Module rust_bert::pipelines

Ready-to-use NLP pipelines and models

Based on Huggingface's pipelines, ready to use end-to-end NLP pipelines are available as part of this crate. The following capabilities are currently available:

1. Question Answering

Extractive question answering from a given question and context. DistilBERT model finetuned on SQuAD (Stanford Question Answering Dataset)

use rust_bert::pipelines::question_answering::{QuestionAnsweringModel, QaInput};
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(&vec!(QaInput { question, context }), 1, 32);

Output:

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

2. Summarization

Abstractive summarization of texts based on the BART encoder-decoder architecture Include techniques such as beam search, top-k and nucleus sampling, temperature setting and repetition penalty.

use rust_bert::pipelines::summarization::SummarizationModel;

let mut 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 = model.summarize(&input);

(example from: WikiNews)

Example output:

"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."

3. Natural Language Generation

Generate language based on a prompt. GPT2 and GPT available as base models. Include techniques such as beam search, top-k and nucleus sampling, temperature setting and repetition penalty. Supports batch generation of sentences from several prompts. Sequences will be left-padded with the model's padding token if present, the unknown token otherwise. This may impact the results and it is recommended to submit prompts of similar length for best results. Additional information on the input parameters for generation is provided in this module's documentation.

use rust_bert::pipelines::generation::GPT2Generator;
let mut model = GPT2Generator::new(Default::default())?;
let input_context_1 = "The dog";
let input_context_2 = "The cat was";
let output = model.generate(Some(vec!(input_context_1, input_context_2)), None);

Example output:

[
    "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."
]

4. Sentiment analysis

Predicts the binary sentiment for a sentence. DistilBERT model finetuned on SST-2.

use rust_bert::pipelines::sentiment::SentimentModel;
let sentiment_model = 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_model.predict(&input);

(Example courtesy of IMDb)

Output:

[
   Sentiment { polarity: Positive, score: 0.998 },
   Sentiment { polarity: Negative, score: 0.992 },
   Sentiment { polarity: Positive, score: 0.999 }
]

5. Named Entity Recognition

Extracts entities (Person, Location, Organization, Miscellaneous) from text. BERT cased large model finetuned on CoNNL03, contributed by the MDZ Digital Library team at the Bavarian State Library

use rust_bert::pipelines::ner::NERModel;
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);

Output:

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

Modules

generation	Natural Language Generation pipeline
ner	Named Entity Recognition pipeline
question_answering	Question Answering pipeline
sentiment	Sentiment Analysis pipeline
summarization	Summarization pipeline