Language::Italian])
        .create_model()?;
    let input_text = "This is a sentence to be translated";
    let output = model.translate(&[input_text], None, Language::Spanish)?;
    for sentence in output {
        println!("{}", sentence);
    }
    Ok(())
}

输出

Il s'agit d'une phrase à traduire

使用预训练的 BART 模型进行抽象摘要。

    let summarization_model = SummarizationModel::new(Default::default())?;
                                                        
    let input = ["In findings published Tuesday in Cornell University's arXiv by a team of scientists \
from the University of Montreal and a separate report published Wednesday in Nature Astronomy by a team \
from University College London (UCL), the presence of water vapour was confirmed in the atmosphere of K2-18b, \
a planet circling a star in the constellation Leo. This is the first such discovery in a planet in its star's \
habitable zone — not too hot and not too cold for liquid water to exist. The Montreal team, led by Björn Benneke, \
used data from the NASA's Hubble telescope to assess changes in the light coming from K2-18b's star as the planet \
passed between it and Earth. They found that certain wavelengths of light, which are usually absorbed by water, \
weakened when the planet was in the way, indicating not only does K2-18b have an atmosphere, but the atmosphere \
contains water in vapour form. The team from UCL then analyzed the Montreal team's data using their own software \
and confirmed their conclusion. This was not the first time scientists have found signs of water on an exoplanet, \
but previous discoveries were made on planets with high temperatures or other pronounced differences from Earth. \
\"This is the first potentially habitable planet where the temperature is right and where we now know there is water,\" \
said UCL astronomer Angelos Tsiaras. \"It's the best candidate for habitability right now.\" \"It's a good sign\", \
said Ryan Cloutier of the Harvard–Smithsonian Center for Astrophysics, who was not one of either study's authors. \
\"Overall,\" he continued, \"the presence of water in its atmosphere certainly improves the prospect of K2-18b being \
a potentially habitable planet, but further observations will be required to say for sure. \"
K2-18b was first identified in 2015 by the Kepler space telescope. It is about 110 light-years from Earth and larger \
but less dense. Its star, a red dwarf, is cooler than the Sun, but the planet's orbit is much closer, such that a year \
on K2-18b lasts 33 Earth days. According to The Guardian, astronomers were optimistic that NASA's James Webb space \
telescope — scheduled for launch in 2021 — and the European Space Agency's 2028 ARIEL program, could reveal more \
about exoplanets like K2-18b."];

    let output = summarization_model.summarize(&input);

（示例来源：WikiNews）

输出

"Scientists have found water vapour on K2-18b, a planet 110 light-years from Earth. 
This is the first such discovery in a planet in its star's habitable zone. 
The planet is not too hot and not too cold for liquid water to exist."

基于微软的DialoGPT的对话模型。此流程允许生成人类与模型之间单轮或多轮对话。DialoGPT页面说明

人类评估结果表明，DialoGPT生成的响应在单轮对话图灵测试中与人类响应质量相当。（DialoGPT 代码库）

该模型使用一个ConversationManager来跟踪活跃对话并生成对其的响应。

use rust_bert::pipelines::conversation::{ConversationModel, ConversationManager};

let conversation_model = ConversationModel::new(Default::default());
let mut conversation_manager = ConversationManager::new();

let conversation_id = conversation_manager.create("Going to the movies tonight - any suggestions?");
let output = conversation_model.generate_responses(&mut conversation_manager);

示例输出

"The Big Lebowski."

基于提示生成语言。提供GPT2和GPT作为基础模型。包括诸如束搜索、top-k和核采样、温度设置和重复惩罚等技术。支持从多个提示中批量生成句子。如果存在模型填充标记，则序列将使用模型填充标记左填充，否则使用未知标记。这可能会影响结果，建议提交长度相似的提示以获得最佳结果

    let model = GPT2Generator::new(Default::default())?;
                                                        
    let input_context_1 = "The dog";
    let input_context_2 = "The cat was";

    let generate_options = GenerateOptions {
        max_length: 30,
        ..Default::default()
    };

    let output = model.generate(Some(&[input_context_1, input_context_2]), generate_options);

示例输出

[
    "The dog's owners, however, did not want to be named. According to the lawsuit, the animal's owner, a 29-year"
    "The dog has always been part of the family. \"He was always going to be my dog and he was always looking out for me"
    "The dog has been able to stay in the home for more than three months now. \"It's a very good dog. She's"
    "The cat was discovered earlier this month in the home of a relative of the deceased. The cat\'s owner, who wished to remain anonymous,"
    "The cat was pulled from the street by two-year-old Jazmine.\"I didn't know what to do,\" she said"
    "The cat was attacked by two stray dogs and was taken to a hospital. Two other cats were also injured in the attack and are being treated."
]

使用针对自然语言推理微调的模型对输入句子进行零样本分类。

    let sequence_classification_model = ZeroShotClassificationModel::new(Default::default())?;

    let input_sentence = "Who are you voting for in 2020?";
    let input_sequence_2 = "The prime minister has announced a stimulus package which was widely criticized by the opposition.";
    let candidate_labels = &["politics", "public health", "economics", "sports"];

    let output = sequence_classification_model.predict_multilabel(
        &[input_sentence, input_sequence_2],
        candidate_labels,
        None,
        128,
    );

输出

[
  [ Label { "politics", score: 0.972 }, Label { "public health", score: 0.032 }, Label {"economics", score: 0.006 }, Label {"sports", score: 0.004 } ],
  [ Label { "politics", score: 0.975 }, Label { "public health", score: 0.0818 }, Label {"economics", score: 0.852 }, Label {"sports", score: 0.001 } ],
]

预测句子的二元情感。DistilBERT模型在SST-2上微调。

    let sentiment_classifier = SentimentModel::new(Default::default())?;
                                                        
    let input = [
        "Probably my all-time favorite movie, a story of selflessness, sacrifice and dedication to a noble cause, but it's not preachy or boring.",
        "This film tried to be too many things all at once: stinging political satire, Hollywood blockbuster, sappy romantic comedy, family values promo...",
        "If you like original gut wrenching laughter you will like this movie. If you are young or old then you will love this movie, hell even my mom liked it.",
    ];

    let output = sentiment_classifier.predict(&input);

（示例由IMDb提供）

输出

[
    Sentiment { polarity: Positive, score: 0.9981985493795946 },
    Sentiment { polarity: Negative, score: 0.9927982091903687 },
    Sentiment { polarity: Positive, score: 0.9997248985164333 }
]

从文本中提取实体（人名、地点、组织、其他）。BERT带大小模型，在CoNNL03上微调，由巴伐利亚州立图书馆的MDZ 数字图书馆团队提供。目前模型可供英语、德语、西班牙语和荷兰语使用。

    let ner_model = NERModel::new(default::default())?;

    let input = [
        "My name is Amy. I live in Paris.",
        "Paris is a city in France."
    ];
    
    let output = ner_model.predict(&input);

输出

[
  [
    Entity { word: "Amy", score: 0.9986, label: "I-PER" }
    Entity { word: "Paris", score: 0.9985, label: "I-LOC" }
  ],
  [
    Entity { word: "Paris", score: 0.9988, label: "I-LOC" }
    Entity { word: "France", score: 0.9993, label: "I-LOC" }
  ]
]

从输入文档中提取关键词和短语提取

fn main() -> anyhow::Result<()> {
    let keyword_extraction_model = KeywordExtractionModel::new(Default::default())?;
    
    let input = "Rust is a multi-paradigm, general-purpose programming language. \
       Rust emphasizes performance, type safety, and concurrency. Rust enforces memory safety—that is, \
       that all references point to valid memory—without requiring the use of a garbage collector or \
       reference counting present in other memory-safe languages. To simultaneously enforce \
       memory safety and prevent concurrent data races, Rust's borrow checker tracks the object lifetime \
       and variable scope of all references in a program during compilation. Rust is popular for \
       systems programming but also offers high-level features including functional programming constructs.";

    let output = keyword_extraction_model.predict(&[input])?;
}

输出

"rust" - 0.50910604
"programming" - 0.35731024
"concurrency" - 0.33825397
"concurrent" - 0.31229728
"program" - 0.29115444

从文本中提取词性标签（名词、动词、形容词等）。

    let pos_model = POSModel::new(default::default())?;

    let input = ["My name is Bob"];
    
    let output = pos_model.predict(&input);

输出

[
    Entity { word: "My", score: 0.1560, label: "PRP" }
    Entity { word: "name", score: 0.6565, label: "NN" }
    Entity { word: "is", score: 0.3697, label: "VBZ" }
    Entity { word: "Bob", score: 0.7460, label: "NNP" }
]

生成句子嵌入（向量表示）。这些可以用于包括密集信息检索在内的应用程序。

    let model = SentenceEmbeddingsBuilder::remote(
            SentenceEmbeddingsModelType::AllMiniLmL12V2
        ).create_model()?;

    let sentences = [
        "this is an example sentence", 
        "each sentence is converted"
    ];
    
    let output = model.encode(&sentences)?;

输出

[
    [-0.000202666, 0.08148022, 0.03136178, 0.002920636 ...],
    [0.064757116, 0.048519745, -0.01786038, -0.0479775 ...]
]

预测输入句子中的遮罩词。

    let model = MaskedLanguageModel::new(Default::default())?;

    let sentences = [
        "Hello I am a <mask> student",
        "Paris is the <mask> of France. It is <mask> in Europe.",
    ];
    
    let output = model.predict(&sentences);

输出

[
    [MaskedToken { text: "college", id: 2267, score: 8.091}],
    [
        MaskedToken { text: "capital", id: 3007, score: 16.7249}, 
        MaskedToken { text: "located", id: 2284, score: 9.0452}
    ]
]