#
Widget Examples

Note that each widget example can also optionally describe the corresponding model output, directly in the `output` property. See the spec for more details.

##
Natural Language Processing

###
Fill-Mask

    widget:
    - text: "Paris is the <mask> of France."
      example_title: "Capital"
    - text: "The goal of life is <mask>."
      example_title: "Philosophy"

###
Question Answering

    widget:
    - text: "What's my name?"
      context: "My name is Clara and I live in Berkeley."
      example_title: "Name"
    - text: "Where do I live?"
      context: "My name is Sarah and I live in London"
      example_title: "Location"

###
Summarization

    widget:
    - text: "The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct."
      example_title: "Eiffel Tower"
    - text: "Laika, a dog that was the first living creature to be launched into Earth orbit, on board the Soviet artificial satellite Sputnik 2, on November 3, 1957. It was always understood that Laika would not survive the mission, but her actual fate was misrepresented for decades. Laika was a small (13 pounds [6 kg]), even-tempered, mixed-breed dog about two years of age. She was one of a number of stray dogs that were taken into the Soviet spaceflight program after being rescued from the streets. Only female dogs were used because they were considered to be anatomically better suited than males for close confinement."
      example_title: "First in Space"

###
Table Question Answering

    widget:
    - text: "How many stars does the transformers repository have?"
      table:
        Repository:
          - "Transformers"
          - "Datasets"
          - "Tokenizers"
        Stars:
          - 36542
          - 4512
          - 3934
        Contributors:
          - 651
          - 77
          - 34
        Programming language:
          - "Python"
          - "Python"
          - "Rust, Python and NodeJS"
      example_title: "Github stars"

###
Text Classification

    widget:
    - text: "I love football so much"
      example_title: "Positive"
    - text: "I don't really like this type of food"
      example_title: "Negative"

###
Text Generation

    widget:
    - text: "My name is Julien and I like to"
      example_title: "Julien"
    - text: "My name is Merve and my favorite"
      example_title: "Merve"

###
Text2Text Generation

    widget:
    - text: "My name is Julien and I like to"
      example_title: "Julien"
    - text: "My name is Merve and my favorite"
      example_title: "Merve"

###
Token Classification

    widget:
    - text: "My name is Sylvain and I live in Paris"
      example_title: "Parisian"
    - text: "My name is Sarah and I live in London"
      example_title: "Londoner"

###
Translation

    widget:
    - text: "My name is Sylvain and I live in Paris"
      example_title: "Parisian"
    - text: "My name is Sarah and I live in London"
      example_title: "Londoner"

###
Zero-Shot Classification

    widget:
    - text: "I have a problem with my car that needs to be resolved asap!!"
      candidate_labels: "urgent, not urgent, phone, tablet, computer"
      multi_class: true
      example_title: "Car problem"
    - text: "Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app."
      candidate_labels: "mobile, website, billing, account access"
      multi_class: false
      example_title: "Phone issue"

###
Sentence Similarity

    widget:
    - source_sentence: "That is a happy person"
      sentences:
        - "That is a happy dog"
        - "That is a very happy person"
        - "Today is a sunny day"
      example_title: "Happy"

###
Conversational

    widget:
    - text: "Hey my name is Julien! How are you?"
      example_title: "Julien"
    - text: "Hey my name is Clara! How are you?"
      example_title: "Clara"

###
Feature Extraction

    widget:
    - text: "My name is Sylvain and I live in Paris"
      example_title: "Parisian"
    - text: "My name is Sarah and I live in London"
      example_title: "Londoner"

##
Audio

###
Text-to-Speech

    widget:
    - text: "My name is Sylvain and I live in Paris"
      example_title: "Parisian"
    - text: "My name is Sarah and I live in London"
      example_title: "Londoner"

###
Automatic Speech Recognition

    widget:
    - src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
      example_title: Librispeech sample 1
    - src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
      example_title: Librispeech sample 2

###
Audio-to-Audio

    widget:
    - src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
      example_title: Librispeech sample 1
    - src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
      example_title: Librispeech sample 2

###
Audio Classification

    widget:
    - src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
      example_title: Librispeech sample 1
    - src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
      example_title: Librispeech sample 2

###
Voice Activity Detection

    widget:
    - src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
      example_title: Librispeech sample 1
    - src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
      example_title: Librispeech sample 2

##
Computer Vision

###
Image Classification

    widget:
    - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
      example_title: Tiger
    - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
      example_title: Teapot

###
Object Detection

    widget:
    - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
      example_title: Football Match
    - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg
      example_title: Airport

###
Image Segmentation

    widget:
    - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
      example_title: Football Match
    - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg
      example_title: Airport

###
Image-to-Image

    widget:
    - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/canny-edge.jpg
      prompt: Girl with Pearl Earring # `prompt` field is optional in case the underlying model supports text guidance

###
Text-to-Image

    widget:
    - text: "A cat playing with a ball"
      example_title: "Cat"
    - text: "A dog jumping over a fence"
      example_title: "Dog"

###
Document Question Answering

    widget:
    - text: "What is the invoice number?"
      src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"
    - text: "What is the purchase amount?"
      src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/contract.jpeg"

###
Visual Question Answering

    widget:
    - text: "What animal is it?"
      src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg"
    - text: "Where is it?"
      src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg"

###
Zero-Shot Image Classification

    widget:
    - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
      candidate_labels: playing music, playing sports
      example_title: Cat & Dog

##
Other

###
Structured Data Classification

    widget:
    - structured_data:
        fixed_acidity:
          - 7.4
          - 7.8
          - 10.3
        volatile_acidity:
          - 0.7
          - 0.88
          - 0.32
        citric_acid:
          - 0
          - 0
          - 0.45
        residual_sugar:
          - 1.9
          - 2.6
          - 6.4
        chlorides:
          - 0.076
          - 0.098
          - 0.073
        free_sulfur_dioxide:
          - 11
          - 25
          - 5
        total_sulfur_dioxide:
          - 34
          - 67
          - 13
        density:
          - 0.9978
          - 0.9968
          - 0.9976
        pH:
          - 3.51
          - 3.2
          - 3.23
        sulphates:
          - 0.56
          - 0.68
          - 0.82
        alcohol:
          - 9.4
          - 9.8
          - 12.6
      example_title: "Wine"

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