Hugging Face Inference API - Mindsdb

This documentation describes the integration of MindsDB with Hugging Face Inference API. The integration allows for the deployment of Hugging Face models through Inference API within MindsDB, providing the models with access to data from various data sources.

Prerequisites

Before proceeding, ensure the following prerequisites are met:

Install MindsDB locally via Docker or Docker Desktop.
To use Hugging Face Inference API within MindsDB, install the required dependencies following this instruction.
Obtain the API key for Hugging Face Inference API required to deploy and use Hugging Face models through Inference API within MindsDB. Generate tokens in the Settings -> Access Tokens tab of the Hugging Face account.

Setup

Create an AI engine from the Hugging Face Inference API handler.

sql

CREATE ML_ENGINE huggingface_api_engine
FROM huggingface_api
USING
      huggingface_api_api_key = 'api-key-value';

Create a model using huggingface_api_engine as an engine.

sql

CREATE MODEL huggingface_api_model
PREDICT target_column
USING
      engine = 'huggingface_api_engine',     -- engine name as created via CREATE ML_ENGINE
      task = 'task_name',                -- choose one of 'text-classification', 'text-generation', 'question-answering', 'sentence-similarity', 'zero-shot-classification', 'summarization', 'fill-mask', 'image-classification', 'object-detection', 'automatic-speech-recognition', 'audio-classification'
      input_column = 'column_name',      -- column that stores input/question to the model
      labels = ['label 1', 'label 2'];   -- labels used to classify data (used for classification tasks)

The following parameters are supported in the USING clause of the CREATE MODEL statement:

Parameter	Required	Description
`engine`	Yes	It is the name of the ML engine created with the `CREATE ML_ENGINE` statement.
`task`	Only if `model_name` is not provided	It describes a task to be performed.
`model_name`	Only if `task` is not provided	It specifies a model to be used.
`input_column`	Yes	It is the name of the column that stores input to the model.
`endpoint`	No	It defines the endpoint to use for API calls. If not specified, the hosted Inference API from Hugging Face will be used.
`options`	No	It is a JSON object containing additional options to pass to the API call. More information about the available options for each task can be found here.
`parameters`	No	It is a JSON object containing additional parameters to pass to the API call. More information about the available parameters for each task can be found here.
`context_column`	Only if `task` is `question-answering`	It is used for the `question-answering` task to provide context to the question.
`input_column2`	Only if `task` is `sentence-similarity`	It is used for the `sentence-similarity` task to provide the second input sentence for comparison.
`candidate_labels`	Only if `task` is `zero-shot-classification`	It is used for the `zero-shot-classification` task to classify input data according to provided labels.

Usage

The following usage examples utilize huggingface_api_engine to create a model with the CREATE MODEL statement.

Create a model to classify input text as spam or ham.

sql

CREATE MODEL spam_classifier
PREDICT is_spam
USING
      engine = 'huggingface_api_engine',
      task = 'text-classification',
      column = 'text';

Query the model to get predictions.

sql

SELECT text, is_spam
FROM spam_classifier
WHERE text = 'Subscribe to this channel asap';

Here is the output:

sql

+--------------------------------+---------+
| text                           | is_spam |
+--------------------------------+---------+
| Subscribe to this channel asap | spam    |
+--------------------------------+---------+

<Info>

Find more quick examples below:

<AccordionGroup> <Accordion title="Text Classification"> ```sql CREATE MODEL mindsdb.hf_text_classifier PREDICT sentiment USING task = 'text-classification', engine = 'hf_api_engine', input_column = 'text'; ``` </Accordion> <Accordion title="Fill Mask"> ```sql CREATE MODEL mindsdb.hf_fill_mask PREDICT sequence USING task = 'fill-mask', engine = 'hf_api_engine', input_column = 'text'; ``` </Accordion> <Accordion title="Summarization"> ```sql CREATE MODEL mindsdb.hf_summarizer PREDICT summary USING task = 'summarization', engine = 'hf_api_engine', input_column = 'text'; ``` </Accordion> <Accordion title="Text Generation"> ```sql CREATE MODEL mindsdb.hf_text_generator PREDICT generated_text USING task = 'text-generation', engine = 'hf_api_engine', input_column = 'text'; ``` </Accordion> <Accordion title="Question Answering"> ```sql CREATE MODEL mindsdb.hf_question_answerer PREDICT answer USING task = 'question-answering', engine = 'hf_api_engine', input_column = 'question', context_column = 'context'; ``` </Accordion> <Accordion title="Sentences Similarity"> ```sql CREATE MODEL mindsdb.hf_sentence_similarity PREDICT similarity USING task = 'sentence-similarity', engine = 'hf_api_engine', input_column = 'sentence1', input_column2 = 'sentence2'; ``` </Accordion> <Accordion title="Zero Shot Classification"> ```sql CREATE MODEL mindsdb.hf_zero_shot_classifier PREDICT label USING task = 'zero-shot-classification', engine = 'hf_api_engine', input_column = 'text', candidate_labels = ['label1', 'label2', 'label3']; ``` </Accordion> <Accordion title="Image Classification"> ```sql CREATE MODEL mindsdb.hf_image_classifier PREDICT label USING task = 'image-classification', engine = 'hf_api_engine', input_column = 'image_url'; ``` </Accordion> <Accordion title="Object Detection"> ```sql CREATE MODEL mindsdb.hf_object_detector PREDICT objects USING task = 'object-detection', engine = 'hf_api_engine', input_column = 'image_url'; ``` </Accordion> <Accordion title="Automatic Speech Recognition"> ```sql CREATE MODEL mindsdb.hf_speech_recognizer PREDICT transcription USING task = 'automatic-speech-recognition', engine = 'hf_api_engine', input_column = 'audio_url'; ``` </Accordion> <Accordion title="Audio Classification"> ```sql CREATE MODEL mindsdb.hf_audio_classifier PREDICT label USING task = 'audio-classification', engine = 'hf_api_engine', input_column = 'audio_url'; ``` </Accordion> </AccordionGroup> </Info> <Tip>

Next Steps

Follow this link to see more use case examples. </Tip>