Hugging Face

The Hugging Face component is an AI component that allows users to connect the AI models served on the Hugging Face Platform. It can carry out the following tasks:

• Text Generation
• Fill Mask
• Summarization
• Text Classification
• Token Classification
• Translation
• Zero Shot Classification
• Question Answering
• Table Question Answering
• Sentence Similarity
• Conversational
• Image Classification
• Image Segmentation
• Object Detection
• Image to Text
• Speech Recognition
• Audio Classification

#Release Stage

Alpha

#Configuration

The component definition and tasks are defined in the definition.json and tasks.json files respectively.

#Setup

In order to communicate with Hugging Face, the following connection details need to be provided. You may specify them directly in a pipeline recipe as key-value pairs within the component's setup block, or you can create a Connection from the Integration Settings page and reference the whole setup as setup: ${connection.<my-connection-id>}.

#Supported Tasks

#Text Generation

Generating text is the task of producing new text. These models can, for example, fill in incomplete text or paraphrase.

#Fill Mask

Masked language modeling is the task of masking some of the words in a sentence and predicting which words should replace those masks. These models are useful when we want to get a statistical understanding of the language in which the model is trained in.

#Summarization

Summarization is the task of producing a shorter version of a document while preserving its important information. Some models can extract text from the original input, while other models can generate entirely new text.

#Text Classification

Text Classification is the task of assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness.

#Token Classification

Token classification is a natural language understanding task in which a label is assigned to some tokens in a text. Some popular token classification subtasks are Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging. NER models could be trained to identify specific entities in a text, such as dates, individuals and places; and PoS tagging would identify, for example, which words in a text are verbs, nouns, and punctuation marks.

#Translation

Translation is the task of converting text from one language to another.

#Zero Shot Classification

Zero-shot text classification is a task in natural language processing where a model is trained on a set of labeled examples but is then able to classify new examples from previously unseen classes.

#Question Answering

Question Answering models can retrieve the answer to a question from a given text, which is useful for searching for an answer in a document. Some question answering models can generate answers without context!

#Table Question Answering

Table Question Answering (Table QA) is the answering a question about an information on a given table.

#Sentence Similarity

Sentence Similarity is the task of determining how similar two texts are. Sentence similarity models convert input texts into vectors (embeddings) that capture semantic information and calculate how close (similar) they are between them. This task is particularly useful for information retrieval and clustering/grouping.

#Conversational

Conversational response modelling is the task of generating conversational text that is relevant, coherent and knowledgable given a prompt. These models have applications in chatbots, and as a part of voice assistants

#Image Classification

Image classification is the task of assigning a label or class to an entire image. Images are expected to have only one class for each image. Image classification models take an image as input and return a prediction about which class the image belongs to.

#Image Segmentation

Image Segmentation divides an image into segments where each pixel in the image is mapped to an object. This task has multiple variants such as instance segmentation, panoptic segmentation and semantic segmentation.

#Object Detection

Object Detection models allow users to identify objects of certain defined classes. Object detection models receive an image as input and output the images with bounding boxes and labels on detected objects.

#Image to Text

Image to text models output a text from a given image. Image captioning or optical character recognition can be considered as the most common applications of image to text.

#Speech Recognition

Automatic Speech Recognition (ASR), also known as Speech to Text (STT), is the task of transcribing a given audio to text. It has many applications, such as voice user interfaces.

#Audio Classification

Audio classification is the task of assigning a label or class to a given audio. It can be used for recognizing which command a user is giving or the emotion of a statement, as well as identifying a speaker.