Hugging Face
This documentation describes the integration of MindsDB with Hugging Face, a company that develops computer tools for building applications using machine learning. The integration allows for the deployment of Hugging Face models 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 within MindsDB, install the required dependencies following this instruction.
Setup
Create an AI engine from the Hugging Face handler.
CREATE ML_ENGINE huggingface_engine
FROM huggingface
USING huggingface_api_api_key = 'hf_xxx';
Create a model using huggingface_engine
as an engine.
CREATE MODEL huggingface_model
PREDICT target_column
USING
engine = 'huggingface_engine', -- engine name as created via CREATE ML_ENGINE
model_name = 'hf_hub_model_name', -- choose one of PyTorch models from the Hugging Face Hub
task = 'task_name', -- choose one of 'text-classification', 'text-generation', 'zero-shot-classification', 'translation', 'summarization', 'text2text-generation', 'fill-mask'
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)
Usage
The following usage examples utilize huggingface_engine
to create a model with the CREATE MODEL
statement.
Create a model to classify input text as spam or ham.
CREATE MODEL spam_classifier
PREDICT spam_or_ham
USING
engine = 'huggingface_engine',
model_name = 'mrm8488/bert-tiny-finetuned-sms-spam-detection',
task = 'text-classification',
input_column = 'text',
labels = ['ham', 'spam'];
Query the model to get predictions.
SELECT text, spam_or_ham
FROM spam_classifier
WHERE text = 'Subscribe to this channel asap';
Here is the output:
+--------------------------------+-------------+
| text | spam_or_ham |
+--------------------------------+-------------+
| Subscribe to this channel asap | spam |
+--------------------------------+-------------+
Next Steps
Follow this link to see more use case examples.
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